PRIME: A System for Multi-lingual Patent Retrieval

Given the growing number of patents filed in multiple countries, users are interested in retrieving patents across languages. We propose a multi-lingual patent retrieval system, which translates a user query into the target language, searches a multilingual database for patents relevant to the query, and improves the browsing efficiency by way of machine translation and clustering. Our system also extracts new translations from patent families consisting of comparable patents, to enhance the translation dictionary

Density Matching for Bilingual Word Embedding

Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages. In this paper, we propose an approach that instead expresses the two monolingual embedding spaces as probability densities defined by a Gaussian mixture model, and matches the two densities using a method called normalizing flow. The method requires no explicit supervision, and can be learned with only a seed dictionary of words that have identical strings. We argue that this formulation has several intuitively attractive properties, particularly with the respect to improving robustness and generalization to mappings between difficult language pairs or word pairs. On a benchmark data set of bilingual lexicon induction and cross-lingual word similarity, our approach can achieve competitive or superior performance compared to state-of-the-art published results, with particularly strong results being found on etymologically distant and/or morphologically rich languages.Comment: Accepted by NAACL-HLT 201

A Comparison of Approaches for Measuring Cross-Lingual Similarity of Wikipedia Articles

Wikipedia has been used as a source of comparable texts for a range of tasks, such as Statistical Machine Translation and CrossLanguage Information Retrieval. Articles written in different languages on the same topic are often connected through inter-language-links. However, the extent to which these articles are similar is highly variable and this may impact on the use of Wikipedia as a comparable resource. In this paper we compare various language-independent methods for measuring cross-lingual similarity: character n-grams, cognateness, word count ratio, and an approach based on outlinks. These approaches are compared against a baseline utilising MT resources. Measures are also compared to human judgements of similarity using a manually created resource containing 700 pairs of Wikipedia articles (in 7 language pairs). Results indicate that a combination of language-independent models (char-ngrams, outlinks and word-count ratio) is highly effective for identifying cross-lingual similarity and performs comparably to language-dependent models (translation and monolingual analysis).The work of the first author was in the framework of the Tacardi research project (TIN2012-38523-C02-00). The work of the fourth author was in the framework of the DIANA-Applications (TIN2012-38603-C02-01) and WIQ-EI IRSES (FP7 Marie Curie No. 269180) research projects.Barrón Cedeño, LA.; Paramita, ML.; Clough, P.; Rosso, P. (2014). A Comparison of Approaches for Measuring Cross-Lingual Similarity of Wikipedia Articles. En Advances in Information Retrieval. Springer Verlag (Germany). 424-429. https://doi.org/10.1007/978-3-319-06028-6_36S424429Adafre, S., de Rijke, M.: Finding Similar Sentences across Multiple Languages in Wikipedia. In: Proc. of the 11th Conf. of the European Chapter of the Association for Computational Linguistics, pp. 62–69 (2006)Dumais, S., Letsche, T., Littman, M., Landauer, T.: Automatic Cross-Language Retrieval Using Latent Semantic Indexing. In: AAAI 1997 Spring Symposium Series: Cross-Language Text and Speech Retrieval, Stanford University, pp. 24–26 (1997)Filatova, E.: Directions for exploiting asymmetries in multilingual Wikipedia. In: Proc. of the Third Intl. Workshop on Cross Lingual Information Access: Addressing the Information Need of Multilingual Societies, Boulder, CO (2009)Levow, G.A., Oard, D., Resnik, P.: Dictionary-Based Techniques for Cross-Language Information Retrieval. Information Processing and Management: Special Issue on Cross-Language Information Retrieval 41(3), 523–547 (2005)Mcnamee, P., Mayfield, J.: Character N-Gram Tokenization for European Language Text Retrieval. Information Retrieval 7(1-2), 73–97 (2004)Mihalcea, R.: Using Wikipedia for Automatic Word Sense Disambiguation. In: Proc. of NAACL 2007. ACL, Rochester (2007)Mohammadi, M., GhasemAghaee, N.: Building Bilingual Parallel Corpora based on Wikipedia. In: Second Intl. Conf. on Computer Engineering and Applications., vol. 2, pp. 264–268 (2010)Munteanu, D., Fraser, A., Marcu, D.: Improved Machine Translation Performace via Parallel Sentence Extraction from Comparable Corpora. In: Proc. of the Human Language Technology and North American Association for Computational Linguistics Conf (HLT/NAACL 2004), Boston, MA (2004)Nguyen, D., Overwijk, A., Hauff, C., Trieschnigg, D.R.B., Hiemstra, D., de Jong, F.: WikiTranslate: Query Translation for Cross-Lingual Information Retrieval Using Only Wikipedia. In: Peters, C., Deselaers, T., Ferro, N., Gonzalo, J., Jones, G.J.F., Kurimo, M., Mandl, T., Peñas, A., Petras, V. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 58–65. Springer, Heidelberg (2009)Paramita, M.L., Clough, P.D., Aker, A., Gaizauskas, R.: Correlation between Similarity Measures for Inter-Language Linked Wikipedia Articles. In: Calzolari, E.A. (ed.) Proc. of the 8th Intl. Language Resources and Evaluation (LREC 2012), pp. 790–797. ELRA, Istanbul (2012)Potthast, M., Stein, B., Anderka, M.: A Wikipedia-Based Multilingual Retrieval Model. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 522–530. Springer, Heidelberg (2008)Simard, M., Foster, G.F., Isabelle, P.: Using Cognates to Align Sentences in Bilingual Corpora. In: Proc. of the Fourth Intl. Conf. on Theoretical and Methodological Issues in Machine Translation (1992)Steinberger, R., Pouliquen, B., Hagman, J.: Cross-lingual Document Similarity Calculation Using the Multilingual Thesaurus EUROVOC. In: Gelbukh, A. (ed.) CICLing 2002. LNCS, vol. 2276, pp. 415–424. Springer, Heidelberg (2002)Toral, A., Muñoz, R.: A proposal to automatically build and maintain gazetteers for Named Entity Recognition using Wikipedia. In: Proc. of the EACL Workshop on New Text 2006. Association for Computational Linguistics, Trento (2006

DCU and UTA at ImageCLEFPhoto 2007

Dublin City University (DCU) and University of Tampere(UTA) participated in the ImageCLEF 2007 photographic ad-hoc retrieval task with several monolingual and bilingual runs. Our approach was language independent: text retrieval based on fuzzy s-gram query translation was combined with visual retrieval. Data fusion between text and image content was performed using unsupervised query-time weight generation approaches. Our baseline was a combination of dictionary-based query translation and visual retrieval, which achieved the best result. The best mixed modality runs using fuzzy s-gram translation achieved on average around 83% of the performance of the baseline. Performance was more similar when only top rank precision levels of P10 and P20 were considered. This suggests that fuzzy sgram query translation combined with visual retrieval is a cheap alternative for cross-lingual image retrieval where only a small number of relevant items are required. Both sets of results emphasize the merit of our query-time weight generation schemes for data fusion, with the fused runs exhibiting marked performance increases over single modalities, this is achieved without the use of any prior training data