Bilingual contexts from comparable corpora to mine for translations of collocations

Proceedings of the 17th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing2016Due to the limited availability of parallel data in many languages, we propose a methodology that benefits from comparable corpora to find translation equivalents for collocations (as a specific type of difficult-to-translate multi-word expressions). Finding translations is known to be more difficult for collocations than for words. We propose a method based on bilingual context extraction and build a word (distributional) representation model drawing on these bilingual contexts (bilingual English-Spanish contexts in our case). We show that the bilingual context construction is effective for the task of translation equivalent learning and that our method outperforms a simplified distributional similarity baseline in finding translation equivalents

Compilation and Exploitation of Parallel Corpora

With more and more text being available in electronic form, it is becoming relatively easy to obtain digital texts together with their translations. The paper presents the processing steps necessary to compile such texts into parallel corpora, an extremely useful language resource. Parallel corpora can be used as a translation aid for second-language learners, for translators and lexicographers, or as a data-source for various language technology tools. We present our work in this direction, which is characterised by the use of open standards for text annotation, the use of publicly available third-party tools and wide availability of the produced resources. Explained is the corpus annotation chain involving normalisation, tokenisation, segmentation, alignment, word-class syntactic tagging, and lemmatisation. Two exploitation results over our annotated corpora are also presented, namely aWeb concordancer and the extraction of bi-lingual lexica

Editor: Donncha O Croinin

Abstract Finnish Romani is a language with a fairly recent written tradition; for all practical purposes it is a 20th century phenomenon. An official orthography was created in 1971, and it is mostly from the 1970's onwards that we see texts of the kind which we normally associate with a written language variety. The text corpus described here is being compiled to support an ongoing investigation into the effects of language contact on Finnish Romani

Mind the source data! : Translation equivalents and translation stimuli from parallel corpora

Statements like ‘Word X of language A is translated with word Y of language B’ are incorrect, although they are quite common: words cannot be translated, as translation takes place on the level of sentences or higher. A better term for the correspondence between lexical items of source texts and their matches in target texts would be translation equivalence (Teq). In addition to Teq, there exists a reverse relation—translation stimulation (Tst), which is a correspondence between the lexical items of target texts and their matches (=stimuli) in source texts. Translation equivalents and translation stimuli must be studied separately and based on natural direct translations. It is not advisable to use pseudo-parallel texts, i.e. aligned pairs of translations from a ‘hub’ language, because such data do not reflect real translation processes. Both Teq and Tst are lexical functions, and they are not applicable to function words like prepositions, conjunctions, or particles, although it is technically possible to find Teq and Tst candidates for such words as well. The process of choosing function words when translating does not proceed in the same way as choosing lexical units: first, a relevant construction is chosen, and next, it is filled with relevant function words. In this chapter, the difference between Teq and Tst will be shown in examples from Russian–Finnish and Finnish–Russian parallel corpora. The use of Teq and Tst for translation studies and contrastive semantic research will be discussed, along with the importance of paying attention to the nature of the texts when analysing corpus findings.acceptedVersionPeer reviewe

