We propose an automatic classification system of movie genres based on different features from their textual
synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis,
and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTEijnlc
We propose an automatic classification system of movie genres based on different features from their textual synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis, and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
Process the sentiments of NLP with Naive Bayes Rule, Random Forest, Support Vector Machine, and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Detailed documented with the definition of text mining along with challenges, implementing modeling techniques, word cloud and much more.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Extracts objects from the entire collection, without modifying the objects themselves. where the goal is to select whole sentences (without modifying them)
The project is developed as a part of IRE course work @IIIT-Hyderabad.
Team members:
Aishwary Gupta (201302216)
B Prabhakar (201505618)
Sahil Swami (201302071)
Links:
https://github.com/prabhakar9885/Text-Summarization
http://prabhakar9885.github.io/Text-Summarization/
https://www.youtube.com/playlist?list=PLtBx4kn8YjxJUGsszlev52fC1Jn07HkUw
http://www.slideshare.net/prabhakar9885/text-summarization-60954970
https://www.dropbox.com/sh/uaxc2cpyy3pi97z/AADkuZ_24OHVi3PJmEAziLxha?dl=0
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTEijnlc
We propose an automatic classification system of movie genres based on different features from their textual synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis, and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
Process the sentiments of NLP with Naive Bayes Rule, Random Forest, Support Vector Machine, and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Detailed documented with the definition of text mining along with challenges, implementing modeling techniques, word cloud and much more.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Extracts objects from the entire collection, without modifying the objects themselves. where the goal is to select whole sentences (without modifying them)
The project is developed as a part of IRE course work @IIIT-Hyderabad.
Team members:
Aishwary Gupta (201302216)
B Prabhakar (201505618)
Sahil Swami (201302071)
Links:
https://github.com/prabhakar9885/Text-Summarization
http://prabhakar9885.github.io/Text-Summarization/
https://www.youtube.com/playlist?list=PLtBx4kn8YjxJUGsszlev52fC1Jn07HkUw
http://www.slideshare.net/prabhakar9885/text-summarization-60954970
https://www.dropbox.com/sh/uaxc2cpyy3pi97z/AADkuZ_24OHVi3PJmEAziLxha?dl=0
High level introduction to text mining analytics, which covers the building blocks or most commonly used techniques of text mining along with useful additional references/links where required for background/literature and R codes to get you started.
Automatic text summarization is the process of reducing the text content and retaining the
important points of the document. Generally, there are two approaches for automatic text summarization:
Extractive and Abstractive. The process of extractive based text summarization can be divided into two
phases: pre-processing and processing. In this paper, we discuss some of the extractive based text
summarization approaches used by researchers. We also provide the features for extractive based text
summarization process. We also present the available linguistic preprocessing tools with their features,
which are used for automatic text summarization. The tools and parameters useful for evaluating the
generated summary are also discussed in this paper. Moreover, we explain our proposed lexical chain
analysis approach, with sample generated lexical chains, for extractive based automatic text summarization.
We also provide the evaluation results of our system generated summary. The proposed lexical chain
analysis approach can be used to solve different text mining problems like topic classification, sentiment
analysis, and summarization.
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Extraction of Data Using Comparable Entity Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Following are the questions which I tried to answer in this ppt
What is text summarization.
What is automatic text summarization?
How it has evolved over the time?
What are different methods?
How deep learning is used for text summarization?
business application
in first few slides extractive summarization is explained, with pro and cons in next section abstractive on is explained.
In the last section business application of each one is highlighted
Nowadays Sentiment Analysis play an important Role in each field such as Stock market, product reviews, news article, political debates which help us to determining current trend in the market regarding specific product, event, issues. Here we are apply sentiment analysis on microblogging platforms such as twitter, Facebook which is used by different people to express their opinion with respect to different kind of foods in the field of home’schef. This paper explain different methods of text preprocessing and applies them with a naive Bayes classifier in a big data, distributed computing platform with the goal of creating a scalable sentiment analysis solution that can classify text into positive or negative categories. We apply negation handling, word n-grams, stemming, and feature selection to evaluate how different combinations of these pre-processing methods affect performance and efficiency.
Most of the text classification problems are associated with multiple class labels and hence automatic text
classification is one of the most challenging and prominent research area. Text classification is the
problem of categorizing text documents into different classes. In the multi-label classification scenario,
each document is associated may have more than one label. The real challenge in the multi-label
classification is the labelling of large number of text documents with a subset of class categories. The
feature extraction and classification of such text documents require an efficient machine learning algorithm
which performs automatic text classification. This paper describes the multi-label classification of product
review documents using Structured Support Vector Machine.
A Survey of Various Methods for Text SummarizationIJERD Editor
Document summarization means retrieved short and important text from the source document. In this paper, we studied various techniques. Plenty of techniques have been developed on English summarization and other Indian languages but very less efforts have been taken for Hindi language. Here, we discusses various techniques in which so many features are included such as time and memory consumption, efficiency, accuracy, ambiguity, redundancy.
An on-going project on Natural Language Processing (using Python and the NLTK toolkit), which focuses on the extraction of sentiment from a Question and its title on www.stackoverflow.com and determining the polarity.Based on the above findings, it is verified whether the rules and guidelines imposed by the SO community on the users are strictly followed or not.
This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
High level introduction to text mining analytics, which covers the building blocks or most commonly used techniques of text mining along with useful additional references/links where required for background/literature and R codes to get you started.
Automatic text summarization is the process of reducing the text content and retaining the
important points of the document. Generally, there are two approaches for automatic text summarization:
Extractive and Abstractive. The process of extractive based text summarization can be divided into two
phases: pre-processing and processing. In this paper, we discuss some of the extractive based text
summarization approaches used by researchers. We also provide the features for extractive based text
summarization process. We also present the available linguistic preprocessing tools with their features,
which are used for automatic text summarization. The tools and parameters useful for evaluating the
generated summary are also discussed in this paper. Moreover, we explain our proposed lexical chain
analysis approach, with sample generated lexical chains, for extractive based automatic text summarization.
We also provide the evaluation results of our system generated summary. The proposed lexical chain
analysis approach can be used to solve different text mining problems like topic classification, sentiment
analysis, and summarization.
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Extraction of Data Using Comparable Entity Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Following are the questions which I tried to answer in this ppt
What is text summarization.
What is automatic text summarization?
How it has evolved over the time?
What are different methods?
