Paper 152 (Research track)

Leveraging Semantic Web Resources to Detect Temporal Ambiguities in Text

Author(s): Farshad Bakhshandegan Moghaddam, Maria Koutraki, Harald Sack

Abstract: Named Entity Recognition and Disambiguation (NERD) is a fundamental analysis task in natural language processing for the correct interpretation of meaning. However, existing NERD applications most times are not able to detect and resolve ambiguities that require the determination of a temporal context for correct interpretation, such as in so-called temporal roles like CEO of a company, Soccer World Champion, or head of a country.
In this paper, we propose a novel learning-based approach to automatically detect and recognize temporal roles as a first step towards the subsequent disambiguation.
The approach is driven by Conditional Random Fields (CRF) leveraging information learned from Wikipedia and Wikidata knowledge graph.
Experiments on a manually annotated dataset as well as on a large dataset automatically collected from Wikipedia show that the CRF-based approach outperforms vanilla baselines such as dictionary matching.

Keywords: NLP; Entity Recognition; Temporal Ambiguity; Conditional Random Fields; Wikidata

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