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

Changki Lee, Yi-Gyu Hwang, Hyo-Jung Oh, Soojong Lim, Jeong Heo, Chung-Hee Lee, Hyeon-Jin Kim, Ji-Hyun Wang, Myung-Gil Jang
2006 Lecture Notes in Computer Science  
In this paper, we describe a fine-grained Named Entity Recognition using Conditional Random Fields (CRFs) for question answering.  ...  In many QA systems, fine-grained named entities are extracted by coarse-grained named entity recognizer and fine-grained named entity dictionary.  ...  Conclusion In this paper, we describe a fine-grained Named Entity Recognition using Conditional Random Fields (CRFs) for question answering.  ... 
doi:10.1007/11880592_49 fatcat:x5e3gsijpbb63bx4wqra3qoqtu

Fine-grained named entity recognition and relation extraction for question answering

Changki Lee, Yi-Gyu Hwang, Myung-Gil Jang
2007 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '07  
But more finely grained named entity recognition and relation extraction between these entities have additional advantages in QA [1, 2, 3, 7] .  ...  perform fine-grained NER and relation extraction between these fine-grained named entities.  ... 
doi:10.1145/1277741.1277915 dblp:conf/sigir/LeeHJ07 fatcat:rinijgz4jzgmtf6fy32wckwi7q

Mapping Questions to Ontology Components for Question Answering over Linked Data

Marius Valeriu Stanciu, Eugen Vasilescu, Stefan Ruseti, Traian Rebedea
2017 Romanian Conference on Human-Computer Interaction  
To accomplish this task, we have trained a Conditional Random Field (CRF) classifier to label sentence tokens with the core data elements of the DBpedia ontology.  ...  The main purpose for this step is to improve question answering systems over linked data by reducing the ambiguity in the subsequent matching and query generation steps.  ...  CONCLUSIONS In this paper, we have proposed to use a Conditional Random Field (CRF) model to label question tokens as core data elements from the DBpedia ontology in order to improve question answering  ... 
dblp:conf/rochi/StanciuVRR17 fatcat:inkyugyjtzcmhljjqteyxpg62q

A Brief Analysis Of Amharic Nlp: From Pos Tagging To Question Answering

Seid Muhie Yimam
2016 Zenodo  
recognition, and Questions Answering [http://etd.aau.edu.et/bitstream/123456789/8587/1/Brook%20Eshetu%20Bete.pdf ].  ...  I specifically discuss POS tagging [http://aflat.org/files/HLTD201109.pdf ], Morphological processing [HornMorpho, see http://homes.soic.indiana.edu/gasser/L3/horn2.5.pdf ], Spell checking, Named entity  ...  /8587/1/Brook%20Eshetu%20Bete.pdf Amharic Named Entity recognition [1] • Machine learning approaches -Conditional random fields -80.66% of F-measure achieved • Amahric NE -no case information  ... 
doi:10.5281/zenodo.160367 fatcat:c4t54agpsrgiloopzvro3y45sq

BERT-Based Transfer-Learning Approach for Nested Named-Entity Recognition Using Joint Labeling

Ankit Agrawal, Sarsij Tripathi, Manu Vardhan, Vikas Sihag, Gaurav Choudhary, Nicola Dragoni
2022 Applied Sciences  
Named-entity recognition (NER) is one of the primary components in various natural language processing tasks such as relation extraction, information retrieval, question answering, etc.  ...  In addition, the performance of the proposed approach was compared with the conditional random field and the Bi-LSTM-CRF model.  ...  Figure 3 shows the basic workflow diagram for the named-entity recognition using the conditional random field model used in this research work.  ... 
doi:10.3390/app12030976 fatcat:ics5x5znkvhuziuydjcsvbcscm

Fine-Grained Entity Recognition

Xiao Ling, Daniel Weld
2021 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
This paper defines a fine-grained set of 112 tags, formulates the tagging problem as multi-class, multi-label classification, describes an unsupervised method for collecting training data, and presents  ...  Experiments show that the system accurately predicts the tags for entities. Moreover, it provides useful information for a relation extraction system, increasing the F1 score by 93%.  ...  We also thank the anonymous reviewers for valuable comments.  ... 
doi:10.1609/aaai.v26i1.8122 fatcat:lmlpohuhfnes3nbijr3alc2lb4

Transformer-based Methods for Recognizing Ultra Fine-grained Entities (RUFES) [article]

Emanuela Boros, Antoine Doucet
2021 arXiv   pre-print
We observe that our approach has great potential in increasing the performance of fine-grained entity recognition.  ...  Our participation relies on two neural-based models, one based on a pre-trained and fine-tuned language model with a stack of Transformer layers for fine-grained entity extraction and one out-of-the-box  ...  We applied our recent proposed model for coarse-grained and fine-grained named entity recognition (Boros et al., 2020a,b) and we used an out-of-the-box neural-based entity coreference model for detecting  ... 
arXiv:2104.06048v1 fatcat:svpqvf65yzezlbb2cebn6cjzei

Transformer-based Methods for Recognizing Ultra Fine-grained Entities (RUFES)

Emanuela Boros, Antoine Doucet
2021 Zenodo  
We observe that our approach has great potential in increasing the performance of fine-grained entity recognition.  ...  Our participation relies on two neural-based models, one based on a pretrained and fine-tuned language model with a stack of Transformer layers for fine-grained entity extraction and one out-of-the-box  ...  We applied our recent proposed model for coarse-grained and fine-grained named entity recognition (Boros et al., 2020a,b) and we used an out-of-the-box neural-based entity coreference model for detecting  ... 
doi:10.5281/zenodo.4555779 fatcat:d6zegzwc5jgm5gtzdujpufeg2m

