Using Alignment to detect associated multiword expressions in bilingual corpora

Johan Segura 1 Violaine Prince 1
1 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Translating multiword expressions from a language to another needs to recognize them as such. Bilingual multiword expressions are an issue when they are not the exact word-to-word translation of each other. The following examples are provided for a French-English translation task: (1) Phrasal verbs such as " to call in on " becoming " rendre visite ", (2) " sorry to hear that ", that a human translator translates into the simple 'désolé que" (3) most of adverbial locutions like " such as ", equivalent to " de telle façon que ", or " de manière à ", etc.Thus, Machine Translation (MT) either requires a rich multiword bilingual database, or tends to create or enrich a first set of associated multiword expressions. Most of the time, existing resources are incomplete, and an interesting way to enhance covering is to provide a tool detecting 'associable' multiword expressions in parallel corpora. The latter are sets of texts that are translations of each others. There is an extensive literature in alignment techniques trying to link sentences from a text in a language, named the source, to one or many in the other language, seen as target. Sentence alignment is the basic preliminary task that underlies all others, more fine-grained. Word-to-word alignment has largely been dealt with by statistical systems. Multiword expressions have a granularity that lies between word and sentence. They are mostly phrasal, and sometimes with a rather strong syntactical and lexical divergence. With the improvement of parsers, alignment methods using syntax have emerged. Syntax allows the translation task, among others, to focus on relevant phrase fragments and to link multiword units together. For instance, Ozdowska's AliBi system is based on dependencies structures. The Groves', Hearnes' and Way's system uses syntactic trees with internal node alignments. Bilingual terminology, consisting in recognizing equivalent groups of words, also relies on syntax to extract patterns, such as Noun-Verb, Adjective-Noun, Prepositional Noun Phrase, etc...(e.g. Claveau, 2009). Most of these multiword expressions could be reduced down to collocations. A collocation is a multiword expression, naturally translated with quite strong constraints (e.g., " to show respect "-" faire preuve de respect "). Seretan's method [Seretan, V. (1999)] recognizes numerous equivalent pairs of collocations throught bilingual alignments which POS-tags are equivalents or close (even with distant words). But it only retrieves two-words collocations. Thus, there is a need for systems that might detect longer collocations, and more divergent ones. The method proposed in this article is an alignment process between pairs of sentences, strongly based on syntax. It relies on is a rule-based system combining partial alignments from a database through a non-iterative graph-theory based process. Multiword expressions patterns built on examples help providing alignments with a good coverage, which in turn detect new multiword expressions, and enrich the database. The article sketches the state-of-the art in alignment, focusing on syntactic oriented systems, describes the designed system as well a corpus run experiment with promising results.
Type de document :
Communication dans un congrès
INIST. TRALOGY, Mar 2011, France. 6, pp.144-156, 2011, 〈http://lodel.irevues.inist.fr/tralogy/index.php?%20id=144〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00764089
Contributeur : Violaine Prince-Barbier <>
Soumis le : mercredi 12 décembre 2012 - 12:16:37
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

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Johan Segura, Violaine Prince. Using Alignment to detect associated multiword expressions in bilingual corpora. INIST. TRALOGY, Mar 2011, France. 6, pp.144-156, 2011, 〈http://lodel.irevues.inist.fr/tralogy/index.php?%20id=144〉. 〈lirmm-00764089〉

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