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Most probabilistic classifiers used for word-sense disam- biguation have either been based on only one contextual feature or have used a model that is simply assumed to characterize the interdependencies …...

This paper presents a statistical model which trains from a corpus annotated with Part-Of- Speech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be …...

This study addresses the problem of au- tomatically detecting decisions in conver- sational speech. We formulate the prob- lem as classifying decision-making units at two levels of granularity: dialogue acts …...

Many acoustic approaches to prosodic la- beling in English have employed only lo- cal classifiers, although text-based classifi- cation has employed some sequential mod- els. In this paper we employ …...

English pronouns like he and they reliably re- flect the gender and number of the entities to which they refer. Pronoun resolution systems can use this fact to filter noun …...

We use a machine learner trained on a combination of acoustic and contextual features to predict the accuracy of incom- ing n-best automatic speech recognition (ASR) hypotheses to a spoken …...

The informal nature of social media text renders it very difficult to be automati- cally processed by natural language pro- cessing tools. Text normalization, which corresponds to restoring the non-standard …...

We present a novel Undirected Machine Translation model of Hierarchical MT that is not constrained to the standard bottom- up inference order. Removing the order- ing constraint makes it possible …...

The problem of accurately predicting rela- tive reading difficulty across a set of sen- tences arises in a number of important natu- ral language applications, such as finding and curating …...

While there is increasing interest in automat- ically recognizing the argumentative structure of a text, recognizing the argumentative pur- pose of revisions to such texts has been less explored. Furthermore, …...

AbstractTextbooks are rich sources of information. Harvesting structured knowledge from textbooks is a key challenge in many educational applications. As a case study, we present an approach for harvesting structured …...

AbstractModeling U.S. Congressional legislation and roll-call votes has received significant attention in previous literature, and while legislators across 50 state governments and D.C. propose over 100,000 bills each year, enacting …...

AbstractWe present 1) a work in progress method to visually segment key regions of scientific articles using an object detection technique augmented with contextual features, and 2) a novel dataset …...

AbstractAutomatic sarcasm detection from text is an important classification task that can help identify the actual sentiment in user-generated data, such as reviews or tweets. Despite its usefulness, sarcasm detection …...

AbstractThis paper revisits feature engineering approaches for predicting the complexity level of English words in a particular context using regression techniques. Our best submission to the Lexical Complexity Prediction (LCP) …...

AbstractNowadays, there are a lot of advertisements hiding as normal posts or experience sharing in social media. There is little research of advertorial detection on Mandarin Chinese texts. This paper …...

AbstractNeural text generation models are likely to suffer from the low-diversity problem. Various decoding strategies and training-based methods have been proposed to promote diversity only by exploiting contextual features, but …...

This paper provides a corpus-based, statistical analysis to explore the semantic (dis)similarities of four metaphorical meanings of the Chinese color term hēi ‘black’ regarding its predicative usages. With the Behavioral …...