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This document summarizes experiments on bilingual semantic text similarity using various word and sentence embedding approaches. It fine-tunes the COMET machine translation evaluation model on 100,000 parallel English-Hindi sentences and compares the results to other sentence encoders like LaBSE, LASER and SBERT using Pearson correlation. LaBSE and SBERT are found to give the best results. Future work could include fine-tuning models on larger data to potentially achieve better performance.
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A simplified classification computational model of opinion mining using deep ...
Opinion and attempts to develop an automated system to determine people's viewpoints towards various units such as events, topics, products, services, organizations, individuals, and issues. Opinion analysis from the natural text can be regarded as a text and sequence classification problem which poses high feature space due to the involvement of dynamic information that needs to be addressed precisely. This paper introduces effective modelling of human opinion analysis from social media data subjected to complex and dynamic content. Firstly, a customized preprocessing operation based on natural language processing mechanisms as an effective data treatment process towards building quality-aware input data. On the other hand, a suitable deep learning technique, bidirectional long short term-memory (Bi-LSTM), is implemented for the opinion classification, followed by a data modelling process where truncating and padding is performed manually to achieve better data generalization in the training phase. The design and development of the model are carried on the MATLAB tool. The performance analysis has shown that the proposed system offers a significant advantage in terms of classification accuracy and less training time due to a reduction in the feature space by the data treatment operation.
2015-SemEval2015_poster
This document describes an SVM approach called MiniExperts for measuring semantic textual similarity that was submitted to SemEval 2015 Task 2. It extracts various features from text such as part-of-speech tags, lemmatization, named entities, and multiword expressions. It also calculates conceptual, semantic, and distributional similarity measures between sentences. When applied to the SemEval task, it achieved a Pearson correlation of 0.7216 for English and 0.5158 for Spanish, ranking 33rd and 9th respectively out of the submitted systems. Future work will focus on extracting conceptual descriptions from BabelNet to further improve matching of conceptual terms between sentences.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
Gender Classification of Blog Authors: With Feature Engineering and Deep Lear...
In this paper, we present two approaches to automatically classify the gender of blog authors: the first is a manual feature extraction based system incorporating two novel feature classes: variable length character sequence patterns and thirteen new word classes, along with an added class of surface features while the second is a first-ever application of a memory variant of Recurrent Neural Networks, i.e. Bidirectional Long Short Term Memory Networks (BLSTMs) on this task. We use two blog data sets to report our results: the first is a well-explored one used by the previous state-of-the-art models while the other is a 20 times larger corpus. For the first system, we use a voting of machine learning classifiers to obtain an improved accuracy with respect to the previous feature mining systems on the former data set. Using our second approach, we show that the accuracy obtained using such deep LSTMs is comparable to the current state-of-the-art deep learning system for the task of gender classification. Finally, we carry out a comparative study of performance of both the systems on the two data sets.
A-STUDY-ON-SENTIMENT-POLARITY.pdf
A STUDY ON SENTIMENT POLARITY IDENTIFICATION OF
INDIAN MULTILINGUAL TWEETS THROUGH DIFFERENT
NEURAL NETWORK MODELS, CNN, RNN, LSTM
A hybrid composite features based sentence level sentiment analyzer
Current lexica and machine learning based sentiment analysis approaches
still suffer from a two-fold limitation. First, manual lexicon construction and
machine training is time consuming and error-prone. Second, the
prediction’s accuracy entails sentences and their corresponding training text
should fall under the same domain. In this article, we experimentally
evaluate four sentiment classifiers, namely support vector machines (SVMs),
Naive Bayes (NB), logistic regression (LR) and random forest (RF). We
quantify the quality of each of these models using three real-world datasets
that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic
movie reviews. Specifically, we study the impact of a variety of natural
language processing (NLP) pipelines on the quality of the predicted
sentiment orientations. Additionally, we measure the impact of incorporating
lexical semantic knowledge captured by WordNet on expanding original
words in sentences. Findings demonstrate that the utilizing different NLP
pipelines and semantic relationships impacts the quality of the sentiment
analyzers. In particular, results indicate that coupling lemmatization and
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With this coupling, the accuracy of the SVM classifier has improved to
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Sentiment Analysis of Bengali text using Gated Recurrent Neural Network
Sentiment analysis is a fundamental part of Natural Language
Processing. There are numerous works on this topic in English and other
languages. However, it is still a comparatively new practice in Bangla. The
absence of a suitable Bangla corpus is the primary obstacle for sentiment
analysis tasks in Bangla. Nonetheless, Long Short-term Memory (LSTM) is a
common technique for resolving sentiments from a dataset containing a large
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datasets with a low amount of text data. In this manuscript, we present a 5-
layered GRU neural network model, each layer comprising of 48 neurons,
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time, we considered the averages of the ten validation accuracy and losses and
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highest accuracy) for Bi-directional LSTM (BLSTM). The highest accuracies
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A simplified classification computational model of opinion mining using deep ...