How deep learning is used for text summarization?
business application
in first few slides extractive summarization is explained, with pro and cons in next section abstractive on is explained.
In the last section business application of each one is highlighted
Nowadays Sentiment Analysis play an important Role in each field such as Stock market, product reviews, news article, political debates which help us to determining current trend in the market regarding specific product, event, issues. Here we are apply sentiment analysis on microblogging platforms such as twitter, Facebook which is used by different people to express their opinion with respect to different kind of foods in the field of home’schef. This paper explain different methods of text preprocessing and applies them with a naive Bayes classifier in a big data, distributed computing platform with the goal of creating a scalable sentiment analysis solution that can classify text into positive or negative categories. We apply negation handling, word n-grams, stemming, and feature selection to evaluate how different combinations of these pre-processing methods affect performance and efficiency.
Most of the text classification problems are associated with multiple class labels and hence automatic text
classification is one of the most challenging and prominent research area. Text classification is the
problem of categorizing text documents into different classes. In the multi-label classification scenario,
each document is associated may have more than one label. The real challenge in the multi-label
classification is the labelling of large number of text documents with a subset of class categories. The
feature extraction and classification of such text documents require an efficient machine learning algorithm
which performs automatic text classification. This paper describes the multi-label classification of product
review documents using Structured Support Vector Machine.
A Survey of Various Methods for Text SummarizationIJERD Editor
Document summarization means retrieved short and important text from the source document. In this paper, we studied various techniques. Plenty of techniques have been developed on English summarization and other Indian languages but very less efforts have been taken for Hindi language. Here, we discusses various techniques in which so many features are included such as time and memory consumption, efficiency, accuracy, ambiguity, redundancy.
An on-going project on Natural Language Processing (using Python and the NLTK toolkit), which focuses on the extraction of sentiment from a Question and its title on www.stackoverflow.com and determining the polarity.Based on the above findings, it is verified whether the rules and guidelines imposed by the SO community on the users are strictly followed or not.
This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
In this paper, I develop a custom binary classifier of search queries for the makeup category using different Machine Learning techniques and models. An extensive exploration of shallow and Deep Learning models was performed using a cross-validation framework to identify the top three models, optimize them tuning their hyperparameters, and finally creating an ensemble of models with a custom decision threshold that outperforms all other models. The final classifier achieves an accuracy of 98.83% on a test set, making it ready for production.
Conceptual Similarity Measurement Algorithm For Domain Specific OntologyZac Darcy
This paper presents the similarity measurement algorithm for domain specific terms collected in the
ontology based data integration system. This similarity measurement algorithm can be used in ontology
mapping and query service of ontology based data integration system. In this paper, we focus on the web
query service to apply this proposed algorithm. Concepts similarity is important for web query service
because the words in user input query are not same wholly with the concepts in ontology. So, we need to
extract the possible concepts that are match or related to the input words with the help of machine readable
dictionary WordNet. Sometimes, we use the generated mapping rules in query generation procedure for
some words that cannot be confirmed the similarity of these words by WordNet. We prove the effect of this
algorithm with two degree semantic result of web mining by generating the concepts results obtained form
the input query.
Conceptual similarity measurement algorithm for domain specific ontology[Zac Darcy
This paper presents the similarity measurement algorithm for domain specific terms collected in the
ontology based data integration system. This similarity measurement algorithm can be used in ontology
mapping and query service of
ontology based data integration sy
stem. In this paper, we focus
o
n the web
query service to apply
this proposed algorithm
. Concepts similarity is important for web query service
because the words in user input query are not
same wholly with the concepts in
ontology. So, we need to
extract the possible concepts that are match or related to the input words with the help of machine readable
dictionary WordNet. Sometimes, we use the generated mapping rules in query generation procedure for
some words that canno
t be
confirmed the similarity of these words
by WordNet. We prove the effect
of this
algorithm with two degree semantic result of web minin
g by generating
the concepts results obtained form
the input query
Abstract: Text classification or categorization is the process of automatically tagging a textual document with most relevant labels or categories. When the number of labels is restricted to one, the task becomes single-label text categorization. However, the multi-label version is challenging. For Arabic language, both tasks (especially the latter one) become more challenging in the absence of large and free Arabic rich and rational datasets. We used sub data from a large Single-labeled Arabic News Articles Dataset (SANAD) of textual data collected from three news portals. The dataset is a large one consisting of almost 200k articles distributed into seven categories that we offer to the research community on Arabic computational linguistics.
Code and data in this link:
https://github.com/hezam2022/text-categorization
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMSgerogepatton
Document similarity is an important part of Natural Language Processing and is most commonly used for
plagiarism-detection and text summarization. Thus, finding the overall most effective document similarity
algorithm could have a major positive impact on the field of Natural Language Processing. This report sets
out to examine the numerous document similarity algorithms, and determine which ones are the most
useful. It addresses the most effective document similarity algorithm by categorizing them into 3 types of
document similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-based
algorithms. The most effective algorithms in each category are also compared in our work using a series of
benchmark datasets and evaluations that test every possible area that each algorithm could be used in.
A COMPARISON OF DOCUMENT SIMILARITY ALGORITHMSgerogepatton
Document similarity is an important part of Natural Language Processing and is most commonly used for
plagiarism-detection and text summarization. Thus, finding the overall most effective document similarity
algorithm could have a major positive impact on the field of Natural Language Processing. This report sets
out to examine the numerous document similarity algorithms, and determine which ones are the most
useful. It addresses the most effective document similarity algorithm by categorizing them into 3 types of
document similarity algorithms: statistical algorithms, neural networks, and corpus/knowledge-based
algorithms. The most effective algorithms in each category are also compared in our work using a series of
benchmark datasets and evaluations that test every possible area that each algorithm could be used in.
With the rapidly increasing growth in the field of internet and web usage, it has become essential to use a certain specific powerful tool, which should be capable to analyze and rank all these available reviews/opinion on the web/Internet. In this paper we have propose a new and effective approach which uses a powerful sentiment analysis procedure which will be based on an ontological adjustment and arrangements. This study also aims to understand pos tag order to get detailed observation for any review or opinion, it also helps in identifying all present positive /Negative sentiments and suggest a proper sentence inclination. For this we have used reviews available on internet regarding Nokia and Stanford parser for the purpose or pos tagging.
Neural Network Based Context Sensitive Sentiment AnalysisEditor IJCATR
Social media communication is evolving more in these days. Social networking site is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform. These analyses are mostly performed in machine learning techniques which are less accurate than neural network methodologies. This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or neutral. It determines the overall topic of the given text. Context independent sentences and implicit meaning in the text are also considered in polarity classification.
