Semantic Similarity and Relatedness as Scaffolding for Natural Language Processing
Channel: Ted Pedersen
Date: 2018-06-07

A talk I gave at the University of Colorado, Information Science Department, on Feb 22, 2018.  Also gave modified versions of this at UNCC in April and Thomson Reuters (Eagan, MN) in June.  Here's the abstract of the talk : 

Measuring semantic similarity and relatedness has become a standard way to evaluate new methods of representing words and their senses. Most recently word2vec and other word embedding algorithms have been shown to perform well in relation to human judgments. There is a long history of techniques based on co-occurrences, dictionary definitions, and the structure of ontologies that have also had their day in the sun and continue to be useful. While we have the means to measure similarity and relatedness to near human levels of performance, the ends are not terribly clear. What does the availability of all these different techniques that perform relatively well actually enable us to do? In this talk I’ll review some of these methods of measuring similarity and relatedness, and will show how they can be used to carry out a variety of NLP tasks including word sense disambiguation, hypernym identification, and ontology enhancement. I’ll close with some thoughts on the role these measures can play in solving the larger problem of automatically constructing dictionaries.