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Entity Recognition in Information Extraction
2014Detecting and resolving entities is an important step in information retrieval applications. Humans are able to recognize entities by context, but information extraction systems IES need to apply sophisticated algorithms to recognize an entity. The development and implementation of an entity recognition algorithm is described in this paper.
Novita Hanafiah, Christoph Quix
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Effective vector representation for the Korean named-entity recognition
Pattern Recognition Letters, 2019 Named-entity recognition, part of information extraction, is the task of finding the position of a proper names in a sentence and assigning it to the correct category.
Sunjae Kwon +2 more
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An Overview of Named Entity Recognition
2018 International Conference on Asian Language Processing (IALP), 2018Named 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 +3 more
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Proceedings of the International Conference & Workshop on Emerging Trends in Technology - ICWET '11, 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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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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Speech recognition of a named entity
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006A hierarchical language model is newly applied to identify a named entity consisting of multiple word sequences for continuous speech recognition. By redesigning an out-of-vocabulary model of a single word using phonotactic constraints for a named entity, a hierarchical model is composed harmoniously with conventional word and word-class N-grams ...
Tatsuhiko Tomita +3 more
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Named Entity Recognition for Vietnamese
2010Named Entity Recognition is an important task but is still relatively new for Vietnamese. It is partly due to the lack of a large annotated corpus. In this paper, we present a systematic approach in building a named entity annotated corpus while at the same time building rules to recognize Vietnamese named entities.
Dat Ba Nguyen +3 more
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Address Entities Extraction using Named Entity Recognition
2019 7th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), 2019Due to presence of large amounts of digital data, many tools for information extraction were developed in order to provide meaningful information and knowledge that could be used in text analysis and interpretation. Machine learning, artificial intelligence and data mining can help there a lot.
Emine Yaman, Kanita Krdzalic-Koric
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Entity Subword Encoding for Chinese Long Entity Recognition
2019Named entity recognition (NER) is a fundamental and important task in natural language processing area, which jointly predicts entity boundaries and pre-defined categories. For Chinese NER task, recognition of long entities has not been well addressed yet.
Changyu Hou, Meiling Wang, Changliang Li
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Constraint-Satisfaction Inference for Entity Recognition
2011Contains fulltext : 333118.pdf (Publisher’s version ) (Closed access)
Canisius, Sander +2 more
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Named Entity Recognition Using Gazetteer of Hierarchical Entities
2019This paper presents a named entity recognition method which finds predetermined entities in an unstructured text. The method uses word similarities based on typical word transformations (lemmatization and stemming), word embeddings and character level based similarity to map those entities onto words in the text.
Miha Stravs, Jernej Zupancic
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