Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning

TL;DR

This paper introduces a graph-based semi-supervised learning approach that automatically expands morpho-syntactic lexicons across multiple languages, significantly improving downstream NLP tasks.

Contribution

The authors propose a language-independent method that enlarges small seed lexicons into extensive, high-quality resources using word relations, enhancing NLP performance.

Findings

Expanded a 1000-word seed lexicon to over 100,000 words in 11 languages

Automatically generated lexicons improve morphological tagging accuracy

Lexicons enhance dependency parsing performance

Abstract

Morpho-syntactic lexicons provide information about the morphological and syntactic roles of words in a language. Such lexicons are not available for all languages and even when available, their coverage can be limited. We present a graph-based semi-supervised learning method that uses the morphological, syntactic and semantic relations between words to automatically construct wide coverage lexicons from small seed sets. Our method is language-independent, and we show that we can expand a 1000 word seed lexicon to more than 100 times its size with high quality for 11 languages. In addition, the automatically created lexicons provide features that improve performance in two downstream tasks: morphological tagging and dependency parsing.