Adapting predominant and novel sense discovery algorithms for identifying corpus-specific sense differences
This work addresses the challenge of dynamic word senses for natural language processing systems, but it is incremental as it adapts existing methods without introducing new paradigms.
The paper tackled the problem of identifying corpus-specific word senses by adapting three existing algorithms to analyze digitized books and newspaper archives, resulting in 45-60% of identified senses being judged as genuine in human evaluations.
Word senses are not static and may have temporal, spatial or corpus-specific scopes. Identifying such scopes might benefit the existing WSD systems largely. In this paper, while studying corpus specific word senses, we adapt three existing predominant and novel-sense discovery algorithms to identify these corpus-specific senses. We make use of text data available in the form of millions of digitized books and newspaper archives as two different sources of corpora and propose automated methods to identify corpus-specific word senses at various time points. We conduct an extensive and thorough human judgment experiment to rigorously evaluate and compare the performance of these approaches. Post adaptation, the output of the three algorithms are in the same format and the accuracy results are also comparable, with roughly 45-60% of the reported corpus-specific senses being judged as genuine.