Contextual self-organizing map: software for constructing semantic representations.

Area of Science:

• Computational linguistics
• Natural Language Processing (NLP)
• Machine Learning

Background:

• Traditional word representation methods often struggle with capturing nuanced semantic relationships.
• Understanding word meaning from large text corpora is crucial for advanced NLP tasks.
• Developing algorithms for semantic representation is key to advancing computational language models.

Purpose of the Study:

• To introduce a novel software package for deriving semantic representations of words.
• To demonstrate a corpus-based algorithm leveraging word co-occurrences.
• To showcase the application of Self-Organizing Maps (SOMs) for semantic clustering.

Main Methods:

• A corpus-based algorithm analyzes word co-occurrences in a large electronic text database.
• Target words are represented by the average of preceding and succeeding words in the corpus.
• Semantic representations are processed using a Self-Organizing Map (SOM) for dimensionality reduction and visualization.

Main Results:

• The SOM projects word semantics onto a 2-D space, clustering words with similar meanings.
• Lexically meaningful categories are formed through the topographic mapping of statistical context.
• The method successfully extracts semantic representations for words in both English and Chinese.

Conclusions:

• The developed software package offers an effective approach to computational semantic representation.
• This corpus-based method, enhanced by SOMs, facilitates the analysis of word meaning and relationships.
• Applications span computational modeling of language acquisition, processing, and other NLP domains.