Answer extraction and passage retrieval forWaheeb Ahmed
—Question Answering systems (QASs) do the task of
retrieving text portions from a collection of documents that
contain the answer to the user’s questions. These QASs use a
variety of linguistic tools that be able to deal with small
fragments of text. Therefore, to retrieve the documents which
contains the answer from a large document collections, QASs
employ Information Retrieval (IR) techniques to minimize the
number of documents collections to a treatable amount of
relevant text. In this paper, we propose a model for passage
retrieval model that do this task with a better performance for
the purpose of Arabic QASs. We first segment each the top five
ranked documents returned by the IR module into passages.
Then, we compute the similarity score between the user’s
question terms and each passage. The top five passages (with
high similarity score) are retrieved are retrieved. Finally,
Answer Extraction techniques are applied to extract the final
answer. Our method achieved an average for precision of
87.25%, Recall of 86.2% and F1-measure of 87%.
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
Mining sequential patterns for interval basedijcsa
Sequential pattern mining finds the frequent subsequences or patterns from the given sequences.
TPrefixSpan algorithm finds the relevant frequent patterns from the given sequential patterns formed using
interval based events. In our proposed work, we add multiple constraints like item, length and aggregate to
the interval based TPrefixSpan algorithm. By adding these constraints the efficiency and effectiveness of
the algorithm improves. The proposed constraint based algorithm CTPrefixSpan has been applied to
synthetic medical dataset. The algorithm can be applied for stock market analysis, DNA sequences analysis
etc.
KEYWORDS
Sequential patterns, temporal patterns, Constraints, Interval based events.
Deep Learning Enabled Question Answering System to Automate Corporate HelpdeskSaurabh Saxena
Studied feasibility of applying state-of-the-art deep learning models like end-to-end memory networks and neural attention- based models to the problem of machine comprehension and subsequent question answering in corporate settings with huge
amount of unstructured textual data. Used pre-trained embeddings like word2vec and GLove to avoid huge training costs.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Answer extraction and passage retrieval forWaheeb Ahmed
—Question Answering systems (QASs) do the task of
retrieving text portions from a collection of documents that
contain the answer to the user’s questions. These QASs use a
variety of linguistic tools that be able to deal with small
fragments of text. Therefore, to retrieve the documents which
contains the answer from a large document collections, QASs
employ Information Retrieval (IR) techniques to minimize the
number of documents collections to a treatable amount of
relevant text. In this paper, we propose a model for passage
retrieval model that do this task with a better performance for
the purpose of Arabic QASs. We first segment each the top five
ranked documents returned by the IR module into passages.
Then, we compute the similarity score between the user’s
question terms and each passage. The top five passages (with
high similarity score) are retrieved are retrieved. Finally,
Answer Extraction techniques are applied to extract the final
answer. Our method achieved an average for precision of
87.25%, Recall of 86.2% and F1-measure of 87%.
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
Mining sequential patterns for interval basedijcsa
Sequential pattern mining finds the frequent subsequences or patterns from the given sequences.
TPrefixSpan algorithm finds the relevant frequent patterns from the given sequential patterns formed using
interval based events. In our proposed work, we add multiple constraints like item, length and aggregate to
the interval based TPrefixSpan algorithm. By adding these constraints the efficiency and effectiveness of
the algorithm improves. The proposed constraint based algorithm CTPrefixSpan has been applied to
synthetic medical dataset. The algorithm can be applied for stock market analysis, DNA sequences analysis
etc.
KEYWORDS
Sequential patterns, temporal patterns, Constraints, Interval based events.
