HFST-SweNER – A New NER Resource for Swedish

Dimitrios Kokkinakis, Jyrki Niemi, Sam Hardwick, Krister Linden, Lars Borin

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

Kuvaus

Named entity recognition (NER) is a knowledge-intensive information extraction task that is used for recognizing textual mentions of entities that belong to a predefined set of categories, such as locations, organizations and time expressions. NER is a challenging, difficult, yet essential preprocessing technology for many natural language processing applications, and particularly crucial for language understanding. NER has been actively explored in academia and in industry especially during the last years due to the advent of social media data. This paper describes the conversion, modeling and adaptation of a Swedish NER system from a hybrid environment, with integrated functionality from various processing components, to the Helsinki Finite-State Transducer Technology (HFST) platform. This new HFST-based NER (HFST-SweNER) is a full-fledged open source implementation that supports a variety of generic named entity types and consists of multiple, reusable resource layers, e.g., various n-gram-based named entity lists (gazetteers).
Alkuperäiskielienglanti
OtsikkoProceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
ToimittajatNicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Sivumäärä7
JulkaisupaikkaReykjavik, Iceland
KustantajaEuropean Language Resources Association (ELRA)
Julkaisupäivä26 toukokuuta 2014
Artikkeli no #391
ISBN (painettu)978-2-9517408-8-4
ISBN (elektroninen)978-2-9517408-8-4
TilaJulkaistu - 26 toukokuuta 2014
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaLanguage and Resource Evaluation Conference - Reykjavik, Islanti
Kesto: 26 toukokuuta 201431 toukokuuta 2014
Konferenssinumero: 9

Lisätietoja


Volume:
Proceeding volume:

Tieteenalat

  • 113 Tietojenkäsittely- ja informaatiotieteet
  • 6121 Kielitieteet

Lainaa tätä

Kokkinakis, D., Niemi, J., Hardwick, S., Linden, K., & Borin, L. (2014). HFST-SweNER – A New NER Resource for Swedish. teoksessa N. Calzolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, ... S. Piperidis (Toimittajat), Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14) [ #391] Reykjavik, Iceland: European Language Resources Association (ELRA).
Kokkinakis, Dimitrios ; Niemi, Jyrki ; Hardwick, Sam ; Linden, Krister ; Borin, Lars. / HFST-SweNER – A New NER Resource for Swedish. Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14). Toimittaja / Nicoletta Calzolari ; Khalid Choukri ; Thierry Declerck ; Hrafn Loftsson ; Bente Maegaard ; Joseph Mariani ; Asuncion Moreno ; Jan Odijk ; Stelios Piperidis. Reykjavik, Iceland : European Language Resources Association (ELRA), 2014.
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title = "HFST-SweNER – A New NER Resource for Swedish",
abstract = "Named entity recognition (NER) is a knowledge-intensive information extraction task that is used for recognizing textual mentions of entities that belong to a predefined set of categories, such as locations, organizations and time expressions. NER is a challenging, difficult, yet essential preprocessing technology for many natural language processing applications, and particularly crucial for language understanding. NER has been actively explored in academia and in industry especially during the last years due to the advent of social media data. This paper describes the conversion, modeling and adaptation of a Swedish NER system from a hybrid environment, with integrated functionality from various processing components, to the Helsinki Finite-State Transducer Technology (HFST) platform. This new HFST-based NER (HFST-SweNER) is a full-fledged open source implementation that supports a variety of generic named entity types and consists of multiple, reusable resource layers, e.g., various n-gram-based named entity lists (gazetteers).",
keywords = "113 Computer and information sciences, finite-state transducers, 6121 Languages, named entity recognition, Swedish",
author = "Dimitrios Kokkinakis and Jyrki Niemi and Sam Hardwick and Krister Linden and Lars Borin",
note = "Volume: Proceeding volume:",
year = "2014",
month = "5",
day = "26",
language = "English",
isbn = "978-2-9517408-8-4",
editor = "Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)",
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Kokkinakis, D, Niemi, J, Hardwick, S, Linden, K & Borin, L 2014, HFST-SweNER – A New NER Resource for Swedish. julkaisussa N Calzolari, K Choukri, T Declerck, H Loftsson, B Maegaard, J Mariani, A Moreno, J Odijk & S Piperidis (toim), Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)., #391, European Language Resources Association (ELRA), Reykjavik, Iceland, Language and Resource Evaluation Conference, Reykjavik, Islanti, 26/05/2014.