IDENTIFYING THE SEMANTIC RELATIONS ON UNSTRUCTURED DATAijistjournal
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic concepts that characterizes the domain as well as their definitions and interrelationships. This paper will describe some algorithms for identifying semantic relations and constructing an Information Technology Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences. We then extract these sentences based on English pattern in order to build training set. We use a random sample among 245 categories of ACM to evaluate our results. Results generated show that our system yields superior performance.
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information
Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology
is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic
concepts that characterizes the domain as well as their definitions and interrelationships. This paper will
describe some algorithms for identifying semantic relations and constructing an Information Technology
Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed
based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our
algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language
Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences.
We then extract these sentences based on English pattern in order to build training set. We use a
random sample among 245 categories of ACM to evaluate our results. Results generated show that our
system yields superior performance.
The most integral part of our work is to extract Aspects from User Feedback and associate Sentiment and Opinion terms to them. The dataset we have at our disposal to work upon, is a set of feedback documents for various departments in a Hospital in XML format which have comments represented in tags. It contains about 65000 responses to a survey taken in a Hospital. Every response or comment is treated as a sentence or a set of them. We perform a sentence level aspect and sentiment extraction and we attempt to understand and mine User Feedback data to gather aspects from it. Further to it, we extract the sentiment mentions and evaluate them contextually for sentiment and associate those sentiment mentions with the corresponding aspects. To start with, we perform a clean up on the User Feedback data, followed by aspect extraction and sentiment polarity calculation, with the help of POS tagging and SentiWordNet filters respectively. The obtained sentiments are further classified according to a set of Linguistic rules and the scores are normalized to nullify any noise that might be present. We lay emphasis on using a rule based approach; rules being Linguistic rules that correspond to the positioning of various parts-of-speech words in a sentence.
Sentiment classification aims to detect information such as opinions, explicit , implicit feelings expressed
in text. The most existing approaches are able to detect either explicit expressions or implicit expressions of
sentiments in the text separately. In this proposed framework it will detect both Implicit and Explicit
expressions available in the meeting transcripts. It will classify the Positive, Negative, Neutral words and
also identify the topic of the particular meeting transcripts by using fuzzy logic. This paper aims to add
some additional features for improving the classification method. The quality of the sentiment classification
is improved using proposed fuzzy logic framework .In this fuzzy logic it includes the features like Fuzzy
rules and Fuzzy C-means algorithm.The quality of the output is evaluated using the parameters such as
precision, recall, f-measure. Here Fuzzy C-means Clustering technique measured in terms of Purity and
Entropy. The data set was validated using 10-fold cross validation method and observed 95% confidence
interval between the accuracy values .Finally, the proposed fuzzy logic method produced more than 85 %
accurate results and error rate is very less compared to existing sentiment classification techniques.
Seeds Affinity Propagation Based on Text ClusteringIJRES Journal
The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and experiential exercises, and in organizing participant’s preferences for the parameters of simulations. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters.
Similar to A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTE (20)
Effect of Query Formation on Web Search Engine Resultskevig
Query in a search engine is generally based on natural language. A query can be expressed in more than
one way without changing its meaning as it depends on thinking of human being at a particular moment.
Aim of the searcher is to get most relevant results immaterial of how the query has been expressed. In the
present paper, we have examined the results of search engine for change in coverage and similarity of first
few results when a query is entered in two semantically same but in different formats. Searching has been
made through Google search engine. Fifteen pairs of queries have been chosen for the study. The t-test has
been used for the purpose and the results have been checked on the basis of total documents found,
similarity of first five and first ten documents found in the results of a query entered in two different
formats. It has been found that the total coverage is same but first few results are significantly different.
Investigations of the Distributions of Phonemic Durations in Hindi and Dogrikevig
Speech generation is one of the most important areas of research in speech signal processing which is now gaining a serious attention. Speech is a natural form of communication in all living things. Computers with the ability to understand speech and speak with a human like voice are expected to contribute to the development of more natural man-machine interface. However, in order to give those functions that are even closer to those of human beings, we must learn more about the mechanisms by which speech is produced and perceived, and develop speech information processing technologies that can generate a more natural sounding systems. The so described field of stud, also called speech synthesis and more prominently acknowledged as text-to-speech synthesis, originated in the mid eighties because of the emergence of DSP and the rapid advancement of VLSI techniques. To understand this field of speech, it is necessary to understand the basic theory of speech production. Every language has different phonetic alphabets and a different set of possible phonemes and their combinations.
For the analysis of the speech signal, we have carried out the recording of five speakers in Dogri (3 male and 5 females) and eight speakers in Hindi language (4 male and 4 female). For estimating the durational distributions, the mean of mean of ten instances of vowels of each speaker in both the languages has been calculated. Investigations have shown that the two durational distributions differ significantly with respect to mean and standard deviation. The duration of phoneme is speaker dependent. The whole investigation can be concluded with the end result that almost all the Dogri phonemes have shorter duration, in comparison to Hindi phonemes. The period in milli seconds of same phonemes when uttered in Hindi were found to be longer compared to when they were spoken by a person with Dogri as his mother tongue. There are many applications which are directly of indirectly related to the research being carried out. For instance the main application may be for transforming Dogri speech into Hindi and vice versa, and further utilizing this application, we can develop a speech aid to teach Dogri to children. The results may also be useful for synthesizing the phonemes of Dogri using the parameters of the phonemes of Hindi and for building large vocabulary speech recognition systems.
May 2024 - Top10 Cited Articles in Natural Language Computingkevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Effect of Singular Value Decomposition Based Processing on Speech Perceptionkevig
Speech is an important biological signal for primary mode of communication among human being and also the most natural and efficient form of exchanging information among human in speech. Speech processing is the most important aspect in signal processing. In this paper the theory of linear algebra called singular value decomposition (SVD) is applied to the speech signal. SVD is a technique for deriving important parameters of a signal. The parameters derived using SVD may further be reduced by perceptual evaluation of the synthesized speech using only perceptually important parameters, where the speech signal can be compressed so that the information can be transformed into compressed form without losing its quality. This technique finds wide applications in speech compression, speech recognition, and speech synthesis. The objective of this paper is to investigate the effect of SVD based feature selection of the input speech on the perception of the processed speech signal. The speech signal which is in the form of vowels \a\, \e\, \u\ were recorded from each of the six speakers (3 males and 3 females). The vowels for the six speakers were analyzed using SVD based processing and the effect of the reduction in singular values was investigated on the perception of the resynthesized vowels using reduced singular values. Investigations have shown that the number of singular values can be drastically reduced without significantly affecting the perception of the vowels.
