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International audience; In this paper, we discuss practical and methodological issues of the creation of reference term lists (RTLs) for the evaluation of mono- lingual and bilingual term candidate extraction from comparable corpora in the domains of wind energy and mobile technology. These reference term lists are intended to serve as a "gold standard" for the qualita- tive and quantitative evaluation of automatic term extraction tools. We present the preliminary results of the evaluation of the monolingual term extraction. Using the manually collected RTLs, we evaluated monolin- gual term candidate lists which are automatically extracted from the Spanish texts in the domain of wind energy.

Poster; National audience; For scientific domains, terminological resources like dictionaries are often not available or not up-to-date. Additionally, term variation (Daille, 2005) is often not documented. As a result, translators working in technical domains usually spend much time building terminological resources. The project TTC aims at compiling domain-specific lexical resources which are to be integrated into CAT tools and SMT systems. Since parallel data is often not available, comparable corpora are used: they are available for a large range of domains in many languages. The TTC tool suite consists of the following steps: 1. corpus collection using a focused crawler (Groc, 2011) 2. pattern-based term extraction of terminologically relevant noun phrases from tagged and lemmatized text (Schmid, 1995), 3. identification of term variants: (DE) "Korrosionsschutz = Schutz gegen Korrosion" (corrosion protection = protection against corrosion) 4. term alignment: for a given term of the source language, equivalents in the target language are searched and aligned. Term lists of both the source and target language, as well as a general language dictionary are taken as an input to this step. In our poster presentation, we focus on term alignment, presenting two approaches: (1) lexical strategies and (2) the use of context vectors. 1. Terms do not necessarily have an equivalent of the same syntactic structure in other languages, particularly German compounds. By applying term variation patterns, compounds can be reformulated, resulting in term variants of different syntactic structures (Morin and Daille, 2010). This allows to individually look up the components of a compound in the dictionary and identify matching target language terms: "Stromspeicherung = Speicherung von Strom = storage of power / storage of electricity". 2. Terms and their translations tend to appear in comparable lexical contexts. For each source language term, context vectors are computed and translated into the target language. The translated vectors are then compared with target language context vectors (using cosine measure): terms with similar context vectors are likely to be translations. Since both approaches depend on the coverage of the dictionary, we consider the lexical strategies as an input for the context vector method.

International audience; In this paper, we deal with bilingual terminology extraction from comparable corpora. The extraction can be seen as a pipeline of processing steps. We will discuss grouping of term variants and describe two methods for bilingual term alignment of neoclassical terms: a knowledge-poor approach using string similarity measures and a linguistically motivated approach which is extended to cover German compound nouns.

Poster and demonstration; National audience; In the TTC project, monolingual term candidate extraction tools are being developed, as a basis for later term alignment, using comparable corpora. TTC covers English, German, French, Spanish, Latvian, Russian and Chinese. In the poster, we address the extraction of morphological and syntactic term variants of the first four languages. The term extraction pipeline is aimed at texts of technical domains (e.g. wind energy), which are obtained by a thematic web-crawler and then are POS-tagged and lemmatized. We extract multiword term candidates by POS-patterns (e.g. adjective+noun) and statistically filter domain-relevant terms. In a second step, morpho-syntactic term variants are grouped into sets: (1) metallhaltiger Abfall ↔ Abfall aus Metall. We use the typology of term variation proposed by (Daille, 2005). Certain types of variation are captured by expanding basic POS-patterns; for modifications (adjectives, embedding under genitive or of-phrases), additional distributional data are used, e.g. the frequency of modifiers in particular constructions. For morphological variation, we experiment with a simple stemming-based approach and a morphological analyzer: stemming rules allow us to relate terms like production d'électricité and électricité produite. To relate e.g. Emissionssteigerung and gesteigerte Emission, we need to split the compound noun Emission|Steigerung and to model the relation between steigern and gesteigert, which can be done using SMOR (Schmid et al., 2004). In addition to monolingual term variants, we are interested in patterns applicable to several languages: (2) noun adjective = noun de noun énergie générable ↔ génération d'énergie (FR) energía generable ↔ generación de energía (ES).