N-gram Features
N-Gram Features is a type of text feature representation in natural language processing that captures contiguous sequences of n items—such as characters, syllables, words, or tokens—from a given corpus, distinguishing itself by its reliance on local sequential order and fixed-length context windows to model lexical and syntactic patterns.
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Authors
402
Institutions
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2017–2026
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391
A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval
Yelong Shen, Xiaodong He, Jianfeng Gao et al. · 2014 · 687 citations
Sentiment classification on customer feedback data
Michael Gamon · 2004 · 437 citations · Full text
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Google (United States)
Mountain View, United States
10 h-index
20 publications
1.9K citations

University of Alberta
Edmonton, Canada
8 h-index
18 publications
715 citations
University of Washington
Seattle, United States
7 h-index
20 publications
774 citations
University of Sheffield
Sheffield, United Kingdom
6 h-index
11 publications
812 citations

University of Tübingen
Tübingen, Germany
6 h-index
15 publications
523 citations
Massachusetts Institute of Technology
Cambridge, United States
6 h-index
14 publications
1.9K citations
Carnegie Mellon University
Pittsburgh, United States
6 h-index
11 publications
355 citations

Shanghai Jiao Tong University
Shanghai, China
5 h-index
12 publications
859 citations
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Repository · 14 publications · 703 citations
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10 publications · 476 citations
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Conference · 6 publications · 113 citations
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5 publications · 593 citations
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4 publications · 92 citations
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