Opinion and attempts to develop an automated system to determine people's viewpoints towards various units such as events, topics, products, services, organizations, individuals, and issues. Opinion analysis from the natural text can be regarded as a text and sequence classification problem which poses high feature space due to the involvement of dynamic information that needs to be addressed precisely. This paper introduces effective modelling of human opinion analysis from social media data subjected to complex and dynamic content. Firstly, a customized preprocessing operation based on natural language processing mechanisms as an effective data treatment process towards building quality-aware input data. On the other hand, a suitable deep learning technique, bidirectional long short term-memory (Bi-LSTM), is implemented for the opinion classification, followed by a data modelling process where truncating and padding is performed manually to achieve better data generalization in the training phase. The design and development of the model are carried on the MATLAB tool. The performance analysis has shown that the proposed system offers a significant advantage in terms of classification accuracy and less training time due to a reduction in the feature space by the data treatment operation.
2015-SemEval2015_poster
This document describes an SVM approach called MiniExperts for measuring semantic textual similarity that was submitted to SemEval 2015 Task 2. It extracts various features from text such as part-of-speech tags, lemmatization, named entities, and multiword expressions. It also calculates conceptual, semantic, and distributional similarity measures between sentences. When applied to the SemEval task, it achieved a Pearson correlation of 0.7216 for English and 0.5158 for Spanish, ranking 33rd and 9th respectively out of the submitted systems. Future work will focus on extracting conceptual descriptions from BabelNet to further improve matching of conceptual terms between sentences.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
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A-STUDY-ON-SENTIMENT-POLARITY.pdf
A STUDY ON SENTIMENT POLARITY IDENTIFICATION OF
INDIAN MULTILINGUAL TWEETS THROUGH DIFFERENT
NEURAL NETWORK MODELS, CNN, RNN, LSTM
A hybrid composite features based sentence level sentiment analyzer
Current lexica and machine learning based sentiment analysis approaches
still suffer from a two-fold limitation. First, manual lexicon construction and
machine training is time consuming and error-prone. Second, the
prediction’s accuracy entails sentences and their corresponding training text
should fall under the same domain. In this article, we experimentally
evaluate four sentiment classifiers, namely support vector machines (SVMs),
Naive Bayes (NB), logistic regression (LR) and random forest (RF). We
quantify the quality of each of these models using three real-world datasets
that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic
movie reviews. Specifically, we study the impact of a variety of natural
language processing (NLP) pipelines on the quality of the predicted
sentiment orientations. Additionally, we measure the impact of incorporating
lexical semantic knowledge captured by WordNet on expanding original
words in sentences. Findings demonstrate that the utilizing different NLP
pipelines and semantic relationships impacts the quality of the sentiment
analyzers. In particular, results indicate that coupling lemmatization and
knowledge-based n-gram features proved to produce higher accuracy results.
With this coupling, the accuracy of the SVM classifier has improved to
90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three
other classifiers.