Deep Learning Enabled Question Answering System to Automate Corporate HelpdeskSaurabh Saxena
Studied feasibility of applying state-of-the-art deep learning models like end-to-end memory networks and neural attention- based models to the problem of machine comprehension and subsequent question answering in corporate settings with huge
amount of unstructured textual data. Used pre-trained embeddings like word2vec and GLove to avoid huge training costs.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Full-Text Retrieval in Unstructured P2P Networks using Bloom Cast Efficientlyijsrd.com
Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P systems to effectively locate items with guaranteed recall. Second, existing schemes to improve search success rate often rely on replicating a large number of item replicas across the wide area network, incurring a large amount of communication and storage costs. In this paper, we propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. By leveraging a hybrid P2P protocol, BloomCast replicates the items uniformly at random across the P2P networks, achieving a guaranteed recall at a communication cost of O (N), where N is the size of the network. Furthermore, by casting Bloom Filters instead of the raw documents across the network, BloomCast significantly reduces the communication and storage costs for replication. Results show that BloomCast achieves an average query recall, which outperforms the existing WP algorithm by 18 percent, while BloomCast greatly reduces the search latency for query processing by 57 percent.
Proceedings of the 50th Hawaii International Conference on System Sciences | 2017
Discovering Malware with Time Series Shapelets
Om P. Patri
University of Southern California
Los Angeles, CA 90089
patri@usc.edu
Email Classification - Why Should it Matter to You?Sherpa Software
In this white paper, learn the basics of email classification, what it is, why it could assist your overall email management strategy and learn how to accomplish it.
Download Free Trial - http://bit.ly/vrIxKv
Get a Quick Quote - http://bit.ly/tw8pi3
Contact Us Now - http://bit.ly/sz9x5r
Cost-effective Interactive Attention Learning with Neural Attention ProcessMLAI2
We propose a novel interactive learning framework which we refer to as Interactive Attention Learning (IAL), in which the human supervisors interactively manipulate the allocated attentions, to correct the model's behavior by updating the attention-generating network. However, such a model is prone to overfitting due to scarcity of human annotations, and requires costly retraining. Moreover, it is almost infeasible for the human annotators to examine attentions on tons of instances and features. We tackle these challenges by proposing a sample-efficient attention mechanism and a cost-effective reranking algorithm for instances and features. First, we propose Neural Attention Process (NAP), which is an attention generator that can update its behavior by incorporating new attention-level supervisions without any retraining. Secondly, we propose an algorithm which prioritizes the instances and the features by their negative impacts, such that the model can yield large improvements with minimal human feedback. We validate IAL on various time-series datasets from multiple domains (healthcare, real-estate, and computer vision) on which it significantly outperforms baselines with conventional attention mechanisms, or without cost-effective reranking, with substantially less retraining and human-model interaction cost.
A Deep Analysis on Prevailing Spam Mail Filteration Machine Learning Approachesijtsrd
In this work, we have reviewed the issue of spam mail which is a big problem in the area of Internet. The growing size of uncalled mass e mail or spam has produced the requirement of a dependable anti spam filter. Now a days the Machine learning ML proedures are being employed to spontaneously filter the spam e mail in an effective manner. In this work, we have reviewed some of the prevalent ML approaches such as Rough sets, Bayesian classification, SVMs, k NN, ANNs and Artificial immune system and of their use fullness in the issue of spam Email taxonomy. We have provided the depictions of the procedures and the divergence of their enactment on the basis of the quantity of Spam Assassin. Anu | Ms. Preeti "A Deep Analysis on Prevailing Spam Mail Filteration Machine Learning Approaches" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33261.pdf Paper Url: https://www.ijtsrd.com/computer-science/data-processing/33261/a-deep-analysis-on-prevailing-spam-mail-filteration-machine-learning-approaches/anu
Mahout is an open source machine learning java library from Apache Software Foundation, and therefore platform independent, that provides a fertile framework and collection of patterns and ready-made component for testing and deploying new large-scale algorithms.
With these slides we aims at providing a deeper understanding of its architecture.