Transformer-based Methods for Recognizing Ultra Fine-grained Entities (RUFES)

Emanuela Boros, Antoine Doucet
2021 Zenodo  
We observe that our approach has great potential in increasing the performance of fine-grained entity recognition.  ...  Our participation relies on two neural-based models, one based on a pretrained and fine-tuned language model with a stack of Transformer layers for fine-grained entity extraction and one out-of-the-box  ...  We applied our recent proposed model for coarse-grained and fine-grained named entity recognition (Boros et al., 2020a,b) and we used an out-of-the-box neural-based entity coreference model for detecting  ... 
doi:10.5281/zenodo.4555778 fatcat:5vzmf555frecjbn4qrw4c4jzu4

Extracting person names from diverse and noisy OCR text

Thomas L. Packer, Joshua F. Lutes, Aaron P. Stewart, David W. Embley, Eric K. Ringger, Kevin D. Seppi, Lee S. Jensen
2010 Proceedings of the fourth workshop on Analytics for noisy unstructured text data - AND '10  
Named entity recognition from scanned and OCRed historical documents can contribute to historical research.  ...  We illustrate the challenges and opportunities at hand for extracting names from OCRed data and identify directions for further improvement.  ...  Acknowledgements We would like to acknowledge Ancestry.com and Lee Jensen of Ancestry.com for providing the OCR data from their free-text collection and for financial support.  ... 
doi:10.1145/1871840.1871845 dblp:conf/and/PackerLSERSJ10 fatcat:6hph2wedg5ef7mzdwxi5g7qt4y

Named Entity Recognition with Extremely Limited Data [article]

John Foley, Sheikh Muhammad Sarwar, James Allan
2018 arXiv   pre-print
Traditional information retrieval treats named entity recognition as a pre-indexing corpus annotation task, allowing entity tags to be indexed and used during search.  ...  We propose exploring named entity recognition as a search task, where the named entity class of interest is a query, and entities of that class are the relevant "documents".  ...  ACKNOWLEDGEMENTS This work was supported in part by the Center for Intelligent Information Retrieval and in part by NSF grant #IIS-1617408.  ... 
arXiv:1806.04411v2 fatcat:jrbtrg26oja6nduxmzmhmijkbu

Randomized Kernel Approach for Named Entity Recognition in Tamil

N. Abinaya, M. Anand Kumar, K. P. Soman
2015 Indian Journal of Science and Technology  
In this paper, we present a new approach for Named Entity Recognition (NER) in Tamil language using Random Kitchen Sink algorithm.  ...  A lot of work has been done in the field of Named Entity Recognition for English language and Indian languages using various machine learning approaches.  ...  Various surveys are done for Named Entity Recognition since NER is a key task for dealing with many NLP tasks such as question answering system, information extraction etc.  ... 
doi:10.17485/ijst/2015/v8i24/85350 fatcat:hfky27ql7jfwvkm3x2yo7ayyga

Improving Twitter Named Entity Recognition using Word Representations

Zhiqiang Toh, Bin Chen, Jian Su
2015 Proceedings of the Workshop on Noisy User-generated Text  
Our system uses Conditional Random Fields to train two separate classifiers for the two evaluations: predicting 10 fine-grained types, and segmenting named entities.  ...  This paper describes our system used in the ACL 2015 Workshop on Noisy Usergenerated Text Shared Task for Named Entity Recognition (NER) in Twitter.  ...  For both evaluations, we model the problem as a sequential labeling task, using Conditional Random Fields (CRF) as the training algorithm.  ... 
doi:10.18653/v1/w15-4321 dblp:conf/aclnut/TohCS15 fatcat:di3hsjjlkfd7jbyigoihn6lajq

Code and Named Entity Recognition in StackOverflow [article]

Jeniya Tabassum, Mounica Maddela, Wei Xu, Alan Ritter
2020 arXiv   pre-print
In this paper, we introduce a new named entity recognition (NER) corpus for the computer programming domain, consisting of 15,372 sentences annotated with 20 fine-grained entity types.  ...  We also present the SoftNER model which achieves an overall 79.10 F_1 score for code and named entity recognition on StackOverflow data.  ...  ., 2019) use a bidirectional LSTM (Lample et al., 2016; Ma and Hovy, 2016) with conditional random field (Collobert et al., 2011) and contextualized word representations (McCann et al., 2017; Peters  ... 
arXiv:2005.01634v3 fatcat:5q6dredpsfcqjkklurzhsi3z7e

Two-Pass Named Entity Classification for Cross Language Question Answering

Yu-Chieh Wu, Kun-Chang Tsai, Jie-Chi Yang
2007 NTCIR Conference on Evaluation of Information Access Technologies  
Our method relies on three main important components, namely, passage retrieval, question classifier, and the named entity recognizer.  ...  In this paper, we present the mono-lingual and bilingual question answering experimental results at NTCIR6-CLQA.  ...  Roth (2002, 2005) provided 5000 manually annotated question set (UIUC question corpus 2 ) and applied the SNoW algorithm to classify 500 TREC-10 testing questions into 50 for fine-grained and 6 for coarse-grained  ... 
dblp:conf/ntcir/WuTY07a fatcat:nx5ysrofczfpfbf2spqpc2yvwi
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