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Coreference resolution using clusterization. / Bodrova, A.; Grafeeva, N.

Proceedings of the International FRUCT Conference on Intelligence, Social Media and Web, ISMW FRUCT 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 9-16.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research

Harvard

Bodrova, A & Grafeeva, N 2016, Coreference resolution using clusterization. in Proceedings of the International FRUCT Conference on Intelligence, Social Media and Web, ISMW FRUCT 2016. Institute of Electrical and Electronics Engineers Inc., pp. 9-16, 2016 International FRUCT Conference on Intelligence, Social Media and Web, Saint-Petersburg, Russian Federation, 28/08/16. https://doi.org/10.1109/FRUCT.2016.7584764

APA

Bodrova, A., & Grafeeva, N. (2016). Coreference resolution using clusterization. In Proceedings of the International FRUCT Conference on Intelligence, Social Media and Web, ISMW FRUCT 2016 (pp. 9-16). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FRUCT.2016.7584764

Vancouver

Bodrova A, Grafeeva N. Coreference resolution using clusterization. In Proceedings of the International FRUCT Conference on Intelligence, Social Media and Web, ISMW FRUCT 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 9-16 https://doi.org/10.1109/FRUCT.2016.7584764

Author

Bodrova, A. ; Grafeeva, N. / Coreference resolution using clusterization. Proceedings of the International FRUCT Conference on Intelligence, Social Media and Web, ISMW FRUCT 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 9-16

BibTeX

@inproceedings{c49d942e8a4d452b9a44e12d69dc2562,
title = "Coreference resolution using clusterization",
abstract = "{\textcopyright} 2016 FRUCT.This work deseribes the experience of ereating a corefarence resolution system for Russian language. Coreference resolution is a key subtask of Information Extraction, and aims to grouping mentions that refer to the same discourse entity. This work was aimed to applying a clusterization algorithm for Russian-language newswire texts. We narrowed the task to Person proper names clusterization. Our approach model included two steps: mention extraction and clusterization. Mention extraction was proceeded by manually-created grammars for Tomita-parser. For mention grouping, we used agglomerative clusterization on entity level with the help of weighted feature vectors. We run our experiments on newswire texts, annotated for factRuEval-2016 competition, organized by Dialogue Evaluation. We compare our results with competitors. As a baseline, we set built-in Tonuta-parser algorithms for name extraction and name clusterization. We got comparable results and outperformed the baseline.",
author = "A. Bodrova and N. Grafeeva",
note = "A. Bodrova and N. Grafeeva, {"}Coreference resolution using clusterization,{"} 2016 International FRUCT Conference on Intelligence, Social Media and Web (ISMW FRUCT), 2016, pp. 1-8, doi: 10.1109/FRUCT.2016.7584764.; 2016 International FRUCT Conference on Intelligence, Social Media and Web, ISMW FRUCT 2016 ; Conference date: 28-08-2016 Through 04-09-2016",
year = "2016",
doi = "10.1109/FRUCT.2016.7584764",
language = "English",
isbn = "978-952-68397-6-9",
pages = "9--16",
booktitle = "Proceedings of the International FRUCT Conference on Intelligence, Social Media and Web, ISMW FRUCT 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

RIS

TY - GEN

T1 - Coreference resolution using clusterization

AU - Bodrova, A.

AU - Grafeeva, N.

N1 - A. Bodrova and N. Grafeeva, "Coreference resolution using clusterization," 2016 International FRUCT Conference on Intelligence, Social Media and Web (ISMW FRUCT), 2016, pp. 1-8, doi: 10.1109/FRUCT.2016.7584764.

PY - 2016

Y1 - 2016

N2 - © 2016 FRUCT.This work deseribes the experience of ereating a corefarence resolution system for Russian language. Coreference resolution is a key subtask of Information Extraction, and aims to grouping mentions that refer to the same discourse entity. This work was aimed to applying a clusterization algorithm for Russian-language newswire texts. We narrowed the task to Person proper names clusterization. Our approach model included two steps: mention extraction and clusterization. Mention extraction was proceeded by manually-created grammars for Tomita-parser. For mention grouping, we used agglomerative clusterization on entity level with the help of weighted feature vectors. We run our experiments on newswire texts, annotated for factRuEval-2016 competition, organized by Dialogue Evaluation. We compare our results with competitors. As a baseline, we set built-in Tonuta-parser algorithms for name extraction and name clusterization. We got comparable results and outperformed the baseline.

AB - © 2016 FRUCT.This work deseribes the experience of ereating a corefarence resolution system for Russian language. Coreference resolution is a key subtask of Information Extraction, and aims to grouping mentions that refer to the same discourse entity. This work was aimed to applying a clusterization algorithm for Russian-language newswire texts. We narrowed the task to Person proper names clusterization. Our approach model included two steps: mention extraction and clusterization. Mention extraction was proceeded by manually-created grammars for Tomita-parser. For mention grouping, we used agglomerative clusterization on entity level with the help of weighted feature vectors. We run our experiments on newswire texts, annotated for factRuEval-2016 competition, organized by Dialogue Evaluation. We compare our results with competitors. As a baseline, we set built-in Tonuta-parser algorithms for name extraction and name clusterization. We got comparable results and outperformed the baseline.

UR - https://ieeexplore.ieee.org/document/7584764/authors#authors

U2 - 10.1109/FRUCT.2016.7584764

DO - 10.1109/FRUCT.2016.7584764

M3 - Conference contribution

SN - 978-952-68397-6-9

SP - 9

EP - 16

BT - Proceedings of the International FRUCT Conference on Intelligence, Social Media and Web, ISMW FRUCT 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 International FRUCT Conference on Intelligence, Social Media and Web

Y2 - 28 August 2016 through 4 September 2016

ER -

ID: 7966426