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Towards Topic-to-Question Generation     Towards Topic  Question Generation       font style='font-size:12px;'> 2015/9/15
This paper is concerned with automatic generation of all possible questions from a topic of interest. Specifically, we consider that each topic is associated with a body of texts containing useful inf...
Multiple Adjunction in Feature-Based Tree-Adjoining Grammar     Multiple Adjunction  Tree-Adjoining Grammar       font style='font-size:12px;'> 2015/9/15
In parsing with Tree Adjoining Grammar (TAG), independent derivations have been shown by Schabes and Shieber (1994) to be essential for correctly supporting syntactic analysis, semantic interpretation...
In this article we address the task of cross-lingual sentiment lexicon learning, which aims to automatically generate sentiment lexicons for the target languages with available English sentiment lexic...
Automatic Adaptation of Annotations     Automatic Adaptation  Annotations       font style='font-size:12px;'> 2015/9/15
Manually annotated corpora are indispensable resources, yet for many annotation tasks, such as the creation of treebanks, there exist multiple corpora with different and incompatible annotation guidel...
Modeling compositional meaning for sentences using empirical distributional methods has been a challenge for computational linguists. The categorical model of Clark, Coecke, and Sadrzadeh (2008) and C...
Lexicalization and Generative Power in CCG     Lexicalization  Generative Power  CCG       font style='font-size:12px;'> 2015/9/15
The weak equivalence of Combinatory Categorial Grammar (CCG) and Tree-Adjoining Grammar (TAG) is a central result of the literature on mildly context-sensitive grammar formalisms. However, the categor...
Efficient Global Learning of Entailment Graphs     Global Learning  Entailment Graphs       font style='font-size:12px;'> 2015/9/15
Entailment rules between predicates are fundamental to many semantic-inference applications. Consequently, learning such rules has been an active field of research in recent years. Methods for learnin...
In this article, we present a novel machine translation model, the Operation Sequence Model (OSM), which combines the benefits of phrase-based and N-gram-based statistical machine translation (SMT) an...
We present a statistical parsing framework for sentence-level sentiment classification in this article. Unlike previous works that use syntactic parsing results for sentiment analysis, we develop a st...
Training speed and accuracy are two major concerns of large-scale natural language processing systems. Typically, we need to make a tradeoff between speed and accuracy. It is trivial to improve the tr...
Automatically identifying the sentiment polarity of words is a very important task that has been used as the essential building block of many natural language processing systems such as text classific...
We describe a probabilistic framework for acquiring selectional preferences of linguistic predicates and for using the acquired representations to model the effects of context on word meaning. Our fra...
As in many natural language processing tasks, data-driven models based on supervised learning have become the method of choice for semantic role labeling. These models are guaranteed to perform well w...
Pushdown Automata in Statistical Machine Translation     Pushdown Automata  Statistical Machine Translation       font style='font-size:12px;'> 2015/9/14
This article describes the use of pushdown automata (PDA) in the context of statistical machine translation and alignment under a synchronous context-free grammar. We use PDAs to compactly represent t...
This paper explores lexicographic semirings and their application to problems in speech and language processing. Specifically, we present two instantiations of binary lexicographic semirings, one invo...

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