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Outline

Title
Abstract
Introduction
Related Work
Methodology
Association Measure Based Filtering (Ambf) Method
Context Similarity Based Filtering (CSBF) Method
Hybrid Dual Filtering (HDF) Method
Results and Analysis
Conclusion and Future Perspectives
References
All Topics
Computer Science
Artificial Intelligence

Hybrid method for automatic extraction of multiword expressions

Profile image of Ratna SanyalRatna Sanyal

2018, International Journal of Engineering & Technology

Abstract

A three phase hybrid method for automatic extraction of English multiword expressions (MWEs) has been proposed. The proposed method is based on linguistic patterns, association and context similarity between constituent words of the MWEs. First, the expressions are extract-ed in the form of N-grams from the raw text and then filtered using well defined linguistic patterns. Next, these expressions are again fil-tered using association score and context similarity score between their constituent words. Two association measures, Dice’s coefficient and PMI have been used for calculating the association score. The context similarity between words has been calculated using Latent Semantic Analysis (LSA) method. The problem of deciding the best value for the cut-off boundary thresholds in statistical methods is quite common. A two phase method of deciding the boundary threshold, using training dataset, has been proposed and employed in the current work. De-tailed performance analysis has b...

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Automatic extraction of multiword expressions (MWE) presents a tough challenge for the NLP community and corpus linguistics. Although various statistically driven or knowledge-based approaches have been proposed and tested, efficient MWE extraction still remains an unsolved issue. In this paper, we present our research work in which we tested approaching the MWE issue using a semantic field annotator. We use an English semantic tagger (USAS) developed at Lancaster University to identify multiword units which depict single semantic concepts. The Meter Corpus built in Sheffield was used to evaluate our approach. In our evaluation, this approach extracted a total of 4,195 MWE candidates, of which, after manual checking, 3,792 were accepted as valid MWEs, producing a precision of 90.39% and an estimated recall of 39.38%. Of the accepted MWEs, 68.22% or 2,587 are low frequency terms, occurring only once or twice in the corpus. These results show that our approach provides a practical solution to MWE extraction.

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