Word sense disambiguation (WSD) is the ability to identify the meaning of words in context
in a computational manner. WSD is considered an AI-complete problem, that is, a task …

In the last decade, the problem of computational metaphor processing has garnered
immense attention from the domains of computational linguistics and cognition. A wide …

We address the task of computing vector space representations for the meaning of word
occurrences, which can vary widely according to context. This task is a crucial step towards …

One of the main obstacles to highperformance Word Sense Disambiguation (WSD) is the
knowledge acquisition bottleneck. In this paper, we present a methodology to automatically …

Sentiment analysis is a branch of natural language processing, or machine learning
methods. It becomes one of the most important sources in decision making. It can extract …

In distributional semantics studies, there is a growing attention in compositionally
determining the distributional meaning of word sequences. Yet, compositional distributional …

In this paper we propose algorithms to automatically classify sentences into metaphoric or
normal usages. Our algorithms only need the WordNet and bigram counts, and does not …

How children go about learning the general regularities that govern language, as well as
kee** track of the exceptions to them, remains one of the challenging open questions in …

We present a vector space–based model for selectional preferences that predicts plausibility
scores for argument headwords. It does not require any lexical resources (such as …

There has been a great deal of recent research into word sense disambiguation, particularly
since the inception of the Senseval evaluation exercises. Because a word often has more …