In the last few years, a number of NLP researchers have developed and participated in the
task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language …

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural
language expressions that convey almost the same information. Textual entailment …

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 …

Distributional word similarity is most commonly perceived as a symmetric relation. Yet,
directional relations are abundant in lexical semantics and in many Natural Language …

Although many algorithms have been developed to harvest lexical resources, few organize
the mined terms into taxonomies. We propose (1) a semi-supervised algorithm that uses a …

Most work on unsupervised entailment rule acquisition focused on rules between templates
with two variables, ignoring unary rules-entailment rules between templates with a single …

This paper describes ongoing work on distributional models for word meaning in context.
We abandon the usual one-vectorper-word paradigm in favor of an exemplar model that …

Vector space models of word meaning typically represent the meaning of a word as a vector
computed by summing over all its corpus occurrences. Words close to this point in space …

Semantic inference is an important component in many natural language understanding
applications. Classical approaches to semantic inference rely on complex logical …

Many NLP tasks need accurate knowledge for semantic inference. To this end, mostly
WordNet is utilized. Yet Word-Net is limited, especially for inference between predicates. To …