Sentiment Analysis of Bengali text using Gated Recurrent Neural Network
Sentiment analysis is a fundamental part of Natural Language
Processing. There are numerous works on this topic in English and other
languages. However, it is still a comparatively new practice in Bangla. The
absence of a suitable Bangla corpus is the primary obstacle for sentiment
analysis tasks in Bangla. Nonetheless, Long Short-term Memory (LSTM) is a
common technique for resolving sentiments from a dataset containing a large
amount of text data. However, Gated Recurrent Unit (GRU) is very efficient for
datasets with a low amount of text data. In this manuscript, we present a 5-
layered GRU neural network model, each layer comprising of 48 neurons,
applied the model on an existing Bangla corpus. We implemented the 10-folds
cross-validation approach and repeated the same processes three times. Each
time, we considered the averages of the ten validation accuracy and losses and
compared the results with the state-of-the-art published outcome (77.85%
highest accuracy) for Bi-directional LSTM (BLSTM). The highest accuracies
for our model was 78.41%, while the lowest accuracy was 76.34%.
Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis
The rapid growth of internet facilities has increased the comments, posts, blogs, feedback, etc., on a
large scale on social networking sites. These social media data are available in an unstructured form,
which includes images, text, and videos. The processing of these data is difficult, but some sentiment
analysis, information retrieval, and recommender systems are used to process these unstructured data.
To extract the opinion and sentiment of internet users from their written social media text, a sentiment
analysis system is required to develop, which can work on both monolingual and bilingual phonetic text.
Therefore, a sentiment analysis (SA) system is developed, which performs well on different domain
datasets. The system performance is tested on four different datasets and achieved better accuracy of
3% on social media datasets, 1.5% on movie reviews, 1.35% on Amazon product reviews, and 4.56%
on large Amazon product reviews than the state-of-art techniques. Also, the stemmer (StemVerb) for
verbs of the English language is proposed, which improves the SA system’s performance.
https://www.researchgate.net/publication/350987101_Classification_of_Code-Mixed_Bilingual_Phonetic_Text_Using_Sentiment_Analysis
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Nowadays, online communication is more convenient and popular than faceto-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have welldeveloped libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.
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Using Decision Tree for Automatic Identification of Bengali Noun-Noun Compounds
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Continuous bag of words (cbow) word2vec word embedding work is that it tends to predict the
probability of a word given a context. A context may be a single word or a group of words. But for
simplicity, I will take a single context word and try to predict a single target word.
The purpose of this question is to be able to create a word embedding for the given data set.
data set text:
In linguistics word embeddings were discussed in the research area of distributional semantics. It
aims to quantify and categorize semantic similarities between linguistic items based on their
distributional properties in large samples of language data. The underlying idea that "a word is
characterized by the company it keeps" was popularized by Firth.
The technique of representing words as vectors has roots in the 1960s with the development of
the vector space model for information retrieval. Reducing the number of dimensions using
singular value decomposition then led to the introduction of latent semantic analysis in the late
1980s.In 2000 Bengio et al. provided in a series of papers the "Neural probabilistic language
models" to reduce the high dimensionality of words representations in contexts by "learning a
distributed representation for words". (Bengio et al, 2003). Word embeddings come in two different
styles, one in which words are expressed as vectors of co-occurring words, and another in which
words are expressed as vectors of linguistic contexts in which the words occur; these different
styles are studied in (Lavelli et al, 2004). Roweis and Saul published in Science how to use
"locally linear embedding" (LLE) to discover representations of high dimensional data structures.
The area developed gradually and really took off after 2010, partly because important advances
had been made since then on the quality of vectors and the training speed of the model.
There are many branches and many research groups working on word embeddings. In 2013, a
team at Google led by Tomas Mikolov created word2vec, a word embedding toolkit which can train
vector space models faster than the previous approaches. Most new word embedding techniques
rely on a neural network architecture instead of more traditional n-gram models and unsupervised
learning.
Limitations
One of the main limitations of word embeddings (word vector space models in general) is that
possible meanings of a word are conflated into a single representation (a single vector in the
semantic space). Sense embeddings are a solution to this problem: individual meanings of words
are represented as distinct vectors in the space.
For biological sequences: BioVectors
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bioinformatics applications have been proposed by Asgari and Mofrad. Named bio-vectors
(BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins
(amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representa.
