Combining Lexical and Syntactic Features for Detecting Content-Dense Texts in News
نویسندگان
چکیده
منابع مشابه
Combining Lexical and Syntactic Features for Detecting Content-dense Texts in News
Content-dense news report important factual information about an event in direct, succinct manner. Information seeking applications such as information extraction, question answering and summarization normally assume all text they deal with is content-dense. Here we empirically test this assumption on news articles from the business, U.S. international relations, sports and science journalism d...
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We introduce the task of identifying information-dense texts, which report important factual information in direct, succinct manner. We describe a procedure that allows us to label automatically a large training corpus of New York Times texts. We train a classifier based on lexical, discourse and unlexicalized syntactic features and test its performance on a set of manually annotated articles f...
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The success of supervised learning approaches to word sense disambiguation is largely dependent on the features used to represent the context in which an ambiguous word occurs. Previous work has reached mixed conclusions; some suggest that combinations of syntactic and lexical features will perform most effectively. However, others have shown that simple lexical features perform well on their o...
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This work evaluates a system that uses interpolated predictions of reading difficulty that are based on both vocabulary and grammatical features. The combined approach is compared to individual grammarand language modeling-based approaches. While the vocabulary-based language modeling approach outperformed the grammar-based approach, grammar-based predictions can be combined using confidence sc...
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ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2017
ISSN: 1076-9757
DOI: 10.1613/jair.5418