Results 251 to 260 of about 2,584,993 (291)

Unveiling Arabic named entity recognition using natural language processing with artificial intelligence approach on Moroccan dialect. [PDF]

Sci Rep
Subait WB, Ahmad N, Alzaidi MSA, Alqahtani M, Alhashmi AA, Aljehane NO, Alshahrani M, Alotaibi SD.   +7 more
europepmc   +1 more source

Evaluation of SURUS: a named entity recognition NLP system to extract knowledge from interventional study records. [PDF]

BMC Med Res Methodol
Peeters C, Vijverberg K, Pouwer M, Westerman B, Boot M, Verberne S.   +5 more
europepmc   +1 more source

Dynamic taxonomy generation for future skills identification using a named entity recognition and relation extraction pipeline. [PDF]

Front Artif Intell
Gonzalez-Gomez LJ, Hernandez-Munoz SM, Borja A, Arana-Salas FA, Azofeifa JD, Noguez J, Caratozzolo P.   +6 more
europepmc   +1 more source

Named Entity Recognition

, 2014
Named entity recognition (NER) is the problem of locating and categorizing important nouns and proper nouns in a text. In this chapter, we review the general state of research on entity recognition, relevant challenges and the current state of the art works on named entity recognition on Semitic languages.
Rylko, Vojtěch
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Named entity recognition for tweets

ACM Transactions on Intelligent Systems and Technology, 2013
Two main challenges of Named Entity Recognition (NER) for tweets are the insufficient information in a tweet and the lack of training data. We propose a novel method consisting of three core elements: (1) normalization of tweets; (2) combination of a K-Nearest Neighbors (KNN) classifier with a linear Conditional Random Fields (CRF) model; and (3 ...
Xiaohua Liu, Furu Wei, Shaodian Zhang, Ming Zhou 0001   +3 more
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A Survey on Named Entity Recognition

2020
Natural language processing is an important research direction and research hotspot in the field of artificial intelligence. Named entity recognition is one of the key tasks, which is to identify entities with specific meanings in the text, such as names of people, places, institutions, proper nouns, etc.
Yan Wen 0002, Cong Fan, Geng Chen 0002, Xin Chen 0079, Ming Chen 0001   +4 more
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Named-Entity Recognition in Bengali

Post-Proceedings of the 4th and 5th Workshops of the Forum for Information Retrieval Evaluation, 2013
This paper describes two systems for Named Entity Recognition (NER) and performance of two systems has been compared. The first system is a rule-based one whereas the second one is statistical (based on CRF) in nature. The systems vary in some other aspects too, for example, the first system works on untagged data (not even POS tag is done) to identify
Apurbalal Senapati, Arjun Das, Utpal Garain   +2 more
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An Overview of Named Entity Recognition

2018 International Conference on Asian Language Processing (IALP), 2018
Named Entity Recognition (NER) is essential for some Natural Language Processing (NLP) tasks. Previous researchers gave a survey of NER in statistical machine learning era, however, research on NER has already changed a lot in recent decade. On the one hand, more and more NER systems adopt deep learning, transfer learning, knowledge base and other ...
Peng Sun, Xuezhen Yang, Xiaobing Zhao, Zhijuan Wang   +3 more
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Named entity recognition in query

Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 2009
This paper addresses the problem of Named Entity Recognition in Query (NERQ), which involves detection of the named entity in a given query and classification of the named entity into predefined classes. NERQ is potentially useful in many applications in web search. The paper proposes taking a probabilistic approach to the task using query log data and
Jiafeng Guo, Gu Xu, Xueqi Cheng, Hang Li 0001   +3 more
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Named entity recognition

Proceedings of the International Conference & Workshop on Emerging Trends in Technology, 2011
Named Entity Recognition (NER) system has two sub-tasks, first is identification and second is classification. In first NER identifies words in texts which represent proper names like location, person-name, organization, date, time etc. and in second it classifies them in to predefined categories.
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