• Refined Salience Weighting and Error Analysis in Anaphora Resolution.

      Evans, Richard (The Research Group in Computational Linguistics, 2002)
      In this paper, the behaviour of an existing pronominal anaphora resolution system is modified so that different types of pronoun are treated in different ways. Weights are derived using a In genetic algorithm for the outcomes of tests applied by this branching algorithm. Detailed evaluation and error analysis is undertaken. Proposals for future research are put forward.
    • A High Precision Information Retrieval Method for WiQA

      Orasan, Constantin; Puşcaşu, Georgiana (Springer, 2007)
      This paper presents Wolverhampton University’s participation in the WiQA competition. The method chosen for this task combines a high precision, but low recall information retrieval approach with a greedy sentence ranking algorithm. The high precision retrieval is ensured by querying the search engine with the exact topic, in this way obtaining only sentences which contain the topic. In one of the runs, the set of retrieved sentences is expanded using coreferential relations between sentences. The greedy algorithm used for ranking selects one sentence at a time, always the one which adds most information to the set of sentences without repeating the existing information too much. The evaluation revealed that it achieves a performance similar to other systems participating in the competition and that the run which uses coreference obtains the highest MRR score among all the participants.
    • NP animacy identification for anaphora resolution

      Orasan, Constantin; Evans, Richard (American Association for Artificial Intelligence, 2007)
      In anaphora resolution for English, animacy identification can play an integral role in the application of agreement restrictions between pronouns and candidates, and as a result, can improve the accuracy of anaphora resolution systems. In this paper, two methods for animacy identification are proposed and evaluated using intrinsic and extrinsic measures. The first method is a rule-based one which uses information about the unique beginners in WordNet to classify NPs on the basis of their animacy. The second method relies on a machine learning algorithm which exploits a WordNet enriched with animacy information for each sense. The effect of word sense disambiguation on the two methods is also assessed. The intrinsic evaluation reveals that the machine learning method reaches human levels of performance. The extrinsic evaluation demonstrates that animacy identification can be beneficial in anaphora resolution, especially in the cases where animate entities are identified with high precision.
    • Automatic multidocument summarization of research abstracts: Design and user evaluation

      Ou, Shiyan; Khoo, Christopher S.G.; Goh, Dion H. (2013-06-28)