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Journal of Computer Science Research | Vol.1, Iss.3
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https://doi.org/10.30564/jcsr.v1i3.1466
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https://doi.org/10.30564/jcsr.v1i3.1264
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https://doi.org/10.30564/jcsr.v1i3.1441
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INFT2060 Applied Artificial Intelligence.docx
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Machine Learning Techniques with Ontology for Subjective Answer Evaluation
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Ontology makes the evaluation process holistic as presence of keywords, synonyms, the right word
combination and coverage of concepts can be checked. In this paper, the above mentioned techniques are
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answers of Computer Science. Domain Ontology of Computer Graphics is designed and developed. The
software used for implementation includes Java Programming Language and tools such as MATLAB,
Protégé, etc. Ten questions from Computer Graphics with sixty answers for each question are used for
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Ontology.
Eurocall 2014 titova
This document summarizes a research project that studied the use of student response systems (SRS) to support mobile learning in university lectures. The study found that incorporating SRS-based formative assessments and collaborative activities into lectures improved student engagement and academic performance. Students reported that the SRS-supported approach prepared them well and helped them understand content. The researcher suggests further exploring new interactive mobile-enabled activities and assessment models to enhance learning beyond traditional boundaries.
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Evaluating sentiment analysis and word embedding techniques on Brexit
In this study, we investigate the effectiveness of pre-trained word embeddings for sentiment analysis on a real-world topic, namely Brexit. We compare the performance of several popular word embedding models such global vectors for word representation (GloVe), FastText, word to vec (word2vec), and embeddings from language models (ELMo) on a dataset of tweets related to Brexit and evaluate their ability to classify the sentiment of the tweets as positive, negative, or neutral. We find that pre-trained word embeddings provide useful features for sentiment analysis and can significantly improve the performance of machine learning models. We also discuss the challenges and limitations of applying these models to complex, real-world texts such as those related to Brexit.
Cross language information retrieval in indian
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
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population expansion, and economic progress, the effects on natural ecosystems are becoming
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significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
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centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
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and water managers, and urban planners, are interested in obtaining data on land use and cover
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crucial for coordinated efforts across different administrative levels. Advanced technologies like
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occur natural.
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Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis
The rapid growth of internet facilities has increased the comments, posts, blogs, feedback, etc., on a
large scale on social networking sites. These social media data are available in an unstructured form,
which includes images, text, and videos. The processing of these data is difficult, but some sentiment
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https://www.researchgate.net/publication/350987101_Classification_of_Code-Mixed_Bilingual_Phonetic_Text_Using_Sentiment_Analysis
Sentiment analysis on Bangla conversation using machine learning approach
Nowadays, online communication is more convenient and popular than faceto-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have welldeveloped libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.
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This document summarizes a research paper that proposes a lexicon-based method called NBLex to predict emotions in Kannada-English code-switch text. The paper describes creating a Kannada-English code-switch corpus by collecting comments from YouTube and annotating them. A Naive Bayes classifier is used to build the NBLex lexicon and predict emotions. Evaluation shows the NBLex method achieves 87.9% accuracy for emotion prediction, outperforming Naive Bayes and BiLSTM baselines. The paper concludes that a simple additive lexicon approach can be an alternative to machine learning for emotion prediction in code-switch text.
IRJET- Short-Text Semantic Similarity using Glove Word Embedding
The document describes a study that uses GloVe word embeddings to measure semantic similarity between short texts. GloVe is an unsupervised learning algorithm for obtaining vector representations of words. The study trains GloVe word embeddings on a large corpus, then uses the embeddings to encode short texts and calculate their semantic similarity, comparing the accuracy to methods that use Word2Vec embeddings. It aims to show that GloVe embeddings may provide better performance for short text semantic similarity tasks.
Text Mining for Lexicography
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Cross-domain sentiment analysis of the natural Romanian language
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Decision-Making Model for Student Assessment by Unifying Numerical and Lingui...
