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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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Interspeech 2010, 2010
This paper describes an extension of the n-gram language model: the similar n-gram language model. The estimation of the probability P(s) of a string s by the classical model of order n is computed using statistics of occurrences of the last n words of the string in the corpus, whereas the proposed model further uses all the strings s' for which the ...
Gillot, Christian +3 more
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This paper describes an extension of the n-gram language model: the similar n-gram language model. The estimation of the probability P(s) of a string s by the classical model of order n is computed using statistics of occurrences of the last n words of the string in the corpus, whereas the proposed model further uses all the strings s' for which the ...
Gillot, Christian +3 more
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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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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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Pipilika N-Gram Viewer: An Efficient Large Scale N-Gram Model for Bengali
2018 International Conference on Bangla Speech and Language Processing (ICBSLP), 2018In this paper, we introduce a large-scale Bengali N-gram model, trained on online newspaper corpus and present results and analysis of two different experiments done by using the model, namely Context-aware spell checker and Trending topic detection. We also present the process with emphasis on the problems that arise in working with data at this scale.
Adnan Ahmad +3 more
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2014
A statistical language model defines a probability distribution over a set of symbol sequences from some finite inventory. An especially simple yet very powerful concept for the formal description of statistical language models is formed by their representation using Markov chains or so-called n-gram models.
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A statistical language model defines a probability distribution over a set of symbol sequences from some finite inventory. An especially simple yet very powerful concept for the formal description of statistical language models is formed by their representation using Markov chains or so-called n-gram models.
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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.
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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.
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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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ACM SIGIR Forum, 2011
The Web N-gram Workshop was held on July 23, 2010 in Geneva, Switzerland, in conjunction with the 33rd Annual ACM SIGIR Conference. The workshop brought together leaders in information retrieval and language modeling to discuss the challenges in information retrieval and how language modeling approaches may help address some of these challenges, with a
Chengxiang Zhai +4 more
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The Web N-gram Workshop was held on July 23, 2010 in Geneva, Switzerland, in conjunction with the 33rd Annual ACM SIGIR Conference. The workshop brought together leaders in information retrieval and language modeling to discuss the challenges in information retrieval and how language modeling approaches may help address some of these challenges, with a
Chengxiang Zhai +4 more
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Discriminative n-gram language modeling
Computer Speech & Language, 2007This paper describes discriminative language modeling for a large vocabulary speech recognition task. We contrast two parameter estimation methods: the perceptron algorithm, and a method based on maximizing the regularized conditional log-likelihood. The models are encoded as deterministic weighted finite state automata, and are applied by intersecting
Brian Roark +2 more
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