Identifying Key Terms in Prompts for Relevance Evaluation with GPT Modelskevig
Relevance evaluation of a query and a passage is essential in Information Retrieval (IR). Recently, numerous studies have been conducted on tasks related to relevance judgment using Large Language Models (LLMs) such as GPT-4,
demonstrating significant improvements. However, the efficacy of LLMs is considerably influenced by the design of the prompt. The purpose of this paper is to
identify which specific terms in prompts positively or negatively impact relevance
evaluation with LLMs. We employed two types of prompts: those used in previous
research and generated automatically by LLMs. By comparing the performance of
these prompts in both few-shot and zero-shot settings, we analyze the influence of
specific terms in the prompts. We have observed two main findings from our study.
First, we discovered that prompts using the term ‘answer’ lead to more effective
relevance evaluations than those using ‘relevant.’ This indicates that a more direct
approach, focusing on answering the query, tends to enhance performance. Second,
we noted the importance of appropriately balancing the scope of ‘relevance.’ While
the term ‘relevant’ can extend the scope too broadly, resulting in less precise evaluations, an optimal balance in defining relevance is crucial for accurate assessments.
The inclusion of few-shot examples helps in more precisely defining this balance.
By providing clearer contexts for the term ‘relevance,’ few-shot examples contribute
to refine relevance criteria. In conclusion, our study highlights the significance of
carefully selecting terms in prompts for relevance evaluation with LLMs.
Identifying Key Terms in Prompts for Relevance Evaluation with GPT Modelskevig
Relevance evaluation of a query and a passage is essential in Information Retrieval (IR). Recently, numerous studies have been conducted on tasks related to relevance judgment using Large Language Models (LLMs) such as GPT-4, demonstrating significant improvements. However, the efficacy of LLMs is considerably influenced by the design of the prompt. The purpose of this paper is to identify which specific terms in prompts positively or negatively impact relevance evaluation with LLMs. We employed two types of prompts: those used in previous research and generated automatically by LLMs. By comparing the performance of these prompts in both few-shot and zero-shot settings, we analyze the influence of specific terms in the prompts. We have observed two main findings from our study. First, we discovered that prompts using the term ‘answer’ lead to more effective relevance evaluations than those using ‘relevant.’ This indicates that a more direct approach, focusing on answering the query, tends to enhance performance. Second, we noted the importance of appropriately balancing the scope of ‘relevance.’ While the term ‘relevant’ can extend the scope too broadly, resulting in less precise evaluations, an optimal balance in defining relevance is crucial for accurate assessments. The inclusion of few-shot examples helps in more precisely defining this balance. By providing clearer contexts for the term ‘relevance,’ few-shot examples contribute to refine relevance criteria. In conclusion, our study highlights the significance of carefully selecting terms in prompts for relevance evaluation with LLMs.
In recent years, great advances have been made in the speed, accuracy, and coverage of automatic word
sense disambiguator systems that, given a word appearing in a certain context, can identify the sense of
that word. In this paper we consider the problem of deciding whether same words contained in different
documents are related to the same meaning or are homonyms. Our goal is to improve the estimate of the
similarity of documents in which some words may be used with different meanings. We present three new
strategies for solving this problem, which are used to filter out homonyms from the similarity computation.
Two of them are intrinsically non-semantic, whereas the other one has a semantic flavor and can also be
applied to word sense disambiguation. The three strategies have been embedded in an article document
recommendation system that one of the most important Italian ad-serving companies offers to its customers.
Genetic Approach For Arabic Part Of Speech Taggingkevig
With the growing number of textual resources available, the ability to understand them becomes critical.
An essential first step in understanding these sources is the ability to identify the parts-of-speech in each
sentence. Arabic is a morphologically rich language, which presents a challenge for part of speech
tagging. In this paper, our goal is to propose, improve, and implement a part-of-speech tagger based on a
genetic algorithm. The accuracy obtained with this method is comparable to that of other probabilistic
approaches.
Rule Based Transliteration Scheme for English to Punjabikevig
Machine Transliteration has come out to be an emerging and a very important research area in the field of
machine translation. Transliteration basically aims to preserve the phonological structure of words. Proper
transliteration of name entities plays a very significant role in improving the quality of machine translation.
In this paper we are doing machine transliteration for English-Punjabi language pair using rule based
approach. We have constructed some rules for syllabification. Syllabification is the process to extract or
separate the syllable from the words. In this we are calculating the probabilities for name entities (Proper
names and location). For those words which do not come under the category of name entities, separate
probabilities are being calculated by using relative frequency through a statistical machine translation
toolkit known as MOSES. Using these probabilities we are transliterating our input text from English to
Punjabi.
Improving Dialogue Management Through Data Optimizationkevig
In task-oriented dialogue systems, the ability for users to effortlessly communicate with machines and computers through natural language stands as a critical advancement. Central to these systems is the dialogue manager, a pivotal component tasked with navigating the conversation to effectively meet user goals by selecting the most appropriate response. Traditionally, the development of sophisticated dialogue management has embraced a variety of methodologies, including rule-based systems, reinforcement learning, and supervised learning, all aimed at optimizing response selection in light of user inputs. This research casts a spotlight on the pivotal role of data quality in enhancing the performance of dialogue managers. Through a detailed examination of prevalent errors within acclaimed datasets, such as Multiwoz 2.1 and SGD, we introduce an innovative synthetic dialogue generator designed to control the introduction of errors precisely. Our comprehensive analysis underscores the critical impact of dataset imperfections, especially mislabeling, on the challenges inherent in refining dialogue management processes.
Document Author Classification using Parsed Language Structurekevig
Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used, for example, to determine authorship of all of The Federalist Papers. Such methods may be useful in more modern times to detect fake or AI authorship. Progress in statistical natural language parsers introduces the possibility of using grammatical structure to detect authorship. In this paper we explore a new possibility for detecting authorship using grammatical structural information extracted using a statistical natural language parser. This paper provides a proof of concept, testing author classification based on grammatical structure on a set of “proof texts,” The Federalist Papers and Sanditon which have been as test cases in previous authorship detection studies. Several features extracted from the statisticalnaturallanguage parserwere explored: all subtrees of some depth from any level; rooted subtrees of some depth, part of speech, and part of speech by level in the parse tree. It was found to be helpful to project the features into a lower dimensional space. Statistical experiments on these documents demonstrate that information from a statistical parser can, in fact, assist in distinguishing authors.
