Manos Tsagkias


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Using Pause Information for More Accurate Entity Recognition
Sahas Dendukuri | Pooja Chitkara | Joel Ruben Antony Moniz | Xiao Yang | Manos Tsagkias | Stephen Pulman
Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI

Entity tags in human-machine dialog are integral to natural language understanding (NLU) tasks in conversational assistants. However, current systems struggle to accurately parse spoken queries with the typical use of text input alone, and often fail to understand the user intent. Previous work in linguistics has identified a cross-language tendency for longer speech pauses surrounding nouns as compared to verbs. We demonstrate that the linguistic observation on pauses can be used to improve accuracy in machine-learnt language understanding tasks. Analysis of pauses in French and English utterances from a commercial voice assistant shows the statistically significant difference in pause duration around multi-token entity span boundaries compared to within entity spans. Additionally, in contrast to text-based NLU, we apply pause duration to enrich contextual embeddings to improve shallow parsing of entities. Results show that our proposed novel embeddings improve the relative error rate by up to 8% consistently across three domains for French, without any added annotation or alignment costs to the parser.


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LiMoSINe Pipeline: Multilingual UIMA-based NLP Platform
Olga Uryupina | Barbara Plank | Gianni Barlacchi | Francisco J. Valverde Albacete | Manos Tsagkias | Antonio Uva | Alessandro Moschitti
Proceedings of ACL-2016 System Demonstrations


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Learning to Explain Entity Relationships in Knowledge Graphs
Nikos Voskarides | Edgar Meij | Manos Tsagkias | Maarten de Rijke | Wouter Weerkamp
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)