Keyphrase Extraction using Neighborhood KnowledgeIJMTST Journal
This paper focus on keyphrase extraction for news articles because news article is one of the popular document genres on the web and most news articles have no author-assigned keyphrases. Existing methods for single document keyphrase extraction usually make use of only the information contained in the specified document. This paper proposes to use a small number of nearest neighbor documents to provide more knowledge to improve single document keyphrase extraction. Experimental results demonstrate the good effectiveness and robustness of our proposed approach. According to experiments conducted on several documents and keyphrases we consider top 10 keyphrases are most suitable keyphrases.
Full-Text Retrieval in Unstructured P2P Networks using Bloom Cast Efficientlyijsrd.com
Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P systems to effectively locate items with guaranteed recall. Second, existing schemes to improve search success rate often rely on replicating a large number of item replicas across the wide area network, incurring a large amount of communication and storage costs. In this paper, we propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. By leveraging a hybrid P2P protocol, BloomCast replicates the items uniformly at random across the P2P networks, achieving a guaranteed recall at a communication cost of O (N), where N is the size of the network. Furthermore, by casting Bloom Filters instead of the raw documents across the network, BloomCast significantly reduces the communication and storage costs for replication. Results show that BloomCast achieves an average query recall, which outperforms the existing WP algorithm by 18 percent, while BloomCast greatly reduces the search latency for query processing by 57 percent.
Proceedings of the 50th Hawaii International Conference on System Sciences | 2017
Discovering Malware with Time Series Shapelets
Om P. Patri
University of Southern California
Los Angeles, CA 90089
patri@usc.edu
Email Classification - Why Should it Matter to You?Sherpa Software
In this white paper, learn the basics of email classification, what it is, why it could assist your overall email management strategy and learn how to accomplish it.
Download Free Trial - http://bit.ly/vrIxKv
Get a Quick Quote - http://bit.ly/tw8pi3
Contact Us Now - http://bit.ly/sz9x5r
Cost-effective Interactive Attention Learning with Neural Attention ProcessMLAI2
We propose a novel interactive learning framework which we refer to as Interactive Attention Learning (IAL), in which the human supervisors interactively manipulate the allocated attentions, to correct the model's behavior by updating the attention-generating network. However, such a model is prone to overfitting due to scarcity of human annotations, and requires costly retraining. Moreover, it is almost infeasible for the human annotators to examine attentions on tons of instances and features. We tackle these challenges by proposing a sample-efficient attention mechanism and a cost-effective reranking algorithm for instances and features. First, we propose Neural Attention Process (NAP), which is an attention generator that can update its behavior by incorporating new attention-level supervisions without any retraining. Secondly, we propose an algorithm which prioritizes the instances and the features by their negative impacts, such that the model can yield large improvements with minimal human feedback. We validate IAL on various time-series datasets from multiple domains (healthcare, real-estate, and computer vision) on which it significantly outperforms baselines with conventional attention mechanisms, or without cost-effective reranking, with substantially less retraining and human-model interaction cost.
A Deep Analysis on Prevailing Spam Mail Filteration Machine Learning Approachesijtsrd
In this work, we have reviewed the issue of spam mail which is a big problem in the area of Internet. The growing size of uncalled mass e mail or spam has produced the requirement of a dependable anti spam filter. Now a days the Machine learning ML proedures are being employed to spontaneously filter the spam e mail in an effective manner. In this work, we have reviewed some of the prevalent ML approaches such as Rough sets, Bayesian classification, SVMs, k NN, ANNs and Artificial immune system and of their use fullness in the issue of spam Email taxonomy. We have provided the depictions of the procedures and the divergence of their enactment on the basis of the quantity of Spam Assassin. Anu | Ms. Preeti "A Deep Analysis on Prevailing Spam Mail Filteration Machine Learning Approaches" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33261.pdf Paper Url: https://www.ijtsrd.com/computer-science/data-processing/33261/a-deep-analysis-on-prevailing-spam-mail-filteration-machine-learning-approaches/anu
Mahout is an open source machine learning java library from Apache Software Foundation, and therefore platform independent, that provides a fertile framework and collection of patterns and ready-made component for testing and deploying new large-scale algorithms.
