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 …

The automatic disambiguation of word senses hasbeen an interest and concern since the
earliest days of computer treatment of language in the 1950s. Sense disambiguation is an" …

The amount of digital text available for analysis by consumer researchers has risen
dramatically. Consumer discussions on the internet, product reviews, and digital archives of …

Words are associated with emotions. For example, delightful and yummy indicate the
emotion of joy, gloomy and cry are indicative of sadness, shout and boiling are indicative of …

This paper presents an unsupervised learning algorithm for sense disambiguation that,
when trained on unannotated English text, rivals the performance of supervised techniques …

TextTiling is a technique for subdividing texts into multi-paragraph units that represent
passages, or subtopics. The discourse cues for identifying major subtopic shifts are patterns …

The Handbook of Natural Language Processing, Second Edition presents practical tools
and techniques for implementing natural language processing in computer systems. Along …

This paper presents context-group discrimination, a disambiguation algorithm based on
clustering. Senses are interpreted as groups (or clusters) of similar contexts of the …

Recently, there has been a rebirth of empiricism in the field of natural language processing.
Manual encoding of linguistic information is being challenged by automated corpus-based …

Distributional approaches to meaning acquisition utilize distributional properties of linguistic
entities as the building blocks of semantics. In doing so, they rely fundamentally on a set of …