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Syntactic n-grams in Computational Linguistics, 2019
In this and the following chapters, we present two ideas related to the non-linear construction of n-grams. Recall that the non-linear construction consists in taking the elements which form n-grams in a different order than the surface (textual) representation, i.e., in a different way than words (lemmas, POS tags, etc.) appear in a text.
G. Sidorov
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In this and the following chapters, we present two ideas related to the non-linear construction of n-grams. Recall that the non-linear construction consists in taking the elements which form n-grams in a different order than the surface (textual) representation, i.e., in a different way than words (lemmas, POS tags, etc.) appear in a text.
G. Sidorov
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Document embeddings learned on various types of n-grams for cross-topic authorship attribution
Computing (Vienna/New York), 2018Juan Pablo Francisco Posadas-Durán +2 more
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Clustering news articles using efficient similarity measure and N-grams
International Journal of Knowledge Engineering and Data Mining, 2018Desmond Bala Bisandu +2 more
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Senti-N-Gram: An n-gram lexicon for sentiment analysis
Expert Systems with Applications, 2018Abstract Sentiment analysis helps evaluating the performance of products or services from user generated contents. Lexicon based sentiment analysis approaches are preferred over learning based ones when training data is not adequate. Existing lexicons contain only unigrams along with their sentiment scores.
Atanu Dey +2 more
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Future generations computer systems, 2020
Ransomware is a special kind of malware, which leads to irreversible data losses and incurs enormous economic costs. It is an urgent task to detect ransomware nowadays.
Bin Zhang +5 more
semanticscholar +1 more source
Ransomware is a special kind of malware, which leads to irreversible data losses and incurs enormous economic costs. It is an urgent task to detect ransomware nowadays.
Bin Zhang +5 more
semanticscholar +1 more source
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, 2003
Stemming can improve retrieval accuracy, but stemmers are language-specific. Character n-gram tokenization achieves many of the benefits of stemming in a language independent way, but its use incurs a performance penalty. We demonstrate that selection of a single n-gram as a pseudo-stem for a word can be an effective and efficient language-neutral ...
James Mayfield, Paul McNamee
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Stemming can improve retrieval accuracy, but stemmers are language-specific. Character n-gram tokenization achieves many of the benefits of stemming in a language independent way, but its use incurs a performance penalty. We demonstrate that selection of a single n-gram as a pseudo-stem for a word can be an effective and efficient language-neutral ...
James Mayfield, Paul McNamee
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An Adversarial Machine Learning Method Based on OpCode N-grams Feature in Malware Detection
International Conference on Data Science in Cyberspace, 2020Machine learning has become an important method in malware detection. However, due to the weakness of machine learning models, a large number of researches related to adversarial machine learning has emerged.
Xiang Li +3 more
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Relative N-gram signatures: Document visualization at the level of character N-grams
2012 IEEE Conference on Visual Analytics Science and Technology (VAST), 2012The Common N-Gram (CNG) classifier is a text classification algorithm based on the comparison of frequencies of character n-grams (strings of characters of length n) that are the most common in the considered documents and classes of documents. We present a text analytic visualization system that employs the CNG approach for text classification and ...
Magdalena Jankowska +2 more
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Proceedings of the 25th International Conference on World Wide Web, 2016
Our proposal, $N$-gram over Context (NOC), is a nonparametric topic model that aims to help our understanding of a given corpus, and be applied to many text mining applications. Like other topic models, NOC represents each document as a mixture of topics and generates each word from one topic.
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Our proposal, $N$-gram over Context (NOC), is a nonparametric topic model that aims to help our understanding of a given corpus, and be applied to many text mining applications. Like other topic models, NOC represents each document as a mixture of topics and generates each word from one topic.
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Chemical Biology and Drug Design, 2019
Physicochemical n‐Grams Tool (PnGT) is an open‐source standalone software for calculating physicochemical descriptors of protein. PnGT was developed using the Python scripting language and developed the user interface using Tkinter.
Shubham Vishnoi +2 more
semanticscholar +1 more source
Physicochemical n‐Grams Tool (PnGT) is an open‐source standalone software for calculating physicochemical descriptors of protein. PnGT was developed using the Python scripting language and developed the user interface using Tkinter.
Shubham Vishnoi +2 more
semanticscholar +1 more source