Fine-Grained Named Entity Recognition Using Conditional Random Fields for Question Answering

Lee, Changki; Hwang, Yi-Gyu; Oh, Hyo-Jung; Lim, Soojong; Heo, Jeongyun; Lee, Chung-Hee; Kim, Hyeonjin; Wang, Ji-Hyun; Jang, Myung-Gil

doi:10.1007/11880592_49

Cited by 64 publications

(50 citation statements)

References 4 publications

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“…However, the initial implementation for rule-based systems is high and rule-based systems are unfeasible when there are too many rules to manage. To address these limitations, ML-based systems have been implemented that primarily utilize supervised learning models to collect statistical information from a large annotated corpus and determine NE classes based on this information [3][4][5][6][7][8][9][10]. Recently, ML-based systems that implement well-known supervised learning models have been developed to improve the accuracy of NER systems.…”

Section: Previous Workmentioning

confidence: 99%

Low-Cost Implementation of a Named Entity Recognition System for Voice-Activated Human-Appliance Interfaces in a Smart Home

Park

Kim

2018

Sustainability

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When we develop voice-activated human-appliance interface systems in smart homes, named entity recognition (NER) is an essential tool for extracting execution targets from natural language commands. Previous studies on NER systems generally include supervised machine-learning methods that require a substantial amount of human-annotated training corpus. In the smart home environment, categories of named entities should be defined according to voice-activated devices (e.g., food names for refrigerators and song titles for music players). The previous machine-learning methods make it difficult to change categories of named entities because a large amount of the training corpus should be newly constructed by hand. To address this problem, we present a semi-supervised NER system to minimize the time-consuming and labor-intensive task of constructing the training corpus. Our system uses distant supervision methods with two kinds of auto-labeling processes: auto-labeling based on heuristic rules for single-class named entity corpus generation and auto-labeling based on a pre-trained single-class NER model for multi-class named entity corpus generation. Then, our system improves NER accuracy by using a bagging-based active learning method. In our experiments that included a generic domain that featured 11 named entity classes and a context-specific domain about baseball that featured 21 named entity classes, our system demonstrated good performances in both domains, with F1-measures of 0.777 and 0.958, respectively. Since our system was built from a relatively small human-annotated training corpus, we believe it is a viable alternative to current NER systems in smart home environments.

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Section: Previous Workmentioning

confidence: 99%

Low-Cost Implementation of a Named Entity Recognition System for Voice-Activated Human-Appliance Interfaces in a Smart Home

Park

Kim

2018

Sustainability

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“…Utilizing fine-grained entity information enhances the performance for tasks like named entity disambiguation (Yosef et al, 2012), relation extraction (Ling and Weld, 2012) and question answering (Lin et al, 2012;Lee et al, 2006). A major challenge with fine grained entity mention classification is the scarcity of human annotated datasets.…”

Section: Related Workmentioning

confidence: 99%

End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification

Karn

Waltinger

Schütze

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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“…For comparison, we run the SVM-Struct that uses SVM-light for solving QP problems [4], maximum entropy (ME), and conditional random fields (CRF) [14]. We also run the LIBSVM which uses the SMO method [15].…”

Section: Application and Experimentsmentioning

confidence: 99%

Fast Training of Structured SVM Using Fixed-Threshold Sequential Minimal Optimization

Lee¹,

Jang²

2009

ETRI Journal

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In this paper, we describe a fixed-threshold sequential minimal optimization (FSMO) for structured SVM problems. FSMO is conceptually simple, easy to implement, and faster than the standard support vector machine (SVM) training algorithms for structured SVM problems. Because FSMO uses the fact that the formulation of structured SVM has no bias (that is, the threshold b is fixed at zero), FSMO breaks down the quadratic programming ( ).

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Fine-Grained Named Entity Recognition Using Conditional Random Fields for Question Answering

Cited by 64 publications

References 4 publications

Low-Cost Implementation of a Named Entity Recognition System for Voice-Activated Human-Appliance Interfaces in a Smart Home

Low-Cost Implementation of a Named Entity Recognition System for Voice-Activated Human-Appliance Interfaces in a Smart Home

End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification

Fast Training of Structured SVM Using Fixed-Threshold Sequential Minimal Optimization

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