N semantic classes are harder than two

Abstract

We show that we can automatically classify semantically related phrases into 10 classes. Classification robustness is improved by training with multiple sources of evidence, including within-document cooccurrence, HTML markup, syntactic relationships in sentences, substitutability in query logs, and string similarity. Our work provides a benchmark for automatic n-way classification into WordNet's semantic classes, both on a TREC news corpus and on a corpus of substitutable search query phrases.

Inferring semantic query relations from collective user behavior

“…There have been studies leveraging external sources like WordNet [7] and Wikipedia [3] to determine semantic relationships among queries. [11] and [12] use the temporal frequency information of queries to infer semantic relationships among them.…”