With these slides we aims at providing a deeper understanding of its architecture.
Keyphrase Extraction using Neighborhood KnowledgeIJMTST Journal
This paper focus on keyphrase extraction for news articles because news article is one of the popular document genres on the web and most news articles have no author-assigned keyphrases. Existing methods for single document keyphrase extraction usually make use of only the information contained in the specified document. This paper proposes to use a small number of nearest neighbor documents to provide more knowledge to improve single document keyphrase extraction. Experimental results demonstrate the good effectiveness and robustness of our proposed approach. According to experiments conducted on several documents and keyphrases we consider top 10 keyphrases are most suitable keyphrases.
Novel Class Detection Using RBF SVM Kernel from Feature Evolving Data Streamsirjes
In the data mining field the classification of data stream creates many problems. The challenges
faces in the data stream are infinite length, concept drift, concept evaluation and feature evolution. Most of the
existing system focuses on the only first two challenges. We propose a framework in which each classifier is
prepared with the novel class detector for addressing the two challenges concept drift and concept evaluation
and for addressing the feature evolution feature set homogeneous technique is proposed. We improved the
novel class detection module by building it more adaptive to evolving the stream. SVM based feature extraction
for RBF kernel method is also proposed for detecting the novel class from the steaming data. By using the
concept of permutation and combination RBF kernel extracts the features and find out the relation between
them. This improves the novel class detect technique and provide more accuracy for classifying the data
Text mining efforts to innovate new, previous unknown or hidden data by automatically extracting
collection of information from various written resources. Applying knowledge detection method to
formless text is known as Knowledge Discovery in Text or Text data mining and also called Text Mining.
Most of the techniques used in Text Mining are found on the statistical study of a term either word or
phrase. There are different algorithms in Text mining are used in the previous method. For example
Single-Link Algorithm and Self-Organizing Mapping(SOM) is introduces an approach for visualizing
high-dimensional data and a very useful tool for processing textual data based on Projection method.
Genetic and Sequential algorithms are provide the capability for multiscale representation of datasets and
fast to compute with less CPU time based on the Isolet Reduces subsets in Unsupervised Feature
Selection. We are going to propose the Vector Space Model and Concept based analysis algorithm it will
improve the text clustering quality and a better text clustering result may achieve. We think it is a good
behavior of the proposed algorithm is in terms of toughness and constancy with respect to the formation of
Neural Network.
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.
Class Diagram Extraction from Textual Requirements Using NLP Techniquesiosrjce
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
T EXT M INING AND C LASSIFICATION OF P RODUCT R EVIEWS U SING S TRUCTURED S U...csandit
Text mining and Text classification are the two pro
minent and challenging tasks in the field of
Machine learning. Text mining refers to the process
of deriving high quality and relevant
information from text, while Text classification de
als with the categorization of text documents
into different classes. The real challenge in these
areas is to address the problems like handling
large text corpora, similarity of words in text doc
uments, and association of text documents with
a subset of class categories. The feature extractio
n and classification of such text documents
require an efficient machine learning algorithm whi
ch performs automatic text classification.
This paper describes the classification of product
review documents as a multi-label
classification scenario and addresses the problem u
sing Structured Support Vector Machine.
The work also explains the flexibility and performan
ce of the proposed approach for e
fficient text classification.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
1. Transductive Support Vector Classification for RNA Related Biological Abstracts Blake Adams Graduate Student Department of Computer Science Advisor: Dr. Muhammad A. Rahman
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26. Mapping a Feature Tokenized Word Discard Is it in the Term Map? Is it in the keyword list? Is word in current document? No Yes No Assign id, docID, set termFreq to 1 Yes Is it in the TermDocFreqMap? Do Nothing No Yes Assign featureId, set docFreq to 1, assign lastDocId Increment termFreq Yes No Increment docFreq Is word in current document? Yes No Increment docFreq
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