Cross-lingual Document Similarity Calculation Using the Multilingual Thesaurus EUROVOC

Who is interested in the automatically calculated semantic similarity between documents written in different languages? What is it good for? Our own motivation for carrying out this work was to help users in the working environment of international organisations such as the European Commission to find their way through large multilingual document collections. One of the functionalities we considered to be useful is the capacity of showing users a ranked list of documents that are similar to one they are interested in, even if these other texts are written in different languages. Another functionality is the one to allow users to navigate through a multilingual document collection, using a document map [2, 12]. Document similarity calculation is also an essential tool for the automatic classification of texts into given classes. In addition to our own motivation, efforts are made to compile automatically a collection of parallel texts in order to gain statistical knowledge on texts and their translations [5, 10]. Assuming that the translation of a text is the most similar text for a given one, our similarity calculation tool can be used for this application, too. 1.3 How to evaluate automatic similarity calculation
author name, source of the text, time of writing and other information aspects could be used. Our own approach is to map different texts onto an existing knowledge structure, i.e. the multilingual thesaurus EUROVOC (see 2.1 and [1]), which has the advantage that it exists in all eleven official European Union languages. Unlike other approaches to measure cross-language document similarity ([4], [10]), our own approach does not require language pair-specific linguistic data because EUROVOC acts as a conceptual interlingua. On the other hand, our own approach cannot be extended to languages other than the ones covered by the thesaurus used. 1.2 Motivation to calculate the semantic similarity between documents
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Introduction
Following the introductory clarification of the question what semantic document similarity is (1.2), why (1.2) and how (1.3) to measure it, and how this work fits in with other activities at the JRC (1.4), section 2 summarises earlier work [11] on assigning controlled vocabulary thesaurus terms (henceforth called descriptors) to texts. Section 3 then describes how we use these lists of automatically assigned thesaurus descriptors as kind of a conceptual interlingua which allows to measure the semantic document similarity without using any dictionaries. Sections 4 and 5 discuss the limitations of the adopted method and give an outlook on future work. 1.1 What is document similarity?
Cross-lingual Document Similarity Calculation Using the Multilingual Thesaurus EUROVOC
Ralf Steinberger, Bruno Pouliquen, Johan Hagman
European Commission, Joint Research Centre Institute for the Protection and Security of the Citizen (IPSC) Cybersecurity and New Technologies for Combating Fraud Unit (CSCF) 21020 Ispra (VA), Italy {Ralf.Steinberger, Bruno.Pouliquen, Johan.Hagman}@jrc.it http://www.jrc.it/langtech
Although everybody has an intuition regarding the question whether two documents are similar, and to what extent, it is difficult to put one’ s finger on this intuition. Similarity measure can be based on the degree of lexical overlap between the texts that are to be compared, but it is also possible to use a more abstract measure by comparing the document contents. Latent semantic indexing approaches, for instance, go beyond counting the mere overlap of words used in texts and map words and documents to a more complex conceptual space [4]. Document similarity can also be based on stylistic information such as sentence length, the type-token ratio, word variation and other stylometric features. Finally, meta-information such as document type,
A. Gelbukh (Ed.): CICLing 2002, LNCS 2276, pp. 415-424, 2002. © Springer-Verlag Berlin Heidelberg 2002
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Байду номын сангаас
Ralf Steinberger, Bruno Pouliquen, and Johan Hagman
It is a non-trivial question how to judge, even intuitively, document content similarity. Are a three-page text and its 20-line abstract more similar than two 3-page documents talking about a similar subject? Should text length play a role at all in document similarity calculation? And should document language be a factor? It seems intuitively obvious that a text and its high-quality translation should be very similar. However, translators and other people speaking two languages very well know that different languages express concepts differently and have different ambiguities so that it is not reasonable to assume a 100% identity between a text and its translation. Due to the complexity of the issue, automatic similarity calculation is rather difficult to evaluate. Lacking other alternatives, we decided to use the successful spotting of text translations as an evaluation criterion: we assume that, looking at a large text collection, if our system identifies the translation of a document as the most similar document, it performs well (more on this in sections 3.1 and 3.3). 1.4 The JRC’ s text management system