Question Answering Using Sentence Parsing and Semantic Network Matching

  • Sven Hartrumpf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3491)


The paper describes a question answering system for German called InSicht. All documents in the system are analyzed by a syntactico-semantic parser in order to represent each document sentence by a semantic network. A question sent to InSicht is parsed yielding its semantic network representation and its sentence type. The semantic network is expanded by applying equivalence rules, implicational rules, and concept variations based on semantic relations in computer lexicons and other knowledge sources. During the search stage, every semantic network generated for the question is matched with semantic networks for document sentences. If a match succeeds, an answer is generated from the matching semantic network for the supporting document. InSicht is evaluated on the QA@CLEF 2004 test set. A hierarchy of problem classes is proposed and a sample of suboptimally answered questions is annotated with these problem classes. Finally, some conclusions are drawn, main problems are identified, and directions for future work as suggested by these problems are indicated.


Problem Class Semantic Network Query Expansion Sentence Type Question Answering 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Neumann, G., Xu, F.: Mining answers in German web pages. In: Proceedings of the International Conference on Web Intelligence (WI 2003), Halifax, Canada (2003)Google Scholar
  2. 2.
    Harabagiu, S., Moldovan, D., Paşca, M., Mihalcea, R., Surdeanu, M., Bunescu, R., Gîrju, R., Rus, V., Morărescu, P.: The role of lexico-semantic feedback in open-domain textual question-answering. In: Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics (ACL 2001), Toulouse, France, pp. 274–281 (2001)Google Scholar
  3. 3.
    Ide, N., Priest-Dorman, G., Véronis, J.: Corpus Encoding Standard (1996)Google Scholar
  4. 4.
    Helbig, H., Hartrumpf, S.: Word class functions for syntactic-semantic analysis. In: Proceedings of the 2nd International Conference on Recent Advances in Natural Language Processing (RANLP 1997), Tzigov Chark, Bulgaria, pp. 312–317 (1997)Google Scholar
  5. 5.
    Hartrumpf, S.: Hybrid Disambiguation in Natural Language Analysis. Der Andere Verlag, Osnabrück (2003)Google Scholar
  6. 6.
    Helbig, H.: Die semantische Struktur natürlicher Sprache: Wissensrepräsentation mit MultiNet. Springer, Berlin (2001)Google Scholar
  7. 7.
    Helbig, H., Gnörlich, C.: Multilayered extended semantic networks as a language for meaning representation in NLP systems. In: Gelbukh, A. (ed.) CICLing 2002. LNCS, vol. 2276, pp. 69–85. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Echihabi, A., Oard, D.W., Marcu, D., Hermjakob, U.: Cross-language question answering at the USC Information Sciences Institute. In: Peters, C. (ed.) Results of the CLEF 2003 Cross-Language System Evaluation Campaign, Working Notes for the CLEF 2003 Workshop, Trondheim, Norway, pp. 331–337 (2003)Google Scholar
  9. 9.
    Hartrumpf, S., Helbig, H., Osswald, R.: The semantically based computer lexicon HaGenLex – Structure and technological environment. Traitement automatique des langues 44(2), 81–105 (2003)Google Scholar
  10. 10.
    Osswald, R.: Die Verwendung von GermaNet zur Pflege und Erweiterung des Computerlexikons HaGenLex. LDV Forum 19(1/2), 43–51 (2004)Google Scholar
  11. 11.
    Leveling, J., Hartrumpf, S.: University of Hagen at CLEF 2004: Indexing and translating concepts for the GIRT task. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds.) CLEF 2004. LNCS, vol. 3491, pp. 271–282. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Hartrumpf, S.: Coreference resolution with syntactico-semantic rules and corpus statistics. In: Proceedings of the Fifth Computational Natural Language Learning Workshop (CoNLL 2001), Toulouse, France, pp. 137–144 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sven Hartrumpf
    • 1
  1. 1.Intelligent Information and Communication Systems (IICS)University of Hagen (FernUniversität in Hagen)HagenGermany

Personalised recommendations