Exploiting Comparable Corpora and Bilingual Dictionaries for Cross-Language Text Categorization

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 553–560, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Exploiting Comparable Corpora and Bilingual Dictionaries for Cross-Language Text Categorization Alfio Gliozzo and Carlo Strapparava ITC-Irst via Sommarive, I-38050, Trento, ITALY {gliozzo,strappa}@itc.it Abstract Cross-language Text Categorization is the task of assigning semantic classes to docu- ments written in a target language (e.g. En- glish) while the system is trained using la- beled documents in a source language (e.g. Italian). In this work we present many solutions ac- cording to the availability of bilingual re- sources, and we show that it is possible to deal with the problem even when no such resources are accessible. The core technique relies on the automatic acquisi- tion of Multilingual Domain Models from comparable corpora. Experiments show the effectiveness of our approach, providing a low cost solution for the Cross Language Text Categorization task. In particular, when bilingual dictio- naries are available the performance of the categorization gets close to that of mono- lingual text categorization. 1 Introduction In the worldwide scenario of the Web age, mul- tilinguality is a crucial issue to deal with and to investigate, leading us to reformulate most of the classical Natural Language Processing (NLP) problems into a multilingual setting. For in- stance the classical monolingual Text Categoriza- tion (TC) problem can be reformulated as a Cross Language Text Categorization (CLTC) task, in which the system is trained using labeled exam- ples in a source language (e.g. English), and it classifies documents in a different target language (e.g. Italian). The applicative interest for the CLTC is im- mediately clear in the globalized Web scenario. For example, in the community based trade (e.g. eBay) it is often necessary to archive texts in dif- ferent languages by adopting common merceolog- ical categories, very often defined by collections of documents in a source language (e.g. English). Another application along this direction is Cross Lingual Question Answering, in which it would be very useful to filter out the candidate answers according to their topics. In the literature, this task has been proposed quite recently (Bel et al., 2003; Gliozzo and Strap- parava, 2005). In those works, authors exploited comparable corpora showing promising results. A more recent work (Rigutini et al., 2005) proposed the use of Machine Translation techniques to ap- proach the same task. Classical approaches for multilingual problems have been conceived by following two main direc- tions: (i) knowledge based approaches, mostly im- plemented by rule based systems and (ii) empirical approaches, in general relying on statistical learn- ing from parallel corpora. Knowledge based ap- proaches are often affected by low accuracy. Such limitation is mainly due to the problem of tun- ing large scale multilingual lexical resources (e.g. MultiWordNet, EuroWordNet) for the specific ap- plication task (e.g. discarding irrelevant senses, extending the lexicon with domain specific terms and their translations). On the other hand, em- pirical approaches are in general more accurate, because they can be …

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