This chapter presents a discourse processing framework based on weighted abduction. We elaborate on ideas described in Hobbs et al. (1993) and implement the abductive inference procedure in a system called Mini-TACITUS. Particular attention is paid to constructing a large and reliable knowledge base for supporting inferences. For this purpose we exploit such lexical-semantic resources as WordNet and FrameNet. English Slot Grammar is used to parse text and produce logical forms. We test the proposed procedure and the resulting knowledge base on the recognizing textual entailment task using the data sets from the RTE-2 challenge for evaluation. In addition, we provide an evaluation of the semantic role labeling produced by the system taking the Frame-Annotated Corpus for Textual Entailment as a gold standard.
- Logical Form
- Word Sense
- Good Interpretation
- Discourse Processing
- Semantic Role Label
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.
Michael C. McCord is an independent researcher.
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In the rest of this chapter we omit quantification.
The actual value of the default costs of the input propositions does not matter, because the interpretation costs are calculated using a multiplication function. The only heuristic we use here concerns setting all costs of the input propositions to be equal (all propositions cost 10 in the discussed example). This heuristic needs further investigation.
The anaphoric he in the logical form is already linked to its antecedent John.
The computation was done on a High Performance Cluster (320 2.4 GHz nodes, CentOS 5.0) of the Center for Industrial Mathematics (Bremen, Germany).
“Number of axioms” stands for the average number of axioms applied per sentence.
In order to get a better understanding of which parts of our KB are useful for computing entailment and for which types of entailment, in future, we are planning to use the detailed annotation of the RTE-2 dataset describing the source of the entailment, which was produced by Garoufi (2007). We would like to thank one of the reviewers of our IWCS 2011 paper which is the basis of this chapter for giving us this idea.
FATE was annotated with the FrameNet 1.3 labels, while we have been using version 1.5 for extracting axioms. However, in the new FN version the number of frames and roles increases and there is no message about removed frames in the General Release Notes R1.5, see http://framenet.icsi.berkeley.edu. Therefore we suppose that most of the frames and roles used for the FATE annotation are still present in FN 1.5.
We do not compare filler matching, because the FATE syntactic annotation follows different standards as the one produced by the ESG parser, which makes aligning fillers non-trivial.
There exists one more probabilistic system labeling text with FrameNet frames and roles, called SEMAFOR (Das et al. 2010). We do not compare our results with the results of SEMAFOR, because it has not been evaluated against the FATE corpus yet.
The discourse processing pipeline including the ILP-based abductive reasoner is available at https://github.com/metaphor-adp/Metaphor-ADP.
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Ovchinnikova, E., Montazeri, N., Alexandrov, T., Hobbs, J.R., McCord, M.C., Mulkar-Mehta, R. (2014). Abductive Reasoning with a Large Knowledge Base for Discourse Processing. In: Bunt, H., Bos, J., Pulman, S. (eds) Computing Meaning. Text, Speech and Language Technology, vol 47. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7284-7_7
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