The main work in bilingual lexicon extraction from comparable corpora is based on the implicit hypothesis that corpora are balanced . However , the historical contextbased projection method dedicated to this task is relatively insensitive to the sizes of each part of the comparable corpus . Within this context , we have carried out a study on the influence of unbalanced specialized comparable corpora on the quality of bilingual terminology extraction through different experiments . Moreover , we have introduced a regression model that boosts the observations of word cooccurrences used in the context-based projection method . Our results show that the use of unbalanced specialized comparable corpora induces a significant gain in the quality of extracted lexicons . The bilingual lexicon extraction task from bilingual corpora was initially addressed by using parallel corpora ( i.e. a corpus that contains source texts and their translation ) . However , despite good results in the compilation of bilingual lexicons , parallel corpora are scarce resources , especially for technical domains and for language pairs not involving English . For these reasons , research in bilingual lexicon extraction has focused on another kind of bilingual corpora comprised of texts sharing common features such as domain , genre , sampling period , etc . without having a source text / target text relationship ( McEnery and Xiao , 2007 ) . These corpora , well known now as comparable corpora , have also initially been introduced as non-parallel corpora ( Fung , 1995 ; Rapp , 1995 ) , and non-aligned corpora ( Tanaka and Iwasaki , 1996 ) . According to Fung and Che-ung ( 2004 ) , who range bilingual corpora from parallel corpora to quasi-comparable corpora going through comparable corpora , there is a continuum from parallel to comparable corpora ( i.e. a kind of filiation ) . The bilingual lexicon extraction task from comparable corpora inherits this filiation . For instance , the historical context-based projection method ( Fung , 1995 ; Rapp , 1995 ) , known as the standard approach , dedicated to this task seems implicitly to lead to work with balanced comparable corpora in the same way as for parallel corpora ( i.e. each part of the corpus is composed of the same amount of data ) . In this paper we want to show that the assumption that comparable corpora should be balanced for bilingual lexicon extraction task is unfounded . Moreover , this assumption is prejudicial for specialized comparable corpora , especially when involving the English language for which many documents are available due the prevailing position of this language as a standard for international scientific publications . Within this context , our main contribution consists in a re-reading of the standard approach putting emphasis on the unfounded assumption of the balance of the specialized comparable corpora . In specialized domains , the comparable corpora are traditionally of small size ( around 1 million words ) in comparison with comparable corpus-based general language ( up to 100 million words ) . Consequently , the observations of word co-occurrences which is the basis of the standard approach are unreliable . To make them more reliable , our second contribution is to contrast different regression models in order to boost the observations of word co-occurrences . This strategy allows to improve the quality of extracted bilingual lexicons from comparable corpora . In this paper , we have studied how an unbalanced specialized comparable corpus could influence the quality of the bilingual lexicon extraction . This aspect represents a significant interest when working with specialized comparable corpora for which the quantity of the data collected may differ depending on the languages involved , especially when involving the English language as many scientific documents are available . More precisely , our different experiments show that using an unbalanced specialized comparable corpus always improves the quality of word translations . Thus , the MAP goes up from 29.6 % ( best result on the balanced corpora ) to 42.3 % ( best result on the unbalanced corpora ) in the breast cancer domain , and from 16.5 % to 26.0 % in the diabetes domain . Additionally , these results can be improved by using a prediction model of the word co-occurrence counts . Here , the MAP goes up from 42.3 % ( best result on the unbalanced corpora ) to 46.9 % ( best result on the unbalanced corpora with prediction ) in the breast cancer domain , and from 26.0 % to 29.8 % in the diabetes domain . We hope that this study will pave the way for using specialized unbalanced comparable corpora for bilingual lexicon extraction .

Challenge: The standard mapping approach for bilingual lexicon extraction assumes that two corpus to be balanced but no study assesses if this influences the performance.
Approach: They analyze the relationship between corpus balance and bilingual lexicon extraction and propose a regression model to improve the performance.
Outcome: They show that bilingual lexicon extraction is influenced by the balance of two source corpus, and imbalance improves the final extraction performance in specialized domains.