Results 251 to 260 of about 14,339,869 (296)
Heterogeneity of Critical Organ Dysfunction During Late-Onset Bacteremia in Preterm Infants.
JAMA Netw OpenLiu S +9 more
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
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
openaire +2 more sources
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
openaire +2 more sources
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
openaire +2 more sources
ACM Transactions on Information Systems, 2017
Inverse Document Frequency (IDF) is widely accepted term weighting scheme whose robustness is supported by many theoretical justifications. However, applying IDF to word N-grams (or simply N-grams) of any length without relying on heuristics has remained a challenging issue. This article describes a theoretical extension of IDF to handle N-grams. First,
Masumi Shirakawa +2 more
openaire +1 more source
Inverse Document Frequency (IDF) is widely accepted term weighting scheme whose robustness is supported by many theoretical justifications. However, applying IDF to word N-grams (or simply N-grams) of any length without relying on heuristics has remained a challenging issue. This article describes a theoretical extension of IDF to handle N-grams. First,
Masumi Shirakawa +2 more
openaire +1 more source
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
openaire +1 more source
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.
openaire +1 more source
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.
openaire +1 more source
Polish N-Grams and Their Correction Process
2010 4th International Conference on Multimedia and Ubiquitous Engineering, 2010Word n-gram statistics collected from over 1 300 000 000 words are presented. Eventhough they were collected from various good sources, they contain several types of errors. The paper focuses on the process of partly supervised correction of the n- grams. Types of errors are described as well as our software allowing efficient and fast corrections.
Bartosz Ziólko +2 more
openaire +2 more sources
Growing an n-gram language model
Interspeech 2005, 2005Traditionally, when building an n-gram model, we decide the span of the model history, collect the relevant statistics and estimate the model. The model can be pruned down to a smaller size by manipulating the statistics or the estimated model. This paper shows how an n-gram model can be built by adding suitable sets of n-grams to a unigram model until
Vesa Siivola, Bryan L. Pellom
openaire +2 more sources
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
openaire +2 more sources