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.

The development of robust "deep" linguistic parsers is known to be a difficult task. Few such systems can claim to satisfy the needs of large-scale NLP applications in terms of robustness, efficiency, granularity or precision. Adapting such systems to more than one language makes the task even more challenging. This paper describes some of the properties of Fips, a multilingual parsing system that has been for a number of years (and still is) under development at LATL. Based on Chomsky's generative grammar for its grammatical aspects, and on objectoriented (OO) sofware engineering techniques for its implementation, Fips is designed to efficiently parse the four Swiss "national" languages (German, French, Italian and English) to which we also added Spanish and (more recently) Greek.

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.

Although multiword expressions (MWEs) have received an increasing amount of attention in the NLP community over the last two decades, few papers have been dedicated to the specific problem of the interaction between MWEs and parsing. In this paper, we will discuss how the collocation identification task has been integrated in our rulebased parser and show how collocation knowledge has a positive impact on the parsing process. A manual evaluation has been conducted over a corpus of 4000 sentences, comparing outputs of the parser used with and without the collocation component. Results of the evaluation clearly support our claim.

In this paper we describe the rule-based MT system Its-2 developed at the University of Geneva and submitted for the shared task on pronoun translation organized within the Second DiscoMT Workshop. For improving pronoun translation, an Anaphora Resolution (AR) step based on Chomsky's Binding Theory and Hobbs' algorithm has been implemented. Since this strategy is currently restricted to 3rd person personal pronouns (i.e. they, it translated as elle, elles, il, ils only), absolute performance is affected. However, qualitative differences between the submitted system and a baseline without the AR procedure can be observed.