Lexical databases are invaluable sources of knowledge about words and their meanings, with numerous applications in areas like NLP, IR, and AI. We propose a methodology for the automatic construction of a large-scale multilingual lexical database where words of many languages are hierarchically organized in terms of their meanings and their semantic relations to other words. This resource is bootstrapped from WordNet, a well-known English-language resource. Our approach extends WordNet with around 1.5 million meaning links for 800,000 words in over 200 languages, drawing on evidence extracted from a variety of resources including existing (monolingual) wordnets, (mostly bilingual) translation dictionaries, and parallel corpora. Graph-based scoring functions and statistical learning techniques are used to iteratively integrate this information and build an output graph. Experiments show that this wordnet has a high level of precision and coverage, and that it can be useful in applied tasks such as cross-lingual text classification.

Sentence alignment is an essential step in building a parallel corpus. In this paper a specialized approach for the alignment of movie subtitles based on time overlaps is introduced. It is used for creating an extensive multilingual parallel subtitle corpus currently containing about 21 million aligned sentence fragments in 29 languages. Our alignment approach yields significantly higher accuracies compared to standard length-based approaches on this data. Furthermore, we can show that simple heuristics for subtitle synchronization can be used to improve the alignment accuracy even further.

In this paper on-going work of creating an extensive multilingual parallel corpus of movie subtitles is presented. The corpus currently contains roughly 23,000 pairs of aligned subtitles covering about 2,700 movies in 29 languages. Subtitles mainly consist of transcribed speech, sometimes in a very condensed way. Insertions, deletions and paraphrases are very frequent which makes them a challenging data set to work with especially when applying automatic sentence alignment. Standard alignment approaches rely on translation consistency either in terms of length or term translations or a combination of both. In the paper, we show that these approaches are not applicable for subtitles and we propose a new alignment approach based on time overlaps specifically designed for subtitles. In our experiments we obtain a significant improvement of alignment accuracy compared to standard length-based approaches. 1

Abstract After a brief account of a parallel corpus project involving many diverse languages and a summary of two previous evaluations of sentential alignment tools, results are presented from tests of three automatic aligners on English-Czech and French-Czech literary and legal texts, clean and noisy. The results confirm that an alignment tool may perform well on one type of texts and fail on another type, and indicate that near-to-perfect alignment is possible when tools providing high precision are combined with manual checking, where the proofreader can focus only on those parts of the text that were either not aligned at all, or that were aligned less reliably. Further gains in precision are shown to be feasible when alignments proposed by multiple aligners are intersected. 1

Example sentences provide an intuitive means of grasping the meaning of a word, and are frequently used to complement conventional word definitions. When a word has multiple meanings, it is useful to have example sentences for specific senses (and hence definitions) of that word rather than indiscriminately lumping all of them together. In this paper, we investigate to what extent such sense-specific example sentences can be extracted from parallel corpora using lexical knowledge bases for multiple languages as a sense index. We use word sense disambiguation heuristics and a cross-lingual measure of semantic similarity to link example sentences to specific word senses. From the sentences found for a given sense, an algorithm then selects a smaller subset that can be presented to end users, taking into account both representativeness and diversity. Preliminary results show that a precision of around 80 % can be obtained for a reasonable number of word senses, and that the subset selection yields convincing results.

Abstract not found

The number and sizes of parallel corpora keep growing, which makes it necessary to have automatic methods of processing them: combining, checking and improving corpora quality, etc. We here introduce a method which enables performing many of these by exploiting overlapping parallel corpora. The method finds the correspondence between sentence pairs in two corpora: first the corresponding language parts of the corpora are aligned and then the two resulting alignments are compared. The method takes into consideration slight differences in the source documents, different levels of segmentation of the input corpora, encoding differences and other aspects of the task. The paper describes two experiments conducted to test the method. In the first experiment, the Estonian-English part of the JRC-Acquis corpus was combined with another corpus of legislation texts. In the second experiment alternatively aligned versions of the JRC-Acquis are compared to each other with the example of all language pairs between English, Estonian and Latvian. Several additional conclusions about the corpora can be drawn from the results. The method proves to be effective for several parallel corpora processing tasks. 1.

natural language tasks

In this paper on-going work of creating an extensive multilingual parallel corpus of movie subtitles is presented. The corpus currently contains roughly 23,000 pairs of aligned subtitles covering about 2,700 movies in 29 languages. Subtitles mainly consist of transcribed speech, sometimes in a very condensed way. Insertions, deletions and paraphrases are very frequent which makes them a challenging data set to work with especially when applying automatic sentence alignment. Standard alignment approaches rely on translation consistency either in terms of length or term translations or a combination of both. In the paper, we show that these approaches are not applicable for subtitles and we propose a new alignment approach based on time overlaps specifically designed for subtitles. In our experiments we obtain a significant improvement of alignment accuracy compared to standard length-based approaches.

Language resources such as corpora consisting of annotated texts and utterances have been shown to be a central component in language studies and natural language processing as they, when