HFST-SweNER – A New NER Resource for Swedish. / Kokkinakis, Dimitrios; Niemi, Jyrki; Hardwick, Sam; Linden, Krister; Borin, Lars.

Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14). toim. / Nicoletta Calzolari; Khalid Choukri; Thierry Declerck; Hrafn Loftsson; Bente Maegaard; Joseph Mariani; Asuncion Moreno; Jan Odijk; Stelios Piperidis. Reykjavik, Iceland : European Language Resources Association (ELRA), 2014. #391.

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

TY - GEN

T1 - HFST-SweNER – A New NER Resource for Swedish

AU - Kokkinakis, Dimitrios

AU - Niemi, Jyrki

AU - Hardwick, Sam

AU - Linden, Krister

AU - Borin, Lars

N1 - Volume: Proceeding volume:

PY - 2014/5/26

Y1 - 2014/5/26

N2 - Named entity recognition (NER) is a knowledge-intensive information extraction task that is used for recognizing textual mentions of entities that belong to a predefined set of categories, such as locations, organizations and time expressions. NER is a challenging, difficult, yet essential preprocessing technology for many natural language processing applications, and particularly crucial for language understanding. NER has been actively explored in academia and in industry especially during the last years due to the advent of social media data. This paper describes the conversion, modeling and adaptation of a Swedish NER system from a hybrid environment, with integrated functionality from various processing components, to the Helsinki Finite-State Transducer Technology (HFST) platform. This new HFST-based NER (HFST-SweNER) is a full-fledged open source implementation that supports a variety of generic named entity types and consists of multiple, reusable resource layers, e.g., various n-gram-based named entity lists (gazetteers).

AB - Named entity recognition (NER) is a knowledge-intensive information extraction task that is used for recognizing textual mentions of entities that belong to a predefined set of categories, such as locations, organizations and time expressions. NER is a challenging, difficult, yet essential preprocessing technology for many natural language processing applications, and particularly crucial for language understanding. NER has been actively explored in academia and in industry especially during the last years due to the advent of social media data. This paper describes the conversion, modeling and adaptation of a Swedish NER system from a hybrid environment, with integrated functionality from various processing components, to the Helsinki Finite-State Transducer Technology (HFST) platform. This new HFST-based NER (HFST-SweNER) is a full-fledged open source implementation that supports a variety of generic named entity types and consists of multiple, reusable resource layers, e.g., various n-gram-based named entity lists (gazetteers).

KW - 113 Computer and information sciences

KW - finite-state transducers

KW - 6121 Languages

KW - named entity recognition

KW - Swedish

M3 - Conference contribution

SN - 978-2-9517408-8-4

BT - Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

A2 - Calzolari, Nicoletta

A2 - Choukri, Khalid

A2 - Declerck, Thierry

A2 - Loftsson, Hrafn

A2 - Maegaard, Bente

A2 - Mariani, Joseph

A2 - Moreno, Asuncion

A2 - Odijk, Jan

A2 - Piperidis, Stelios

PB - European Language Resources Association (ELRA)

CY - Reykjavik, Iceland

ER -

Kokkinakis D, Niemi J, Hardwick S, Linden K, Borin L. HFST-SweNER – A New NER Resource for Swedish. julkaisussa Calzolari N, Choukri K, Declerck T, Loftsson H, Maegaard B, Mariani J, Moreno A, Odijk J, Piperidis S, toimittajat, Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14). Reykjavik, Iceland: European Language Resources Association (ELRA). 2014. #391