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Using search session context for named entity recognition in query

Junwu Du, Zhimin Zhang, Jun Yan, Yan Cui, Zheng Chen
2010 Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '10  
However, the lack of context information in short queries makes some classical named entity recognition (NER) algorithms fail.  ...  Recently, the problem of Named Entity Recognition in Query (NERQ) is attracting increasingly attention in the field of information retrieval.  ...  CONTEXT AWARE NAMED ENTITY RECOGNITION In this Section, we propose two novel search session features for NERQ.  ... 
doi:10.1145/1835449.1835605 dblp:conf/sigir/DuZYCC10 fatcat:bw66eg7im5avzo25j2qa53k43m

Context-aware multi-token concept recognition of biological entities

Kwangmin Kim, Doheon Lee
2021 BMC Bioinformatics  
Results In this paper, we propose a concept recognition method of multi-token biological entities using neural models combined with literature contexts.  ...  Background Concept recognition is a term that corresponds to the two sequential steps of named entity recognition and named entity normalization, and plays an essential role in the field of bioinformatics  ...  However, it can be easily extended to deal with other types of biological entities, when given proper context knowledge-bases.  ... 
doi:10.1186/s12859-021-04248-8 pmid:34674631 fatcat:7cukl5nn4bdkdpvpj3dcz6y4ie

Cybersecurity Named Entity Recognition using Multi-modal Ensemble Learning

Feng Yi, Bo Jiang, Lu Wang, Jianjun Wu
2020 IEEE Access  
Therefore, through the in-depth study of security entity characteristic, we propose a novel security named entity recognition model based on regular expressions and known-entity dictionary as well as conditional  ...  INDEX TERMS Cybersecurity, named entity recognition, regular expression, known-entity dictionary, conditional random fields. This work is licensed under a Creative Commons Attribution 4.0 License.  ...  Her main research interests include cyber security situational awareness, knowledge graph, and graph database mining.  ... 
doi:10.1109/access.2020.2984582 fatcat:vi4jkk5p6zfvdii5dmeor7bfue

Biomedical Entity Linking with Contrastive Context Matching [article]

Shogo Ujiie, Hayate Iso, Eiji Aramaki
2021 arXiv   pre-print
Specifically, we build the training instances from raw PubMed articles by dictionary matching and use them to train a context-aware entity linking model with contrastive learning.  ...  Results found that BioCoM substantially outperforms state-of-the-art models, especially in low-resource settings, by effectively using the context of the entities.  ...  This mismatch can be addressed by adopting named entity recognition system if available.  ... 
arXiv:2106.07583v2 fatcat:4c62l7ekwjc3fasolnhwo75iee

An Algorithm of Vocabulary Enhanced Intelligent Question Answering Based on FLAT1 [chapter]

Jing Sheng Lei, Shi Chao Ye, Sheng Ying Yang, Wei Song, Guan Mian Liang
2021 Frontiers in Artificial Intelligence and Applications  
In recent years, the lexical enhancement structure of word nodes combined with word nodes has been proved to be an effective method for Chinese named entity recognition.  ...  This method uses a new dictionary that combines the entity information of the knowledge graph, and only uses layer normalization for the removal of residual connection for the shallower network model.  ...  Dictionary Combining Entity Information Of Knowledge Graph This model does named entity recognition in question sentences.  ... 
doi:10.3233/faia210460 fatcat:vdhu3vyqzbhgngiuryagzof2vi

EDRAK: Entity-Centric Data Resource for Arabic Knowledge

Mohamed H. Gad-elrab, Mohamed Amir Yosef, Gerhard Weikum
2015 Proceedings of the Second Workshop on Arabic Natural Language Processing  
We are making EDRAK publicly available as a valuable resource to help advance research in Arabic NLP and IR tasks such as dictionary-based Named-Entity Recognition, entity classification, and entity summarization  ...  EDRAK contains more than two million entities together with their Arabic names and contextual keyphrases. Manual evaluation confirmed the quality of the generated data.  ...  For example, building a dictionary-based Named Entity Recognition (NER) system, requires a comprehensive and accurate dictionary of names (Darwish, 2013; Shaalan, 2014) .  ... 
doi:10.18653/v1/w15-3224 dblp:conf/wanlp/Gad-ElrabYW15 fatcat:ot4im7rl2rg4lf4j6hglmdpswq

Class LM and Word Mapping for Contextual Biasing in End-to-End ASR

Rongqing Huang, Ossama Abdel-hamid, Xinwei Li, Gunnar Evermann
2020 Interspeech 2020  
This algorithm is able to reduce the named entity utterance WER by 57% with little accuracy degradation on regular utterances.  ...  In this paper, we propose to train a context aware E2E model and allow the beam search to traverse into the context FST during inference.  ...  The text is processed with 6.4K BPE tokens. We use two test sets, first is a named entity rich set (named entity set) with 13K utterances.  ... 
doi:10.21437/interspeech.2020-1787 dblp:conf/interspeech/HuangALE20 fatcat:5ppmn3w25rhrnhp3msmymb66oi

Knowledge environments representing molecular entities for the virtual physiological human

