This paper reports on work, carried out in the framework of the CombiNet project, focusing on the automatic extraction of word combinations from large corpora, with a view to represent the full distributional profile of selected lemmas. We describe two extraction methods, based on part-of-speech sequences (P-method) and syntactic patterns (S-method), respectively, evaluating their performance – contrastively, and with reference to external benchmarks – and discussing the relevance of automatic knowledge acquisition for lexicographic purposes. Our results indicate that both approaches provide valuable data and confirm previous claims that P-methods and S-methods are largely complementary, as they tend to retrieve different types of word combinations. In the second part of the paper, we present SYMPAThy, a data representation format devised to fruitfully merge the two methods by leveraging their respective points of strength. In order to explore SYMPAThy’s potentialities, a preliminary investigation on a small set of Italian idioms, and specifically their degree of fixedness/productivity, is also described.

This paper reports on work, carried out in the framework of the CombiNet project, focusing on the automatic extraction of word combinations from large corpora, with a view to represent the full distributional profile of selected lemmas. We describe two extraction methods, based on part-of-speech sequences (P-method) and syntactic patterns (S-method), respectively, evaluating their performance - contrastively, and with reference to external benchmarks - and discussing the relevance of automatic knowledge acquisition for lexicographic purposes. Our results indicate that both approaches provide valuable data and confirm previous claims that P-methods and S-methods are largely complementary, as they tend to retrieve different types of word combinations. In the second part of the paper, we present SYMPAThy, a data representation format devised to fruitfully merge the two methods by leveraging their respective points of strength. In order to explore SYMPAThy's potentialities, a preliminary investigation on a small set of Italian idioms, and specifically their degree of fixedness/productivity, is also described.

How to harvest Word Combinations from corpora. Methods, evaluation and perspectives

Lebani Gianluca;
2017-01-01

Abstract

This paper reports on work, carried out in the framework of the CombiNet project, focusing on the automatic extraction of word combinations from large corpora, with a view to represent the full distributional profile of selected lemmas. We describe two extraction methods, based on part-of-speech sequences (P-method) and syntactic patterns (S-method), respectively, evaluating their performance – contrastively, and with reference to external benchmarks – and discussing the relevance of automatic knowledge acquisition for lexicographic purposes. Our results indicate that both approaches provide valuable data and confirm previous claims that P-methods and S-methods are largely complementary, as they tend to retrieve different types of word combinations. In the second part of the paper, we present SYMPAThy, a data representation format devised to fruitfully merge the two methods by leveraging their respective points of strength. In order to explore SYMPAThy’s potentialities, a preliminary investigation on a small set of Italian idioms, and specifically their degree of fixedness/productivity, is also described.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3715954
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