Rag-Fusion: A New Take on Retrieval Augmented Generationkevig
Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores and fusing the documents and scores. Through manually evaluating answers on accuracy, relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and comprehensive answers due to the generated queries contextualizing the original query from various perspectives. However, some answers strayed off topic when the generated queries' relevance to the original query is insufficient. This research marks significant progress in artificial intelligence (AI) and natural language processing (NLP) applications and demonstrates transformations in a global and multi-industry context.
Performance, Energy Consumption and Costs: A Comparative Analysis of Automati...kevig
The common practice in Machine Learning research is to evaluate the top-performing models based on their performance. However, this often leads to overlooking other crucial aspects that should be given careful consideration. In some cases, the performance differences between various approaches may be insignificant, whereas factors like production costs, energy consumption, and carbon footprint should be taken into account. Large Language Models (LLMs) are widely used in academia and industry to address NLP problems. In this study, we present a comprehensive quantitative comparison between traditional approaches (SVM-based) and more recent approaches such as LLM (BERT family models) and generative models (GPT2 and LLAMA2), using the LexGLUE benchmark. Our evaluation takes into account not only performance parameters (standard indices), but also alternative measures such as timing, energy consumption and costs, which collectively contribute to the carbon footprint. To ensure a complete analysis, we separately considered the prototyping phase (which involves model selection through training-validation-test iterations) and the in-production phases. These phases follow distinct implementation procedures and require different resources. The results indicate that simpler algorithms often achieve performance levels similar to those of complex models (LLM and generative models), consuming much less energy and requiring fewer resources. These findings suggest that companies should consider additional considerations when choosing machine learning (ML) solutions. The analysis also demonstrates that it is increasingly necessary for the scientific world to also begin to consider aspects of energy consumption in model evaluations, in order to be able to give real meaning to the results obtained using standard metrics (Precision, Recall, F1 and so on).
Evaluation of Medium-Sized Language Models in German and English Languagekevig
Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answering, which requires models to provide elaborate answers without external document retrieval. The paper introduces an own test dataset and presents results from human evaluation. Results show that combining the best answers from different MLMs yielded an overall correct answer rate of 82.7% which is better than the 60.9% of ChatGPT. The best MLM achieved 71.8% and has 33B parameters, which highlights the importance of using appropriate training data for fine-tuning rather than solely relying on the number of parameters. More fine-grained feedback should be used to further improve the quality of answers. The open source community is quickly closing the gap to the best commercial models.
IMPROVING DIALOGUE MANAGEMENT THROUGH DATA OPTIMIZATIONkevig
In task-oriented dialogue systems, the ability for users to effortlessly communicate with machines and
computers through natural language stands as a critical advancement. Central to these systems is the
dialogue manager, a pivotal component tasked with navigating the conversation to effectively meet user
goals by selecting the most appropriate response. Traditionally, the development of sophisticated dialogue
management has embraced a variety of methodologies, including rule-based systems, reinforcement
learning, and supervised learning, all aimed at optimizing response selection in light of user inputs. This
research casts a spotlight on the pivotal role of data quality in enhancing the performance of dialogue
managers. Through a detailed examination of prevalent errors within acclaimed datasets, such as
Multiwoz 2.1 and SGD, we introduce an innovative synthetic dialogue generator designed to control the
introduction of errors precisely. Our comprehensive analysis underscores the critical impact of dataset
imperfections, especially mislabeling, on the challenges inherent in refining dialogue management
processes.
Document Author Classification Using Parsed Language Structurekevig
Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the
text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used,
for example, to determine authorship of all of The Federalist Papers. Such methods may be useful in more modern
times to detect fake or AI authorship. Progress in statistical natural language parsers introduces the possibility of
using grammatical structure to detect authorship. In this paper we explore a new possibility for detecting authorship
using grammatical structural information extracted using a statistical natural language parser. This paper provides a
proof of concept, testing author classification based on grammatical structure on a set of “proof texts,” The Federalist
Papers and Sanditon which have been as test cases in previous authorship detection studies. Several features extracted
of some depth, part of speech, and part of speech by level in the parse tree. It was found to be helpful to project the
features into a lower dimensional space. Statistical experiments on these documents demonstrate that information
from a statistical parser can, in fact, assist in distinguishing authors.
RAG-FUSION: A NEW TAKE ON RETRIEVALAUGMENTED GENERATIONkevig
Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product
information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots,
but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines
RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal
scores and fusing the documents and scores. Through manually evaluating answers on accuracy,
relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and
comprehensive answers due to the generated queries contextualizing the original query from various
perspectives. However, some answers strayed off topic when the generated queries' relevance to the
original query is insufficient. This research marks significant progress in artificial intelligence (AI) and
natural language processing (NLP) applications and demonstrates transformations in a global and multiindustry context
Performance, energy consumption and costs: a comparative analysis of automati...kevig
The common practice in Machine Learning research is to evaluate the top-performing models based on their
performance. However, this often leads to overlooking other crucial aspects that should be given careful
consideration. In some cases, the performance differences between various approaches may be insignificant, whereas factors like production costs, energy consumption, and carbon footprint should be taken into
account. Large Language Models (LLMs) are widely used in academia and industry to address NLP problems. In this study, we present a comprehensive quantitative comparison between traditional approaches
(SVM-based) and more recent approaches such as LLM (BERT family models) and generative models (GPT2 and LLAMA2), using the LexGLUE benchmark. Our evaluation takes into account not only performance
parameters (standard indices), but also alternative measures such as timing, energy consumption and costs,
which collectively contribute to the carbon footprint. To ensure a complete analysis, we separately considered the prototyping phase (which involves model selection through training-validation-test iterations) and
the in-production phases. These phases follow distinct implementation procedures and require different resources. The results indicate that simpler algorithms often achieve performance levels similar to those of
complex models (LLM and generative models), consuming much less energy and requiring fewer resources.
These findings suggest that companies should consider additional considerations when choosing machine
learning (ML) solutions. The analysis also demonstrates that it is increasingly necessary for the scientific
world to also begin to consider aspects of energy consumption in model evaluations, in order to be able to
give real meaning to the results obtained using standard metrics (Precision, Recall, F1 and so on).
