Expressing Implicit Semantic Relations without Supervision

TL;DR

This paper introduces an unsupervised algorithm that extracts and ranks patterns from large text corpora to identify implicit semantic relations between words, aiding lexicon and ontology construction.

Contribution

It presents a novel unsupervised method for mining and ranking patterns that express semantic relations without supervision, achieving state-of-the-art results.

Findings

Achieved state-of-the-art performance on SAT analogy questions

Outperformed tf-idf based pattern ranking algorithms

Effectively classifies semantic relations in noun-modifier pairs

Abstract

We present an unsupervised learning algorithm that mines large text corpora for patterns that express implicit semantic relations. For a given input word pair X:Y with some unspecified semantic relations, the corresponding output list of patterns <P1,...,Pm> is ranked according to how well each pattern Pi expresses the relations between X and Y. For example, given X=ostrich and Y=bird, the two highest ranking output patterns are "X is the largest Y" and "Y such as the X". The output patterns are intended to be useful for finding further pairs with the same relations, to support the construction of lexicons, ontologies, and semantic networks. The patterns are sorted by pertinence, where the pertinence of a pattern Pi for a word pair X:Y is the expected relational similarity between the given pair and typical pairs for Pi. The algorithm is empirically evaluated on two tasks, solving multiple-choice SAT word analogy questions and classifying semantic relations in noun-modifier pairs. On both tasks, the algorithm achieves state-of-the-art results, performing significantly better than several alternative pattern ranking algorithms, based on tf-idf.