M. Hofmann-Apitius, J. Fluck, L. Furlong, O. Fornes, C. Kolarik, S. Hanser, M. Boeker, S. Schulz, F. Sanz, R. Klinger, T. Mevissen, T. Gattermayer (+2 others)
2008 Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences  
The different scales of the VPH deal with different entities, relationships and processes, and in consequence the models used to describe and simulate biological functions vary significantly.  ...  Finally, we provide a perspective for the future implementation and use of knowledge environments representing molecular entities for the VPH.  ...  We will apply this approach in combination with dictionary-based approaches for the recognition of trivial names to extend the chemical knowledge in the VPH knowledge environment.  ... 
doi:10.1098/rsta.2008.0099 pmid:18559317 fatcat:ib6jt7mzkrg6fcq4vs5w7xwkfa

Extracting Meaningful Entities from Human-generated Tactical Reports

Jinhong K. Guo, David Van Brackle, Nicolas LoFaso, Martin O. Hofmann
2015 Procedia Computer Science  
Likewise, named entity recognizers have low recall, because few of the names in reports appear in standard dictionaries.  ...  Traditionally, tools did not exploit human generated, textual reports, leaving analysts to manually map dots on the map into meaningful entities using background knowledge about adversary equipment, organization  ...  Our context aware dictionary augmentation method significantly enhanced recall.  ... 
doi:10.1016/j.procs.2015.09.153 fatcat:wqyxb6tda5hnxpuer6u32z6wjq

Platform to Build the Knowledge Base by Combining Sensor Data and Context Data

Sungho Shin, Jungho Um, Dongmin Seo, Sung-Pil Choi, Seungwoo Lee, Hanmin Jung, Mun Yong Yi
2014 International Journal of Distributed Sensor Networks  
Thus, this study proposes an improved platform which builds a knowledge base for context awareness by applying distributed and parallel computing approach considering the characteristics of sensor data  ...  There have been many platforms to produce meaningful information and support human behavior and context-awareness through integrating diverse mobile, social, and sensing input streams.  ...  ., a morphological tagging, chunking, named entity recognition, parsing, etc.), and it shows better accuracy.  ... 
doi:10.1155/2014/542764 fatcat:5vjcyycgffc57nlz6cpzgmnknm

Class LM and word mapping for contextual biasing in End-to-End ASR

Rongqing Huang, Ossama Abdel-hamid, Xinwei Li, Gunnar Evermann
2020 arXiv   pre-print
This algorithm is able to reduce the named entity utterance WER by 57% with little accuracy degradation on regular utterances.  ...  In this paper, we propose to train a context aware E2E model and allow the beam search to traverse into the context FST during inference.  ...  The text is processed with 6.4K BPE tokens. We use two test sets, first is a named entity rich set (named entity set) with 13K utterances.  ... 
arXiv:2007.05609v3 fatcat:a42chuw5vjcuxmfuwdpaa46sje

Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition

Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Bin Dong, Shanshan Jiang
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
Experiments show that our ANN can achieve the state-of-the-art performance on ACE2005 named entity recognition benchmark.  ...  To alleviate this problem, this paper proposes Gazetteer-Enhanced Attentive Neural Networks, which can enhance region-based NER by learning name knowledge of entity mentions from easilyobtainable gazetteers  ...  Introduction Named entity recognition (NER), aiming to identify text mentions of specific entity types, is a fundamental NLP task.  ... 
doi:10.18653/v1/d19-1646 dblp:conf/emnlp/LinLHSDJ19 fatcat:u74g3fo5vvbcfjr5xzuu7dm2pu

Afan-Oromo Named Entity Recognition Using Bidirectional RNN

Birhanu Gardie, School of Computing and informatics, Mizan Tepi University, Ethiopia, Zemedkun Solomon
2022 Indian Journal of Science and Technology  
Objectives: This work aims about the development of Afan-Oromo language named entity recognition which widely used in question answering, information extraction and information retrieval aimed at categorizing  ...  and predicting tokens of a given corpus into predefined named entity classes like organization, location person and others (non-named entity tags).  ...  In (16) develop a NER with context aware dictionary knowledge through combining the dictionary matching features with the hidden representation using the LSTM-CRF technique.  ... 
doi:10.17485/ijst/v15i16.123 fatcat:iw3uhdmk6fffrgyxtwuudo3ylm

Automated Semantic Tagging of Textual Content

Jelena Jovanovic, Ebrahim Bagheri, John Cuzzola, Dragan Gasevic, Zoran Jeremic, Reza Bashash
2014 IT Professional Magazine  
., semantic taggersthat not only extract and disambiguate entities mentioned in the text, but also identify topics that unambiguously describe the text's main themes.  ...  AIDA [7] Pure text processing for entity spotting: a Named Entity Recognition tool is used to detect noun phrases as candidate entity mentions; YAGO2 is used for the selection of candidate entities  ...  Named Entity Recognition (NER) is a traditional Information Extraction task that consists of recognizing entities of a restricted set of types (e.g., Person, Organization, and Date) in a given text [2  ... 
doi:10.1109/mitp.2014.85 fatcat:lklbciaiyrh7dh6fhw55zcpdge

Linked Enterprise Data for Fine Grained Named Entity Linking and Web Intelligence

Albert Weichselbraun, Daniel Streiff, Arno Scharl
2014 Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14) - WIMS '14  
This section provides an overview of related work in named entity recognition and information extraction approaches that draw upon background knowledge retrieved from structured sources to improve their  ...  for named entity linking.  ...  named entity recognition process.  ... 
doi:10.1145/2611040.2611052 dblp:conf/wims/WeichselbraunSS14 fatcat:eloapridfjhp7mmieddytqxbza
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