Bootstrapping semantics from text is one of the greatest challenges in natural language
learning. We first define a word similarity measure based on the distributional pattern of …

This paper proposes an efficient example sampling method for example-based word sense
disambiguation systems. To construct a database of practical size, a considerable overhead …

We propose a method to build thesauri on the basis of grammatical relations. The proposed
method constructs thesauri by using a hierarchical clustering algorithm. An important point in …

Manually constructing an inventory of word senses has suffered from problems including
high cost, arbitrary assignment of meaning to words, and mismatch to domains. To …

Resolution of lexical ambiguity, commonly termed``word sense disambiguation'', is expected
to improve the analytical accuracy for tasks which are sensitive to lexical semantics. Such …

This paper describes unsupervised learning algorithm for disambiguating verbal word
senses using term weight learning. In our method, collocations which characterise every …

Word co-occurrences form a graph, regarding words as nodes and co-occurrence relations
as branches. Thus, a co-occurrence graph can be constructed by co-occurrence relations in …

Terminological dissonance is created by the inherent ambiguity of natural language and
compounded by ontological specialization efforts within fields. Terminological dissonance …

This paper describes a semantic clustering method for data extracted from machine
readable dictionaries (MRDs) in order to build a terminological information retrieval system …

Text categorization can be viewed as a process of category search, in which one or more
categories for a test document are searched for by using given training documents with …