EVALUATION OF MEDIUM-SIZED LANGUAGE MODELS IN GERMAN AND ENGLISH LANGUAGEkevig
Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks
clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six
billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative
question answering, which requires models to provide elaborate answers without external document
retrieval. The paper introduces an own test dataset and presents results from human evaluation. Results
show that combining the best answers from different MLMs yielded an overall correct answer rate of
82.7% which is better than the 60.9% of ChatGPT. The best MLM achieved 71.8% and has 33B
parameters, which highlights the importance of using appropriate training data for fine-tuning rather than
solely relying on the number of parameters. More fine-grained feedback should be used to further improve
the quality of answers. The open source community is quickly closing the gap to the best commercial
models.
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
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A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTE
1. International Journal on Natural Language Computing (IJNLC) Vol.7, No.5, October 2018
DOI: 10.5121/ijnlc.2018.7503 27
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON
MAJORITY VOTE
Roger Booto Tokime1 and Eric Hervet1
1Perception, Robotics, and Intelligent Machines Research Group (PRIME), Computer
Science, Universite´ de Moncton, Moncton, NB, Canada
ABSTRACT
We propose an automatic classification system of movie genres based on different features from their textual
synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learn-
ing relationships between textual signatures and movie genres. Then it is tested on other movie synopsis,
and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
KEYWORDS
Movie Synopsis, Text Classifier, Information Retrieval, Text Mining.
1. INTRODUCTION
Automatic classification of text is known to be a difficult task for a computer. Usually, algorithms
in the field try to regroup a given set of texts into genres or categories that can contain subcategories
(e.g. News articles can be separated in sports, news, weather, etc.), or even regroup them together
to form a new one. This can be accomplished by the use of several methods and algorithms that are
usually based on text analysis, word grouping, sentence segmentation, etc. Automatic text classi-
fication is widely tackled in the field of classifying textual data from the web [6] [9] [8], and some
research has been done on classifying other textual data such as books summaries [2]. Though
there are many algorithms and strategies already in place, with some using advanced approaches
like machine learning [3], it is still difficult for algorithms to classify correctly texts into genres.
The main reason for this has been explained by Chandler in his Introduction to Genre Theory:
”There are no rigid rules of inclusion or exclusion. Genres are not discrete systems consisting of
a fixed number of listable items”. While working on a computer vision homework, we had to
implement an algorithm for the feature extraction task. We used the Hough transform to do so,
and we got to study the purpose of this technique. Wikipedia gives a very good description of its
purpose: ”The purpose of the technique is to find imperfect instances of objects within a certain
class of shapes by a voting procedure. This voting procedure is carried out in a parameter space,
from which object candidates are obtained as local maxima in a so-called accumulator space, that
is explicitly constructed by the algorithm for computing the Hough transform.” [12]. What is
important to keep in mind is that in computer vision, we are able to find features in an image by
letting multiples algorithms vote to what they consider a good feature, and at the end keeping
only the most rated. We then thought to apply this principle to classify text into genres, which is
the main matter of this work. This paper shows how to make use of a combination of features in
order to build a majority voting system that finds automatically the category(ies) which a text
belongs to. Our work focuses on the classification of movie synopsis into genres. First, we are
going to introduce a new concept, the movie signature. A movie signature is defined as the list of
2. International Journal on Natural Language Computing (IJNLC) Vol.7, No.5, October 2018
28
categories found by the system after voting. Now, let’s have an overview of the struc- ture of this
paper, which is organized as follows: First, we describe how the data collection has been retrieved
and processed before being presented to the system. Second, we present the different algorithms
compared to compute the similarity between movie synopsis. Then we present the results that
show how the majority voting system reaches a precision exceed- ing 75%. Finally, we conclude
with a discussion about possible improvements and other application do- mains where our system
could be used to classify other types of documents than movie synopsis
2. RELATED WORK
This paper is about automatic classification of movie synopsis into genres. To do so, we use a
novel approach that combines the strengths of several similarity algorithms (that could be referred
as voters) from the domain of Natural Language Processing (NLP) in order to obtain a better
classification. Each of these algorithms taken individually performs great on some specific data,
but is not able to process efficiently other various kind of data. The goal of our method is to
generalize the classification of textual data into pre-labeled categories. To do so, we explore
different NLP methods that classify textual data based on specific features. The methods chosen
as eligible to vote are the ones that perform the best in their category. We have the following
feature comparison themes: term frequencies, sets, vector-based and strings. Those features are
the most used in the field of information retrieval and text classification.
2.1 Term frequencies comparison
In order to know if a textual query is a member of a category, the first thing we can think of is to
count the number of words from the textual query and then sum it up. This sum can be used as a
measure of the similarity between the textual query and the pre-labeled database.
2.1.1 Term frequency
Term frequency is at first glance a good idea because we would expect that the words representing a
target category appear more often than the others, but it is not that simple. Term frequency has a
big defect due to its definition: Indeed, by counting only the number of words from the textual
query to a category, e.g., Action, the frequent words in the English language which bring no
relevant information such as “the”, “a”, “about”, etc. are the words that come up most often. To
avoid this problem we could consider only the words repeating with a certain frequency, but then
it rises the question of the right threshold to use. To bypass this issue, we use a list of stop-words
from the English language [7]. A stop-word is defined as a word that comes up most often in
various texts of a given language and provides no relevant information to categorize the text. Now
that we are able to filer stop-words, here is the mathematical expression that we use to calculate
the similarity between two texts, based on the term frequency. Let us assume that w is a word
from the query that occurs in a target category, cat (reset at each run of an algorithm) is the list of
categories, and x is a given target category, e.g., Action. We have that:
This means that we use a binary classification for each word, according it is a stop-word or not.
Since in our case, a text query is a movie synopsis, cat represents a similarity measure between a
movie plot and all the different known genres in which we would like to know to what extent a
given synopsis applies to. This method is simple to implement and give good results. The main
3. International Journal on Natural Language Computing (IJNLC) Vol.7, No.5, October 2018
29
.
concern about this method is that the quality of the results is really dependent on the stop-word
list, so in practice a prior knowledge and pre-processing of the genres, especially about what a
stop-word is in the field of movie synopsis, is necessary to achieve accurate classification results.
