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This paper focuses on the use of advanced techniques of text analysis as support for collocation extraction. A hybrid system is presented that combines statistical methods and multilingual parsing for detecting accurate collocational information from English, French, Spanish and Italian corpora. The advantage of relying on full parsing over using a traditional window method (which ignores the syntactic information) is first theoretically motivated, then empirically validated by a comparative evaluation experiment.

An impressive amount of work was devoted over the past few decades to collocation extraction. The state of the art shows that there is a sustained interest in the morphosyntactic preprocessing of texts in order to better identify candidate expressions; however, the treatment performed is, in most cases, limited (lemmatization, POS-tagging, or shallow parsing). This article presents a collocation extraction system based on the full parsing of source corpora, which supports four languages: English, French, Spanish, and Italian. The performance of the system is compared against that of the standard mobile-window method. The evaluation experiment investigates several levels of the significance lists, uses a fine-grained annotation schema, and covers all the languages supported. Consistent results were obtained for these languages: parsing, even if imperfect, leads to a significant improvement in the quality of results, in terms of collocational precision (between 16.4 and 29.7%, depending on the language; 20.1% overall), MWE precision (between 19.9 and 35.8%; 26.1% overall), and grammatical precision (between 47.3 and 67.4%; 55.6% overall). This positive result bears a high importance, especially in the perspective of the subsequent integration of extraction results in other NLP applications.

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The correct interpretation of Multiword Units (MWUs) is crucial to many applications in Natural Language Processing but is a challenging and complex task. In recent years, the computational treatment of MWUs has received considerable attention but we believe that there is much more to be done before we can claim that NLP and Machine Translation (MT) systems process MWUs successfully. In this chapter, we present a survey of the field with particular reference to Machine Translation and Translation Technology.