Turkish Coreference Resolution

Tugba Pamay; Gulsen Eryigit

doi:10.1109/INISTA.2018.8466293

Turkish Coreference Resolution

Tugba Pamay, Gulsen Eryigit

Yapay Zeka ve Veri Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

4 Atıf (Scopus)

Özet

This paper presents the state-of-the-art results in Turkish coreference resolution (CR) which is a task of determining sets of mentions which identify the same real-world entity (e.g. a person, a place, a thing, an event). The proposed system uses support vector machines and solves the CR task with a mention-pair model that basically accepts mention couples and decides on whether they are coreferential with each other or not. The results are evaluated on Marmara Turkish Coreference Corpus by using well-known evaluation metrics (viz. MUC, B³, BLANC and LEA). The introduced approach obtains F1 scores of 90.68% (MUC), 86.89% (B³), 85.13% (BLANC) and 78.34% (LEA) yielding an improvement of 9.12, 16.06, 13.08 and 12.57 percentage points respectively over a recent baseline system on Turkish CR. The paper introduces the system setup (SVM parameters and negative sampling strategy) as well as the selected features and analyzes the impact of these features on the Turkish CR task.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018
Editörler	Plamen Angelov, Tulay Yildirim, Lazaros Iliadis, Yannis Manolopoulos
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781538651506
DOI'lar	https://doi.org/10.1109/INISTA.2018.8466293
Yayın durumu	Yayınlandı - 14 Eyl 2018
Etkinlik	2018 IEEE International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018 - Thessaloniki, Greece Süre: 3 Tem 2018 → 5 Tem 2018

Yayın serisi

Adı	2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2018 IEEE International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018
Ülke/Bölge	Greece
Şehir	Thessaloniki
Periyot	3/07/18 → 5/07/18

Bibliyografik not

Publisher Copyright:
© 2018 IEEE.

Finansman

This work is part of a research project supported by ITU Scientific Research Projects Grant no: MYL-2017-40971.

Finansörler	Finansör numarası
International Technological University	MYL-2017-40971

Belgeye Erişim

10.1109/INISTA.2018.8466293

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Pamay, T., & Eryigit, G. (2018). Turkish Coreference Resolution. In P. Angelov, T. Yildirim, L. Iliadis, & Y. Manolopoulos (Eds.), 2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018 Makale 8466293 (2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INISTA.2018.8466293

Pamay, Tugba ; Eryigit, Gulsen. / Turkish Coreference Resolution. 2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018. editör / Plamen Angelov ; Tulay Yildirim ; Lazaros Iliadis ; Yannis Manolopoulos. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018).

@inproceedings{5409bed3cdd945728c45344cdfcbc127,

title = "Turkish Coreference Resolution",

abstract = "This paper presents the state-of-the-art results in Turkish coreference resolution (CR) which is a task of determining sets of mentions which identify the same real-world entity (e.g. a person, a place, a thing, an event). The proposed system uses support vector machines and solves the CR task with a mention-pair model that basically accepts mention couples and decides on whether they are coreferential with each other or not. The results are evaluated on Marmara Turkish Coreference Corpus by using well-known evaluation metrics (viz. MUC, B3, BLANC and LEA). The introduced approach obtains F1 scores of 90.68% (MUC), 86.89% (B3), 85.13% (BLANC) and 78.34% (LEA) yielding an improvement of 9.12, 16.06, 13.08 and 12.57 percentage points respectively over a recent baseline system on Turkish CR. The paper introduces the system setup (SVM parameters and negative sampling strategy) as well as the selected features and analyzes the impact of these features on the Turkish CR task.",

keywords = "Coreference Resolution, Natural Language Processing, Turkish",

author = "Tugba Pamay and Gulsen Eryigit",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018 ; Conference date: 03-07-2018 Through 05-07-2018",

year = "2018",

month = sep,

day = "14",

doi = "10.1109/INISTA.2018.8466293",

language = "English",

series = "2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Pamay, T & Eryigit, G 2018, Turkish Coreference Resolution. in P Angelov, T Yildirim, L Iliadis & Y Manolopoulos (eds), 2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018., 8466293, 2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018, Thessaloniki, Greece, 3/07/18. https://doi.org/10.1109/INISTA.2018.8466293

Turkish Coreference Resolution. / Pamay, Tugba; Eryigit, Gulsen.
2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018. ed. / Plamen Angelov; Tulay Yildirim; Lazaros Iliadis; Yannis Manolopoulos. Institute of Electrical and Electronics Engineers Inc., 2018. 8466293 (2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - Turkish Coreference Resolution

AU - Pamay, Tugba

AU - Eryigit, Gulsen

PY - 2018/9/14

Y1 - 2018/9/14

N2 - This paper presents the state-of-the-art results in Turkish coreference resolution (CR) which is a task of determining sets of mentions which identify the same real-world entity (e.g. a person, a place, a thing, an event). The proposed system uses support vector machines and solves the CR task with a mention-pair model that basically accepts mention couples and decides on whether they are coreferential with each other or not. The results are evaluated on Marmara Turkish Coreference Corpus by using well-known evaluation metrics (viz. MUC, B3, BLANC and LEA). The introduced approach obtains F1 scores of 90.68% (MUC), 86.89% (B3), 85.13% (BLANC) and 78.34% (LEA) yielding an improvement of 9.12, 16.06, 13.08 and 12.57 percentage points respectively over a recent baseline system on Turkish CR. The paper introduces the system setup (SVM parameters and negative sampling strategy) as well as the selected features and analyzes the impact of these features on the Turkish CR task.

AB - This paper presents the state-of-the-art results in Turkish coreference resolution (CR) which is a task of determining sets of mentions which identify the same real-world entity (e.g. a person, a place, a thing, an event). The proposed system uses support vector machines and solves the CR task with a mention-pair model that basically accepts mention couples and decides on whether they are coreferential with each other or not. The results are evaluated on Marmara Turkish Coreference Corpus by using well-known evaluation metrics (viz. MUC, B3, BLANC and LEA). The introduced approach obtains F1 scores of 90.68% (MUC), 86.89% (B3), 85.13% (BLANC) and 78.34% (LEA) yielding an improvement of 9.12, 16.06, 13.08 and 12.57 percentage points respectively over a recent baseline system on Turkish CR. The paper introduces the system setup (SVM parameters and negative sampling strategy) as well as the selected features and analyzes the impact of these features on the Turkish CR task.

KW - Coreference Resolution

KW - Natural Language Processing

KW - Turkish

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M3 - Conference contribution

AN - SCOPUS:85055570942

T3 - 2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018

BT - 2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018

A2 - Angelov, Plamen

A2 - Yildirim, Tulay

A2 - Iliadis, Lazaros

A2 - Manolopoulos, Yannis

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018

Y2 - 3 July 2018 through 5 July 2018

ER -

Pamay T, Eryigit G. Turkish Coreference Resolution. In Angelov P, Yildirim T, Iliadis L, Manolopoulos Y, editörler, 2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8466293. (2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018). doi: 10.1109/INISTA.2018.8466293

Turkish Coreference Resolution

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap