Learning Morphology of Natural Language as a Finite-state Grammar

Javad Nouri; Roman Yangarber

doi:10.1007/978-3-319-68456-7_4

Learning Morphology of Natural Language as a Finite-state Grammar

Tietojenkäsittelytieteen osasto

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

Abstrakti

We present algorithms that learn to segment words in morphologically
rich languages, in an unsupervised fashion. Morphology of many languages can
be modeled by finite state machines (FSMs). We start with a baseline MDL-
based learning algorithm. We then formulate well-motivated and general linguistic
principles about morphology, and incorporate them into the algorithm as heuristics,
to constrain the search space. We evaluate the algorithm on three highly-inflecting
languages. Evaluation of segmentation shows gains in performance compared to
the state of the art. We conclude with a discussion about how the learned model
relates to a morphological FSM, which is the ultimate goal.

Alkuperäiskieli	englanti
Otsikko	Statistical Language and Speech Processing : 5th International Conference, SLSP 2017, Le Mans, France, October 23-25, 2017, Proceedings
Toimittajat	Nathalie Camelin, Yannick Estève, Carlos Martín-Vide
Julkaisupaikka	Cham
Kustantaja	Springer International Publishing AG
Julkaisupäivä	27 syysk. 2017
Sivut	44-57
ISBN (painettu)	978-3-319-68455-0
ISBN (elektroninen)	978-3-319-68456-7
DOI - pysyväislinkit	https://doi.org/10.1007/978-3-319-68456-7_4
Tila	Julkaistu - 27 syysk. 2017
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	International Conference on Statistical Language and Speech Processing - Le Mans, Ranska Kesto: 23 lokak. 2017 → 25 lokak. 2017 Konferenssinumero: 5

Julkaisusarja

Nimi	Lecture Notes in Artificial Intelligence
Kustantaja	Springer International Publishing AG
Vuosikerta	10583
ISSN (painettu)	0302-9743
ISSN (elektroninen)	1611-3349

Tieteenalat

113 Tietojenkäsittely- ja informaatiotieteet

Pääsy asiakirjaan

10.1007/978-3-319-68456-7_4

LLL: Language Learning Lab
Yangarber, R. (Projektinjohtaja), Katinskaia, A. (Osallistuja), Hou, J. (Osallistuja), Furlan, G. (Osallistuja) & Kylliäinen, I. P. (Osallistuja)
Projekti: Tutkimusprojekti
Revita: Language learning and AI
Yangarber, R. (Projektinjohtaja), Katinskaia, A. (Osallistuja), Hou, J. (Osallistuja), Furlan, G. (Osallistuja) & Kylliäinen, I. P. (Osallistuja)
Projekti: Tutkimusprojekti

Siteeraa tätä

Nouri, J., & Yangarber, R. (2017). Learning Morphology of Natural Language as a Finite-state Grammar. teoksessa N. Camelin, Y. Estève, & C. Martín-Vide (Toimittajat), Statistical Language and Speech Processing: 5th International Conference, SLSP 2017, Le Mans, France, October 23-25, 2017, Proceedings (Sivut 44-57). (Lecture Notes in Artificial Intelligence; Vuosikerta 10583). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-68456-7_4

Nouri, Javad ; Yangarber, Roman. / Learning Morphology of Natural Language as a Finite-state Grammar. Statistical Language and Speech Processing: 5th International Conference, SLSP 2017, Le Mans, France, October 23-25, 2017, Proceedings. Toimittaja / Nathalie Camelin ; Yannick Estève ; Carlos Martín-Vide. Cham : Springer International Publishing AG, 2017. Sivut 44-57 (Lecture Notes in Artificial Intelligence).

@inproceedings{d252d6d2dc6d4c0fac495815b5d3764a,

title = "Learning Morphology of Natural Language as a Finite-state Grammar",

abstract = "We present algorithms that learn to segment words in morphologicallyrich languages, in an unsupervised fashion. Morphology of many languages canbe modeled by finite state machines (FSMs). We start with a baseline MDL-based learning algorithm. We then formulate well-motivated and general linguisticprinciples about morphology, and incorporate them into the algorithm as heuristics,to constrain the search space. We evaluate the algorithm on three highly-inflectinglanguages. Evaluation of segmentation shows gains in performance compared tothe state of the art. We conclude with a discussion about how the learned modelrelates to a morphological FSM, which is the ultimate goal.",