2.1.2 Term frequency inverse documentfrequency
Text classification by simply counting the frequencies of words, minus the stop-words, is then
very limited when previous knowledge of the classifying genres is not available. Since we are
trying to build an accurate voting system, we use the same basis as this type of algorithm, but we
introduce a much more robust term frequency (TF) similarity algorithm. Here, the approach is
different in a way that we consider the words of each kind of our genre list as independent
entities, and their influence during the classification is represented by some weight wt. This way
of doing things is called feature selection. Feature selection studies how to select a subset or list
of attributes or variables that are used to construct models describing data. Its purposes include
dimensions reduction, removing irrelevant and redundant features, reducing the amount of data
needed for learning, improving algorithm’s predictive accuracy, and increasing the constructed
model’s comprehensibility [14]. Our goal here is to reduce the vector dimension by automatically
removing irrelevant words. An irrelevant word is defined as a word that appears in at least 80% of
the categories. To do so, we use the inverse document frequency (IDF) measure. The IDF measure
is based on the document frequency (DF), which is simply the number of documents in which a
word w appears. The IDF can be computed as follows:
where D is the total number of document (genres), and the plus one at denominator is used in case a
word appears in none of the datasets, to avoid dividing by zero [5]. With this measurement, we
can simply remove irrelevant words that appear in at least 80% of the categories by checking if the
result of the ratio (DF/D) is less than 0.8. The calculation of the term frequency inverse document
frequency (TFIDF) is performed as follows:
TFIDF is a measure of the importance of a word w in a document or a set of documents. Let us
assume that w is a word from the query that occurs in a category, cat is the list of categories, and
x is a target category, e.g. Action. We have that
We have now a robust term frequency similarity measure that eliminates non relevant words to
our purpose from the entire vocabulary (over all documents).
2.2 Sets comparison
In this section, we present a new approach which does not compare the frequencies of words when
calculating the similarity, but simply checks the presence or the absence of a word. To do so, we
use the Jaccard coefficient.
4. International Journal on Natural Language Computing (IJNLC) Vol.7, No.5, October 2018
30
2.2.1Jaccard similarity
The Jaccard coefficient, also known as the Tanimoto coefficient, is a measure of similarity based
on datasets. In the article ”Text Categorization using Jaccard Coefficient for Text Messages” [4], it
is shown that this kind of similarity is extremely simple and fast to compute, since it comes down
to the simple calculation of the division of the intersection by the union of two sets of data The
values of the Jaccard coefficient range from [0, 1], with 0 meaning that the two datasets are not
similar, and 1 that they are exactly the same. Let us assume that pq is the plot query, cat is the list
of categories and x is any target category, e.g., Action. We have that
J (x) measures the similarity between the set of categories and the set of words of the plot query.
Since our case, the cat sets are much larger than the pq set, the Jaccard coefficient can never reach
the value 1. But it still provides a good order of similarity between the different genres. Therefore,
the Jaccard similarity measure is one of the method we use in our combined voting system.
2.3 Comparison of the angle between vectors
At this stage, we change the way we look at our documents, and rather represent them as vectors
of words, also called bags of words. A bag of words representation allows us to calculate the
correlation between two bags, which is used as the similarity measure between the corresponding
documents. Calculating the correlation between bags or vectors of words is the same as calculating
the angle between two vectors, and is known as cosine similarity.
2.3.1 Cosine similarity
The cosine similarity measures the cosine of the angle between two vectors of words. Each vector
is an n-dimensional vector over the words W = {w1, w2, . . . , wn}, and each dimension represents a
word with its weight (usually TF) in the document. Also, the word vector similarity coefficient
does not vary if the vectors do not have the same lengths. This feature is especially convenient for
us since our datasets vocabulary is usually much bigger than the synopsis query. Because the
weight of each word is its TF, the angle of similarity between two vectors is non-negative and
bounded in [0, 1] [1]. Let us assume that v is the plot query vector, cat is the list of categories and
cat(x) is any specific category vector, e.g., Action. We have that:
This means that we consider the information as a vector of words rather than a set of words, and
the angle between the two vectors is used as a similarity metric.
2.4 String comparison
Here, we get deeper into our comparison scheme by going as low as comparing the strings from a
given plot query to the ones in the dataset. This level is the lowest we can go in order to compare
two sets of textual data. The idea here is to have an algorithm that performs well under the
following conditions: strings (words) with a small distance must be similar, and the order in
which the words are distributed in the text must not affect the similarity coefficient. The reason
5. International Journal on Natural Language Computing (IJNLC) Vol.7, No.5, October 2018
31
behind these conditions is to give the algorithm the possibility to be used at large scale.
2.4.1 Dice similarity
Well-known algorithms, such as Edit Distance, Longest Common Substring and others, do not
perform well against these requirements. The Edit distance does respect the first requirement,
which states “words with a small distance must be similar”, as it rates “FRANCE” and “QUEBEC”
as being similar with a distance of 6, compared to “FRANCE” and “REPUBLIC OF FRANCE”
with a distance of 12. The Longest Common Substring would give “FRANCE” and “REPUBLIC
OF FRANCE” quite a good rating of similarity with a distance of 6, but would fail at giving the
same similarity value for “FRENCH REPUBLIC”, “REPUBLIC OF FRANCE” and “REPUBLIC
OF CUBA” [10]. For these reasons, we decided to use the Dice coefficient as it does respect both
conditions. The Dice coefficient is defined as twice the number of common terms in the compared
strings, divided by the total number of terms in both strings [11]. Let us assume that pairs(x) is
function that generates the pairs of adjacent letters in the plot query, cat(x) is the same function
for the list of categories, and s1and s2 are respectively a plot query and a specific dataset genre
text, e.g., Action. We have that
Here, each plot and categories are decomposed in pairs of words, and the calculation works as
follows: it computes the percentage of pairs of words that can be found both in the plot s1and in
the category s2, then multiplies it by a factor of pairs that belongs to both s1 and s2.
