Results 161 to 170 of about 14,087 (197)

Location-Aware Named Entity Disambiguation

2022
Named Entity Disambiguation (NED) and linking has been traditionally evaluated on natural language content that is both well-written and contextually rich. However, many NED approaches display poor performance on text sources that are short and noisy.
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Named Entity Disambiguation for Resource-Poor Languages

Proceedings of the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval, 2015
Named entity disambiguation (NED) is the task of linking ambiguous names in natural language text to canonical entities like people, organizations or places, registered in a knowledge base. The problem is well-studied for English text, but few systems have considered resource-poor languages that lack comprehensive name-entity dictionaries, entity ...
Gad-Elrab, M., Yosef, M., Weikum, G.
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Mining evidences for named entity disambiguation

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013
Named entity disambiguation is the task of disambiguating named entity mentions in natural language text and link them to their corresponding entries in a knowledge base such as Wikipedia. Such disambiguation can help enhance readability and add semantics to plain text.
Yang Li, Chi Wang, Fangqiu Han, Jiawei Han, Dan Roth, Xifeng Yan   +5 more
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Learning Entity Representation for Named Entity Disambiguation

2015
In this paper we present a novel disambiguation model, based on neural networks. Most existing studies focus on designing effective man-made features and complicated similarity measures to obtain better disambiguation performance. Instead, our method learns distributed representation of entity to measure similarity without man-made features.
Rui Cai, Houfeng Wang, Junhao Zhang
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Semantic Relatedness Approach for Named Entity Disambiguation

2010
Natural Language is a mean to express and discuss about concepts, objects, events, i.e., it carries semantic contents. One of the ultimate aims of Natural Language Processing techniques is to identify the meaning of the text, providing effective ways to make a proper linkage between textual references and their referents, that is, real world objects ...
Gentile, Anna Lisa, Zhang, Ziqi, Xia, Lei, Iria, José   +3 more
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Exploiting Wikipedia for Entity Name Disambiguation in Tweets

2014
Social media repositories serve as a significant source of evidence when extracting information related to the reputation of a particular entity (e.g., a particular politician, singer or company). Reputation management experts are in need of automated methods for mining the social media repositories (in particular Twitter) to monitor the reputation of ...
QURESHI, MUHAMMAD ATIF, O'Riordan, C, PASI, GABRIELLA   +2 more
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Personalized Page Rank for Named Entity Disambiguation

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2015
The task of Named Entity Disambiguation is to map entity mentions in the document to their correct entries in some knowledge base. We present a novel graph-based disambiguation approach based on Personalized PageRank (PPR) that combines local and global evidence for disambiguation and effectively filters out noise introduced by incorrect candidates ...
Maria Pershina, Yifan He, Ralph Grishman
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Named Entity Disambiguation Leveraging Multi-aspect Information

2015 IEEE International Conference on Data Mining Workshop (ICDMW), 2015
Named Entity Disambiguation (NED) aims at dis-ambiguating named entity mentions in a text to their corre-sponding entries in a knowledge base such as Wikipedia. Itis a fundamental task in Natural Language Processing (NLP)and has many applications such as information extraction, information retrieval, and knowledge acquisition.
Quanlong Zhang, Feng Li, Fang Wang, Zhoujun Li   +3 more
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Robust named entity disambiguation with random walks

Semantic Web, 2018
Named Entity Disambiguation is the task of assigning entities from a Knowledge Graph (KG) to mentions of such entities in a textual document. The state-of-the-art for this task balances two disparate sources of similarity: lexical, defined as the pairwise similarity between mentions in the text and names of entities in the KG; and semantic, defined ...
Guo, Zhaochen, Barbosa, Denilson
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Named Entity Disambiguation Based on Explicit Semantics

2012
In our work we present an approach to the Named Entity Disambiguation based on semantic similarity measure. We employ existing explicit semantics present in datasets such as Wikipedia to construct a disambiguation dictionary and vector---based word model. The analysed documents are transformed into semantic vectors using explicit semantic analysis. The
Martin Jačala, Jozef Tvarožek
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