Augmenting Translation Lexica by Learning Generalised Translation Patterns

Bilingual Lexicons do improve quality: of parallel corpora alignment, of newly extracted translation pairs, of Machine Translation, of cross language information retrieval, among other applications. In this regard, the first problem addressed in this thesis pertains to the classification of automatically extracted translations from parallel corpora-collections of sentence pairs that are translations of each other. The second problem is concerned with machine learning of bilingual morphology with applications in the solution of first problem and in the generation of Out-Of-Vocabulary translations. With respect to the problem of translation classification, two separate classifiers for handling multi-word and word-to-word translations are trained, using previously extracted and manually classified translation pairs as correct or incorrect. Several insights are useful for distinguishing the adequate multi-word candidates from those that are inadequate such as, lack or presence of parallelism, spurious terms at translation ends such as determiners, co-ordinated conjunctions, properties such as orthographic similarity between translations, the occurrence and co-occurrence frequency of the translation pairs. Morphological coverage reflecting stem and suffix agreements are explored as key features in classifying word-to-word translations. Given that the evaluation of extracted translation equivalents depends heavily on the human evaluator, incorporation of an automated filter for appropriate and inappropriate translation pairs prior to human evaluation contributes to tremendously reduce this work, thereby saving the time involved and progressively improving alignment and extraction quality. It can also be applied to filtering of translation tables used for training machine translation engines, and to detect bad translation choices made by translation engines, thus enabling significative productivity enhancements in the post-edition process of machine made translations. An important attribute of the translation lexicon is the coverage it provides. Learning suffixes and suffixation operations from the lexicon or corpus of a language is an extensively researched task to tackle out-of-vocabulary terms. However, beyond mere words or word forms are the translations and their variants, a powerful source of information for automatic structural analysis, which is explored from the perspective of improving word-to-word translation coverage and constitutes the second part of this thesis. In this context, as a phase prior to the suggestion of out-of-vocabulary bilingual lexicon entries, an approach to automatically induce segmentation and learn bilingual morph-like units by identifying and pairing word stems and suffixes is proposed, using the bilingual corpus of translations automatically extracted from aligned parallel corpora, manually validated or automatically classified. Minimally supervised technique is proposed to enable bilingual morphology learning for language pairs whose bilingual lexicons are highly defective in what concerns word-to-word translations representing inflection diversity. Apart from the above mentioned applications in the classification of machine extracted translations and in the generation of Out-Of-Vocabulary translations, learned bilingual morph-units may also have a great impact on the establishment of correspondences of sub-word constituents in the cases of word-to-multi-word and multi-word-to-multi-word translations and in compression, full text indexing and retrieval applications

Lexical selection for machine translation

Current research in Natural Language Processing (NLP) tends to exploit corpus resources as a way of overcoming the problem of knowledge acquisition. Statistical analysis of corpora can reveal trends and probabilities of occurrence, which have proved to be helpful in various ways. Machine Translation (MT) is no exception to this trend. Many MT researchers have attempted to extract knowledge from parallel bilingual corpora. The MT problem is generally decomposed into two sub-problems: lexical selection and reordering of the selected words. This research addresses the problem of lexical selection of open-class lexical items in the framework of MT. The work reported in this thesis investigates different methodologies to handle this problem, using a corpus-based approach. The current framework can be applied to any language pair, but we focus on Arabic and English. This is because Arabic words are hugely ambiguous and thus pose a challenge for the current task of lexical selection. We use a challenging Arabic-English parallel corpus, containing many long passages with no punctuation marks to denote sentence boundaries. This points to the robustness of the adopted approach. In our attempt to extract lexical equivalents from the parallel corpus we focus on the co-occurrence relations between words. The current framework adopts a lexicon-free approach towards the selection of lexical equivalents. This has the double advantage of investigating the effectiveness of different techniques without being distracted by the properties of the lexicon and at the same time saving much time and effort, since constructing a lexicon is time-consuming and labour-intensive. Thus, we use as little, if any, hand-coded information as possible. The accuracy score could be improved by adding hand-coded information. The point of the work reported here is to see how well one can do without any such manual intervention. With this goal in mind, we carry out a number of preprocessing steps in our framework. First, we build a lexicon-free Part-of-Speech (POS) tagger for Arabic. This POS tagger uses a combination of rule-based, transformation-based learning (TBL) and probabilistic techniques. Similarly, we use a lexicon-free POS tagger for English. We use the two POS taggers to tag the bi-texts. Second, we develop lexicon-free shallow parsers for Arabic and English. The two parsers are then used to label the parallel corpus with dependency relations (DRs) for some critical constructions. Third, we develop stemmers for Arabic and English, adopting the same knowledge -free approach. These preprocessing steps pave the way for the main system (or proposer) whose task is to extract translational equivalents from the parallel corpus. The framework starts with automatically extracting a bilingual lexicon using unsupervised statistical techniques which exploit the notion of co-occurrence patterns in the parallel corpus. We then choose the target word that has the highest frequency of occurrence from among a number of translational candidates in the extracted lexicon in order to aid the selection of the contextually correct translational equivalent. These experiments are carried out on either raw or POS-tagged texts. Having labelled the bi-texts with DRs, we use them to extract a number of translation seeds to start a number of bootstrapping techniques to improve the proposer. These seeds are used as anchor points to resegment the parallel corpus and start the selection process once again. The final F-score for the selection process is 0.701. We have also written an algorithm for detecting ambiguous words in a translation lexicon and obtained a precision score of 0.89.EThOS - Electronic Theses Online ServiceEgyptian GovernmentGBUnited Kingdo