3. DATASETS
The data used in this work are movie synopsis from Wikipedia [13]. The training data is a selection
of American, British, and French cinematographic works from 1920 to 2016. The collection of
data is made once and requires four steps: The first step consists in retrieving the information from
the Wikipedia website; The second step consists in filtering only the cinematographic movies, of
which at least one of the genres belongs to the following list of 19 genres: Action, Adventure,
Animation, Comedy, Crime, Drama, Fantasy, Family, Fiction, Historical, Horror, Mystery, Noir,
Romance, Science, Short, Thriller, War, and Western. The third step consists in repeating steps 1
and 2 with the data from the OMDB database, with the difference that this time the requests are
limited to the movie titles previously filtered. At the end of the third step, the number of movies
goes from 25561 to 9937, and after eliminating duplicates there is a total of 8492 movie titles
remaining. Those are the ones used to build the training dataset used in the fourth step. The fourth
step consists in the creation of 19-word banks corresponding to the 19 aforementioned genres. The
word banks are created as follows: Let T be a movie object, and cat a word bank. We have that:
The means we add the movie titles to the word bank to help the algorithms that calculate the
similarity between two documents. The same steps are performed when creating the test data. The
test data contains Canadian, Spanish, Australian, Japanese, and Chinese cinematographic works
from 2000 to 2015. A total of 793 different titles are used as test data. We would like to precise
that for this experiment, all textual information were taken from the english version of Wikipedia,
therefore the only language conflict we may have could be the translations of the synopsis into
english.
6. International Journal on Natural Language Computing (IJNLC) Vol.7, No.5, October 2018
4. SIMILARITY MEASURES
To allow various algorithms to vote,
two objects and measure their level
characteristic features upon which
istics are dependent on the data,
universally best for all kinds of clustering
that a combination of different features
approach is similar to the ones used
created pre-labeled databases. The
by different similarity calculation
rithms, the classification of the test
obtained by each of the similarity algorithms. More
compare similar features between
and angles between vectors of words.
the algorithms used and compared in this
5. RESULTS
Our method is a majority voting system, which means that each algorithm has the chance to
for its top candidates. The final election is conducted as follows: First, each algorithm receives
the same synopsis query, and classifies
each algorithm are selected to create
to the true genres, and the average
where MS represents the movie signature,
lenSum the true positive predictions for a
expected across the test set. We
candidates from each voter. The reason we limited our
mathematical: Indeed, we have 5
case, if each voter elects different candidates, we get a movie signature
total number of genres, which means
reduces the precision, and enhances the probability to find a genre in which a film synopsis
belongs to. Table 1 shows the resul
majority voting system on the precision
the measure of precision in the context of classification is computed as
Natural Language Computing (IJNLC) Vol.7, No.5, October 2018
5
EASURES
vote, a measure of similarity must be defined, in order
level of disparity or similarity. Usually, this measure depends on
which we want to make a comparison. In many cases, these
or on the problem context at hand, and there is no measure
clustering problems [1]. This is the reason we made the
features would perform a better classification of textual
used in supervised machine learning since it is based on
The percentage of belonging of a text to each category
calculation algorithms. However, unlike supervised machine learning
test data is performed by choosing the most significant
obtained by each of the similarity algorithms. More precisely, the algorithms used in this work
between two text documents based on: term frequencies, sets,
words. The following sections of this paper give some
the algorithms used and compared in this work.
Our method is a majority voting system, which means that each algorithm has the chance to
for its top candidates. The final election is conducted as follows: First, each algorithm receives
classifies the query over all genres. Second, the top candidates
create the movie signature. Third, the movie signature is
average accuracy is computed in the following way
represents the movie signature, GT the ground truth, N the number of test synopsis,
predictions for a given synopsis, and SumttT the total amount of genres
We conducted three elections where we selected the top 1, 2 and 3
The reason we limited our vote to the top 3 best candidates is purely
voters and 19 genres, and: 19
= 3. This means that in
case, if each voter elects different candidates, we get a movie signature length that is less than the
means that there is a long list of elected candidates.
reduces the precision, and enhances the probability to find a genre in which a film synopsis
results ordered from top 1 to 3, while table 2 shows the
precision of the algorithm. This result is completely expected
the measure of precision in the context of classification is computed as
Natural Language Computing (IJNLC) Vol.7, No.5, October 2018
32
order to compare
this measure depends on
these character-
measure that is
the hypothesis
textual data. This
on previously
category is computed
learning algo-
significant categories
the algorithms used in this work
sets, bi-grams,
some details about
Our method is a majority voting system, which means that each algorithm has the chance to vote
for its top candidates. The final election is conducted as follows: First, each algorithm receives
candidates from
is compared
the number of test synopsis,
the total amount of genres
we selected the top 1, 2 and 3
to the top 3 best candidates is purely
the worst
length that is less than the
Therefore, it
reduces the precision, and enhances the probability to find a genre in which a film synopsis
the effect of the
expected since
the measure of precision in the context of classification is computed as follows:
7. International Journal on Natural Language Computing (IJNLC) Vol.7, No.5, October 2018
33
It is the ratio between the ground truth (truePositive), and the sum of our signature and the ground
truth. Our signature (predicted genres) length is significantly bigger than the ground truth when
considering the top choices, as shown in table 3, and because a false positive result is the set of
propositions that are in our signature, but not in the ground truth, we end up with a low precision.
This is a trade-off we had to make because finding the genre(s) which a synopsis truly belongs to
is more important in our case. So here is how we compute the value of falsePositive based on MS
(Movie Signature) and GT (Ground Truth):
Table 1 presents the best accuracies obtained when considering the top 1, 2, and 3 movie genres.
Table 2 compares our algorithm, Majority Vote, to the five other existing algorithms described
earlier, in terms of accuracy, precision, recall, and F-measure, also called F1-score. The F-measure
is defined as the harmonic mean of precision and recall :
Finally, Table 3 presents the average signature lengths for top 1, 2, and 3 movie genres based on
our algorithm.
Table 1. Top 1 to 3 best classification accuracies
Table 2.Performance of our proposed algorithm vs. the existing ones
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34
Table 3 . Increase of the signature length with our method
6. CONCLUSION
In summary, we tackled a challenging problem in the field of text classification into multiple
genres. More specifically, we developed a new method that automatically assigns genres to a
movie synopsis. As explained in the introduction, the difficulty of this task comes from the fact
that there is no rigid boundaries between the genres. To solve this issue, we proposed a majority
voting system inspired by the Hough transform which is usually used in computer vision. Our
method is able to generalize text classification into a category with an accuracy exceeding 75%. To
obtain those results, we had to make some trade-off to the detriment of the precision of prediction.
Due to the absence of rigid rules to discriminate between genres, we put the focus on the accuracy
results, and outperformed the existing algorithms in the field. In addition, our system is language-
invariant up to the extent of the stop-word list used by the term frequency voting method. We
tested our method on movie synopsis with good results, and we believe that it could be used to
classify books, journals, and even websites.
The next step after this work is to use deep learning, especially long short-term memory (LSTM)
neural networks, to compare and classify movie synopsis.
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