Learning assessment deals with the process of making a decision on the quality or performance of student achievement in a number of competency standards. In the process, teacher’s preferences are provided through both test and non-test, generally in a numeric value, from which the final results are then converted into letters or linguistic value. In the proposed model, linguistic variables are exploited as a form of teacher’s preferences in nontest techniques. Consequently, the assessment data set will consist of numerical and linguistic information, so it requires a method to unify them to obtain the final value. A model that uses the 2-tuple linguistic approach and based on matrix operations is proposed to solve the problem. This study proposed a new procedure that consists of four stages: preprocessing, transformation, aggregation and exploitation. The final result is presented in 2-tuple linguistic representation and its equivalent number, accompanied by a description of the achievement of each competency. The α value of 2-tuple linguistic in the final result and in the description of each competency becomes meaningful information that can be interpreted as a comparative ability one student has related to other students, and shows how much potential is achieved to reach higher ranks. The proposed model contributes to enrich the learning assessment techniques, since the exploitation of linguistic variable as representation preferences provides flexible space for teachers in their assessments. Moreover, using the result with respect to students’ levels of each competency, students’ mastery of each attribute can be diagnosed and their progress of learning can be estimated.
A Review Of Text Mining Techniques And Applications
This document provides a review of various text mining techniques and applications. It discusses techniques used for text classification and summarization, including Naive Bayes classification, backpropagation neural networks, keyword matching, and information extraction. It also covers applications of text mining in areas like sentiment analysis of social media posts and hotel reviews. Finally, it discusses the need for organizational text mining to extract useful information and insights from large amounts of unstructured text data.
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This paper presents a supervised machine learning
approach that uses a decision tree learning algorithm for
recognition of Bengali noun-noun compounds as multiword
expression (M WE) from Bengali corpus. Our proposed
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identifying MWEs. The proposed system is tested on a Bengali
corpus for identifying noun-noun compound MWEs from the
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Continuous bag of words cbow word2vec word embedding work .pdf
Continuous bag of words (cbow) word2vec word embedding work is that it tends to predict the
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The purpose of this question is to be able to create a word embedding for the given data set.
data set text:
In linguistics word embeddings were discussed in the research area of distributional semantics. It
aims to quantify and categorize semantic similarities between linguistic items based on their
distributional properties in large samples of language data. The underlying idea that "a word is
characterized by the company it keeps" was popularized by Firth.
The technique of representing words as vectors has roots in the 1960s with the development of
the vector space model for information retrieval. Reducing the number of dimensions using
singular value decomposition then led to the introduction of latent semantic analysis in the late
1980s.In 2000 Bengio et al. provided in a series of papers the "Neural probabilistic language
models" to reduce the high dimensionality of words representations in contexts by "learning a
distributed representation for words". (Bengio et al, 2003). Word embeddings come in two different
styles, one in which words are expressed as vectors of co-occurring words, and another in which
words are expressed as vectors of linguistic contexts in which the words occur; these different
styles are studied in (Lavelli et al, 2004). Roweis and Saul published in Science how to use
"locally linear embedding" (LLE) to discover representations of high dimensional data structures.
The area developed gradually and really took off after 2010, partly because important advances
had been made since then on the quality of vectors and the training speed of the model.
There are many branches and many research groups working on word embeddings. In 2013, a
team at Google led by Tomas Mikolov created word2vec, a word embedding toolkit which can train
vector space models faster than the previous approaches. Most new word embedding techniques
rely on a neural network architecture instead of more traditional n-gram models and unsupervised
learning.
Limitations
One of the main limitations of word embeddings (word vector space models in general) is that
possible meanings of a word are conflated into a single representation (a single vector in the
semantic space). Sense embeddings are a solution to this problem: individual meanings of words
are represented as distinct vectors in the space.
For biological sequences: BioVectors
Word embeddings for n-grams in biological sequences (e.g. DNA, RNA, and Proteins) for
bioinformatics applications have been proposed by Asgari and Mofrad. Named bio-vectors
(BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins
(amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representa.
