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Delexicalized Word Embeddings for Cross-lingual Dependency Parsing
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In: EACL ; https://hal.inria.fr/hal-01590639 ; EACL, Apr 2017, Valencia, Spain. pp.241 - 250, ⟨10.18653/v1/E17-1023⟩ ; http://eacl2017.org/ (2017)
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IRISA at DeFT2017 : classification systems of increasing complexity ; Participation de l'IRISA à DeFT2017 : systèmes de classification de complexité croissante
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In: DeFT 2017 - Défi Fouille de texte ; https://hal.archives-ouvertes.fr/hal-01643993 ; DeFT 2017 - Défi Fouille de texte, Jun 2017, Orléans, France. pp.1-10 (2017)
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Invariance: a Theoretical Approach for Coding Sets of Words Modulo Literal (Anti)Morphisms
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In: Springer, LNCS. ; https://hal-normandie-univ.archives-ouvertes.fr/hal-02117030 ; Springer, LNCS., 2017, pp.214-227 (2017)
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Things and Strings and More: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence, Topic Modeling, and Word Embedding
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Ju, Yiting. - : eScholarship, University of California, 2017
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In: Ju, Yiting. (2017). Things and Strings and More: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence, Topic Modeling, and Word Embedding. 0035: Geography. Retrieved from: http://www.escholarship.org/uc/item/4w60s702 (2017)
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Abstract:
Place name disambiguation, i.e., toponym disambiguation or toponym resolution, is the task of correctly identifying a place from a set of places sharing a common name. It contributes to a variety of tasks such as knowledge extraction, query answering, geographic information retrieval, and automatic tagging. Disambiguation quality relies on the ability to correctly identify and interpret contextual clues, complicating the task for short texts. Here I propose a novel approach to the disambiguation of place names from short texts that integrates three models: entity co-occurrence, topic modeling, and word embedding. The first model uses Linked Data to identify related entities to improve disambiguation quality. The second model uses topic modeling to differentiate places based on the terms used to describe them. The third model uses word embeddings to uncover the semantic relatedness between places and contexts. I evaluate this approach using a corpus of short texts collected through web scraping, determine the suitable weights for the models, and demonstrate that the combined model, i.e., Things and Strings Model, outperforms benchmark systems such as DBpedia Spotlight, TextRazor, and Open Calais by up to 85% in F-score and 46% in Precision at 1. A web service is built to demonstrate the proposed method and it can be a building block for those applications that need place name recognition and disambiguation.
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Keyword:
DBpedia; Geographic information science and geodesy; Geography; Information science; LDA; Linked Data; Natural Language Processing; Place name disambiguation; Word Embedding
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URL: http://www.escholarship.org/uc/item/4w60s702
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An empirical study of the Algerian dialect of Social network
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In: ICNLSSP 2017 - International Conference on Natural Language, Signal and Speech Processing ; https://hal.inria.fr/hal-01659997 ; ICNLSSP 2017 - International Conference on Natural Language, Signal and Speech Processing, Dec 2017, Casablanca, Morocco ; http://icnlssp.isga.ma (2017)
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Linguistic Knowledge Transfer for Enriching Vector Representations
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In: http://rave.ohiolink.edu/etdc/view?acc_num=osu1500571436042414 (2017)
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Induction de lexiques bilingues à partir de corpus comparables et parallèles
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