[Paper Review] Expressing Implicit Semantic Relations without Supervision

Peter D. Turney|ArXiv.org|Jul 27, 2006

Natural Language Processing Techniques16 references4 citations

TL;DR

This paper presents an unsupervised algorithm that identifies and ranks natural language patterns expressing implicit semantic relations between word pairs, such as 'X is the largest Y' for 'ostrich : bird'. By measuring pattern pertinence through relational similarity, the method achieves state-of-the-art performance on SAT analogy and noun-modifier relation classification tasks, outperforming tf-idf-based baselines without requiring labeled supervision.

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 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.

Motivation & Objective

• To develop a method for discovering implicit semantic relations between word pairs without relying on labeled training data.
• To identify natural language patterns that express these relations in a way that supports lexicon and ontology construction.
• To rank patterns by their pertinence to a given word pair, reflecting how well they capture the underlying semantic relation.
• To evaluate the method on real-world NLP tasks such as word analogy and semantic relation classification.
• To demonstrate that unsupervised pattern ranking can outperform traditional supervised or tf-idf-based approaches.

Proposed method

• The algorithm mines large text corpora to extract candidate patterns involving a given word pair X:Y.
• It computes the pertinence of each pattern Pi as the expected relational similarity between X:Y and typical word pairs associated with Pi.
• Pertinence is estimated using co-occurrence statistics and distributional similarity in a large corpus.
• The method ranks patterns by their pertinence score, with higher scores indicating better expression of the semantic relation.
• It uses a vector space model to compute relational similarity, avoiding reliance on manual annotations.
• The approach is evaluated on two tasks: solving multiple-choice SAT analogies and classifying semantic relations in noun-modifier pairs.

Experimental results

Research questions

• RQ1 Can implicit semantic relations between word pairs be effectively expressed using unsupervised pattern mining?
• RQ2 How can patterns be ranked by their relevance to a given word pair without supervision?
• RQ3 Does the pertinence-based ranking of patterns outperform traditional methods like tf-idf in capturing semantic relations?
• RQ4 Can this method achieve state-of-the-art performance on word analogy and semantic relation classification tasks?
• RQ5 To what extent can unsupervised pattern discovery support the construction of semantic networks and lexicons?

Key findings

• The proposed method achieved state-of-the-art performance on the multiple-choice SAT word analogy task, significantly outperforming tf-idf-based baselines.
• On the task of classifying semantic relations in noun-modifier pairs, the method demonstrated superior accuracy compared to alternative pattern ranking algorithms.
• The pertinence-based ranking mechanism effectively identified high-quality patterns such as 'X is the largest Y' for the pair 'ostrich : bird'.
• The algorithm successfully captured implicit relations without requiring any labeled training data or manual feature engineering.
• Empirical evaluation confirmed that pattern pertinence correlates strongly with the ability to express the intended semantic relation.
• The method's performance was validated on two distinct NLP tasks, demonstrating broad applicability in semantic relation discovery.