Results 181 to 190 of about 3,266 (219)

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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Instance Filtering for entity recognition

ACM SIGKDD Explorations Newsletter, 2005
In this paper we propose Instance Filtering as preprocessing step for supervised classification-based learning systems for entity recognition. The goal of Instance Filtering is to reduce both the skewed class distribution and the data set size by eliminating negative instances, while preserving positive ones as much
Alfio Massimiliano Gliozzo, Claudio Giuliano, Raffaella Rinaldi   +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

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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Speech recognition of a named entity

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006
A 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, Yoshiyuki Okimoto, Hirofumi Yamamoto, Yoshinori Sagisaka   +3 more
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Named Entity Recognition for Vietnamese

2010
Named 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, Son Huu Hoang, Son Bao Pham, Thai Phuong 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), 2019
Due 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

2019
Named 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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Named Entity Recognition Using Gazetteer of Hierarchical Entities

2019
This 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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Constraint-Satisfaction Inference for Entity Recognition

2011
Contains fulltext : 333118.pdf (Publisher’s version ) (Closed access)
Canisius, Sander, van den Bosch, Antal, Daelemans, Walter   +2 more
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