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Journal of Computer Science Research | Vol.1, Iss.3
● A Foreword from the Editor-in-Chief
https://doi.org/10.30564/jcsr.v1i3.1466
● Discussion on Innovation of Seasoning under the Background of “Internet +”
https://doi.org/10.30564/jcsr.v1i3.1264
● Evaluating Word Similarity Measure of Embeddings Through Binary Classification
https://doi.org/10.30564/jcsr.v1i3.1268
● Performance Evaluation of Reactive Routing Protocols in MANETs in Association with TCP Newreno
https://doi.org/10.30564/jcsr.v1i3.1441
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INFT2060 Applied Artificial Intelligence.docx
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Machine Learning Techniques with Ontology for Subjective Answer Evaluation
Computerized Evaluation of English Essays is performed using Machine learning techniques like Latent
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Ontology, a concept map of domain knowledge, can enhance the performance of these techniques. Use of
Ontology makes the evaluation process holistic as presence of keywords, synonyms, the right word
combination and coverage of concepts can be checked. In this paper, the above mentioned techniques are
implemented both with and without Ontology and tested on common input data consisting of technical
answers of Computer Science. Domain Ontology of Computer Graphics is designed and developed. The
software used for implementation includes Java Programming Language and tools such as MATLAB,
Protégé, etc. Ten questions from Computer Graphics with sixty answers for each question are used for
testing. The results are analyzed and it is concluded that the results are more accurate with use of
Ontology.
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IRJET -Survey on Named Entity Recognition using Syntactic Parsing for Hindi L...
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Natural Language Processing Through Different Classes of Machine Learning
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Evaluating sentiment analysis and word embedding techniques on Brexit
In this study, we investigate the effectiveness of pre-trained word embeddings for sentiment analysis on a real-world topic, namely Brexit. We compare the performance of several popular word embedding models such global vectors for word representation (GloVe), FastText, word to vec (word2vec), and embeddings from language models (ELMo) on a dataset of tweets related to Brexit and evaluate their ability to classify the sentiment of the tweets as positive, negative, or neutral. We find that pre-trained word embeddings provide useful features for sentiment analysis and can significantly improve the performance of machine learning models. We also discuss the challenges and limitations of applying these models to complex, real-world texts such as those related to Brexit.
Cross language information retrieval in indian
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis
Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis
Sentiment analysis on Bangla conversation using machine learning approach
Sentiment analysis on Bangla conversation using machine learning approach
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NBLex: emotion prediction in Kannada-English code-switchtext using naïve baye...
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IRJET- Short-Text Semantic Similarity using Glove Word Embedding
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ppt for IASNLP.pptx
- 1. Bilingual Semantic Text Similarity MT Evaluation (Bilingual Evaluation) Submitted By Binod Kumar Mishra, Pursotam Kumar, Vaibhavi Kailash Kothadi Mentor by Ms. Ananya Mukherjee, Dr. Manish Shrivastava 11th Advanced Summer School on NLP (IASNLP-2022) IIIT Hyderabad
- 2. Contents • Introduction • Problem Statement • Data • Experiments • Result & Analysis • Conclusion & Future Work
- 3. 1. Introduction • Bilingual Semantic Text similarity [1] • Measures the equivalence of meanings between two languages. • With the bilingual distributed representation of words we try to capture semantic meaning. • It can be divided into three phases, Preprocessing, Similarity Measure, and Score estimation • It can be used in various NLP applications like Question-Answer, Machine Translation, and Information Extraction. • Word level embedding and Sentence level embedding • Word Level Embedding: Word2Vec, Glove, Fasttext, Elmo, BERT [2-8] • Sentence Level Embedding: SBERT, LaBSE, LASER [9-11] • Fine Tune COMET machine translation evaluation model[12-13] • Pearson correlation using Normalized score and other sentence embedding approaches
- 4. 2. Problem Statement • Word level embedding vs Sentence level embedding • Pre-trained COMET model vs Fine-tune COMET model • Compare various sentence encoders using Pearson Correlation
- 5. 3. Data • We have collected dataset from [14] containing 1,00,000 parallel English and translated Hindi sentences to fine-tune COMET machine translation model .
- 6. 4. Experiment Fig 1: Fine-tune COMET machine translation evaluation model
- 7. 5. Result & Analysis
- 8. 5. Result & Analysis (Cont.)
- 9. 5. Result & Analysis (Cont.)
- 10. 6. Conclusion & Future Work In this report, we focus on three important things • Embedding approach • Fine tune COMET machine translation evaluation model • Compare Correlation Pearson value between LaBSE, LASER, SBERT, and COMET. And concluded that LaBSE and SBERT give the best result. • The future work is that if we Fine-tune models with huge data then it may be possible that we get a better result.
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- 12. THANK YOU