keywords = "113 Computer and information sciences, finite-state automata, unsupervised morphology induction, minimum description length principle, MDL",

author = "Javad Nouri and Roman Yangarber",

year = "2017",

month = sep,

day = "27",

doi = "10.1007/978-3-319-68456-7_4",

language = "English",

isbn = "978-3-319-68455-0",

series = "Lecture Notes in Artificial Intelligence",

publisher = "Springer International Publishing AG",

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editor = "Nathalie Camelin and Yannick Est{\`e}ve and Carlos Mart{\'i}n-Vide",

booktitle = "Statistical Language and Speech Processing",

address = "Switzerland",

note = "International Conference on Statistical Language and Speech Processing, SLSP ; Conference date: 23-10-2017 Through 25-10-2017",

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Nouri, J & Yangarber, R 2017, Learning Morphology of Natural Language as a Finite-state Grammar. julkaisussa N Camelin, Y Estève & C Martín-Vide (toim), Statistical Language and Speech Processing: 5th International Conference, SLSP 2017, Le Mans, France, October 23-25, 2017, Proceedings. Lecture Notes in Artificial Intelligence, Vuosikerta 10583, Springer International Publishing AG, Cham, Sivut 44-57, International Conference on Statistical Language and Speech Processing, Le Mans, Ranska, 23/10/2017. https://doi.org/10.1007/978-3-319-68456-7_4

Learning Morphology of Natural Language as a Finite-state Grammar. / Nouri, Javad; Yangarber, Roman.
Statistical Language and Speech Processing: 5th International Conference, SLSP 2017, Le Mans, France, October 23-25, 2017, Proceedings. toim. / Nathalie Camelin; Yannick Estève; Carlos Martín-Vide. Cham: Springer International Publishing AG, 2017. s. 44-57 (Lecture Notes in Artificial Intelligence; Vuosikerta 10583).

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

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AU - Yangarber, Roman

N1 - Conference code: 5

PY - 2017/9/27

Y1 - 2017/9/27

N2 - We present algorithms that learn to segment words in morphologicallyrich languages, in an unsupervised fashion. Morphology of many languages canbe modeled by finite state machines (FSMs). We start with a baseline MDL-based learning algorithm. We then formulate well-motivated and general linguisticprinciples about morphology, and incorporate them into the algorithm as heuristics,to constrain the search space. We evaluate the algorithm on three highly-inflectinglanguages. Evaluation of segmentation shows gains in performance compared tothe state of the art. We conclude with a discussion about how the learned modelrelates to a morphological FSM, which is the ultimate goal.

AB - We present algorithms that learn to segment words in morphologicallyrich languages, in an unsupervised fashion. Morphology of many languages canbe modeled by finite state machines (FSMs). We start with a baseline MDL-based learning algorithm. We then formulate well-motivated and general linguisticprinciples about morphology, and incorporate them into the algorithm as heuristics,to constrain the search space. We evaluate the algorithm on three highly-inflectinglanguages. Evaluation of segmentation shows gains in performance compared tothe state of the art. We conclude with a discussion about how the learned modelrelates to a morphological FSM, which is the ultimate goal.

KW - 113 Computer and information sciences

KW - finite-state automata

KW - unsupervised morphology induction

KW - minimum description length principle

KW - MDL

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DO - 10.1007/978-3-319-68456-7_4

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T3 - Lecture Notes in Artificial Intelligence

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EP - 57

BT - Statistical Language and Speech Processing

A2 - Camelin, Nathalie

A2 - Estève, Yannick

A2 - Martín-Vide, Carlos

PB - Springer International Publishing AG

CY - Cham

T2 - International Conference on Statistical Language and Speech Processing

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Nouri J, Yangarber R. Learning Morphology of Natural Language as a Finite-state Grammar. julkaisussa Camelin N, Estève Y, Martín-Vide C, toimittajat, Statistical Language and Speech Processing: 5th International Conference, SLSP 2017, Le Mans, France, October 23-25, 2017, Proceedings. Cham: Springer International Publishing AG. 2017. s. 44-57. (Lecture Notes in Artificial Intelligence). doi: 10.1007/978-3-319-68456-7_4

Learning Morphology of Natural Language as a Finite-state Grammar

Abstrakti

Julkaisusarja

Tieteenalat

Pääsy asiakirjaan

Projektit

LLL: Language Learning Lab

Revita: Language learning and AI

Siteeraa tätä