Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Arabian Journal for Science and Engineering
Erschienen in: Arabian Journal for Science and Engineering 8/2022

02.12.2021 | Research Article-Computer Engineering and Computer Science

Improving Phrase Chunking by using Contextualized Word Embeddings for a Morphologically Rich Language

verfasst von: Toqeer Ehsan, Javairia Khalid, Saadia Ambreen, Asad Mustafa, Sarmad Hussain

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 8/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Phrase chunking is an important task in various natural language processing (NLP) applications. This paper presents a neural phrase chunking for Urdu by training contextualized word representations. This work also produces an annotated corpus. The annotation has been performed by using IOB (inside-outside-begin) labels. Comprehensive guidelines have been developed for four phrases which are noun phrase (NP), verb phrase (VP), post-positional phrase (PP) and prepositional phrase (PRP). The annotated text has been evaluated for completeness and correctness automatically. Inter-annotator agreement has been calculated for ten percent reference corpus. A neural chunker has been developed and trained on the annotated corpus. The chunker is based on long–short- term memory networks. Transfer learning has been employed to improve the chunking results. For that purpose, context-free (Word2Vec) and contextualized (ELMo) word representations have been trained. The chunker performed with an f-score of 94.9 when trained by using third layer of ELMo embeddings.
Vorheriger Artikel A Distributed Data Storage Strategy Based on LOPs
Nächster Artikel AI-Based Mobile Edge Computing for IoT: Applications, Challenges, and Future Scope

