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Interactive Topic Graph Extraction and Exploration of Web Content

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Multi-source, Multilingual Information Extraction and Summarization

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Abstract

In the following, we present an approach using interactive topic graph extraction for the exploration of Web content. The initial information request, in the form of a query topic description, is issued online by a user to the system. The topic graph is then constructed from N Web snippets that are produced by a standard search engine. We consider the extraction of a topic graph to be a specific empirical collocation extraction task, where collocations are extracted between chunks. Our measure of association strength is based on the pointwise mutual information between chunk pairs which explicitly takes their distance into account. This topic graph can then be further analyzed by users so that they can request additional background information with the help of interesting nodes and pairs of nodes in the topic graph, e.g., explicit relationships extracted from Wikipedia or those automatically extracted from additional Web content as well as conceptual information of the topic in form of semantically oriented clusters of descriptive phrases. This information is presented to the users, who can investigate the identified information nuggets to refine their information search. An initial user evaluation shows that our approach is especially helpful for finding new interesting information on topics about which the user has only a vague idea or no idea, at all.

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Notes

  1. 1.

    Actually, both languages are only supported in the i–GNSSMM mode. In the case of the i–MILREX mode, we currently only support the English Wikipedia.

  2. 2.

    Consult, for example, the Web page http://nlp.uned.es/weps/ for more information about the problem space.

  3. 3.

    The screenshots shows relations retrieved from Wikipedia infoboxes only. The component for detecting missing relationships is not yet integrated in the running system.

  4. 4.

    For the remainder of the paper N = 1000. We are using Bing (http://www.bing.com/) for Web search.

  5. 5.

    Concerning the English PoS tags, “word/PoS” expressions that match the following regular expression are considered as extended noun tag: “/(N(N∣P))∣/VB(N∣G)∣/IN∣/DT”. The English Verbs are those whose PoS tag start with VB. We are using the tag sets from the Penn treebank (English) and the Negra treebank (German).

  6. 6.

    Currently, the main purpose of recognizing verb chunks is to improve proper recognition of noun groups. The verb chunks are ignored when building the topic graph.

  7. 7.

    In fact we used the polynomials of the Taylor series for ln(1 + x). Note also that k is actually restricted by the number of chunks in a snippet.

  8. 8.

    For “Jim Clark”, e.g., wikipedia’s infoboxes do not provide information for the relations: birthplace, place_of_death, or cause_of_death.

  9. 9.

    The classification of NP chunks to argument types like times and dates is currently done by using simple regular expressions.

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Acknowledgements

The presented work was partially supported by grants from the German Federal Ministry of Economics and Technology (BMWi) to the Theseus project (FKZ: 01MQ07016).

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  1. German Research Center for Artificial Intelligence GmbH (DFKI), Stuhlsatzenhausweg 3, D-66123, Saarbrücken, Germany

    Günter Neumann & Sven Schmeier

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  1. Günter Neumann
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Correspondence to Günter Neumann .

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Editors and Affiliations

  1. Universite Sorbonne Nouvelle, LATTICE-CNRS, Ecole Normale Superieure and, rue d'Ulm 45, Paris, 75005, France

    Thierry Poibeau

  2. , Information & Communication Technologies, Universitat Pompeu Fabra, C/ Tanger 122-140, Barcelona, 08018, Spain

    Horacio Saggion

  3. Institute for Computer Science, Polish Acadmey of Science, ul. Jana Kazimierza 5, Warsaw, 01-248, Poland

    Jakub Piskorski

  4. Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2, Helsinki, 00014, Finland

    Roman Yangarber

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Neumann, G., Schmeier, S. (2013). Interactive Topic Graph Extraction and Exploration of Web Content. In: Poibeau, T., Saggion, H., Piskorski, J., Yangarber, R. (eds) Multi-source, Multilingual Information Extraction and Summarization. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28569-1_7

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