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
Literatur
1.
Eberhard, D.M.; Simons, G.F.; Fennig, C.D.: Ethnologue: Languages of the World . SIL International (2019)
2.
Bögel, T.; Butt, M.; Hautli, A.; Sulger, S.: Developing a Finite-State Morphological Analyzer for Urdu and Hindi. Universität Potsdam (2008)
3.
Hussain, S.: Finite-State Morphological Analyzer for Urdu. Unpublished MS thesis, Center for Research in Urdu Language Processing, National University of Computer and Emerging Sciences, Pakistan (2004)
4.
Butt, M.: The Structure of Complex Predicates in Urdu. Center for the Study of Language (CSLI) (1995)
5.
Butt, M.; Ramchand, G.: Complex Aspectual Structure in Hindi/Urdu. M. Liakata, B. Jensen, D. Maillat, Eds, 1–30 (2001)
6.
Khan, T.A.: Spatial Expressions and Case in South Asian Languages. PhD thesis (2009)
7.
Butt, M.; King, T.H.: The Status of Case. In: Clause Structure in South Asian Languages, pp. 153–198. Springer (2004)
8.
Raza, G.; Ahmed, T.; Butt, M.; King, T.H.: Argument Scrambling within Urdu NPs. Proceedings of LFG11, 461 (2011)
9.
Carreras, X.; Marquez, L.: Phrase Recognition by Filtering and Ranking with Perceptrons. Recent advances in natural language processing III: selected papers from RANLP 2003 260, 205 (2004)
10.
Etzioni, O.; Banko, M.; Soderland, S.; Weld, D.S.: Open information extraction from the web. Commun. ACM 51(12), 68–74 (2008)CrossRef
11.
Ahmed, T.; Urooj, S.; Hussain, S.; Mustafa, A.; Parveen, R.; Adeeba, F.; Hautli, A.; Butt, M.: The CLE Urdu POS Tagset. In: LREC 2014, Ninth International Conference on Language Resources and Evaluation, pp. 2920–2925 (2015)
12.
Ali, W.; Malik, M.K.; Hussain, S.; Siddiq, S.; Ali, A.: Urdu Noun Phrase Chunking: HMM based approach. In: 2010 International Conference on Educational and Information Technology, vol. 2, pp. 2–494 (2010). IEEE
13.
Ali, W.; Hussain, S.: A Hybrid Approach to Urdu Verb Phrase Chunking. In: Proceedings of the Eighth Workshop on Asian Language Resouces, pp. 137–143 (2010)
14.
Asopa, S.; Asopa, P.; Mathur, I.; Joshi, N.: Rule based Chunker for Hindi. In: 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), pp. 442–445 (2016). IEEE
15.
Ehsani, R.; Solak, E.; Yıldız, O.T.: Hybrid Chunking for Turkish Combining Morphological and Semantic Features
16.
Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G.; Dean, J.: Distributed Representations of Words and Phrases and their Compositionality. arXiv preprint arXiv:​1310.​4546 (2013)
17.
Park, S.-B.; Zhang, B.-T.: Text Chunking by Combining Hand-crafted Rules and Memory-based Learning. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pp. 497–504 (2003)
18.
Le Nguyen, M.; Nguyen, H.T.; Nguyen, P.-T.; Ho, T.-B.; Shimazu, A.: An Empirical Study of Vietnamese Noun Phrase Chunking with Discriminative Sequence Models. In: Proceedings of the 7th Workshop on Asian Language Resources (ALR7), pp. 9–16 (2009)
19.
Knutsson, O.; Bigert, J.; Kann, V.: A Robust Shallow Parser for Swedish. In: Proceedings of Nodalida, vol. 2003, p. 2003 (2003)
20.
Diab, M.; Hacioglu, K.; Jurafsky, D.: Automatic Tagging of Arabic Text: From Raw Text to Base Phrase Chunks. In: Proceedings of HLT-NAACL 2004: Short Papers, pp. 149–152 (2004)
21.
Eiselen, R.: South African Language Resources: Phrase Chunking. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pp. 689–693 (2016)
22.
23.
Gharaibeh, I.K.: Development of Arabic Noun Phrase Extractor (ANPE). International Journal on Natural Language Computing (IJNLC) Vol 6 (2017)
24.
Prathibba, R.; Padma, M.: Shallow parser for Kannada sentences using machine learning approach. Int. J. Comput. Linguistic. Res. 8(4), 158–170 (2017)
25.
Sun, X.; Nan, X.: Chinese Base Phrases Chunking Based on Latent Semi-CRF Model. In: Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering (NLPKE-2010), pp. 1–7 (2010). IEEE
26.
Sun, X.; Nan, X.: Chinese Noun Phrases Chunking: A Latent Discriminative Model with Global Features. In: 2011 14th IEEE International Conference on Computational Science and Engineering, pp. 167–172 (2011). IEEE
27.
Sarkar, K.; Gayen, V.: Bengali Noun Phrase Chunking Based on Conditional Random Fields. In: 2014 2nd International Conference on Business and Information Management (ICBIM), pp. 148–153 (2014). IEEE
28.
Pawar, S.; Ramrakhiyani, N.; Palshikar, G.; Bhattacharyya, P.; Hingmire, S.: Noun Phrase Chunking for Marathi using Distant Supervision. In: Proceedings of the 12th International Conference on Natural Language Processing, pp. 29–38 (2015)
29.
Sassano, M.; Kurohashi, S.: A Unified Single Scan Algorithm for Japanese Base Phrase Chunking and Dependency Parsing. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pp. 49–52 (2009)
30.
Supnithi, T.; Onman, C.; Porkaew, P.; Ruangrajitpakorn, T.; Trakultaweekoon, K.; Kawtrakul, A.: A Supervised Learning based Chunking in Thai using Categorial Grammar. In: Proceedings of the Eighth Workshop on Asian Language Resouces, pp. 129–136 (2010)
31.
Nongmeikapam, K.; Chingangbam, C.; Keisham, N.; Varte, B.; Bandopadhyay, S.: Chunking in Manipuri using CRF. Int. J. Nat. Lang. Comput. (IJNLC) 3(3) (2014)
32.
Aung, M.P.; Moe, A.L.: New phrase chunking algorithm for Myanmar natural language processing. In: Applied Mechanics and Materials, vol. 695, pp. 548–552 (2015). Trans Tech Publications
33.
Ehsan, T.; Hussain, S.: Development and evaluation of an Urdu treebank (CLE-UTB) and a statistical parser. Language Resourc. Eval. 1–40 (2020)
34.
Ehsan, T.; Hussain, S.: Analysis of experiments on statistical and neural parsing for a morphologically rich and free word order language Urdu. IEEE Access 7, 161776–161793 (2019)CrossRef
35.
Ahmed, T.; Ehsan, T.; Ashraf, A.; u Rahman, M.; Hussain, S.; Butt, M.: A Multilayered Urdu Treebank. In: International Conference on Language and Technology (CLT 2020) (2020)
36.
Ehsan, T.; Butt, M.: Dependency parsing for Urdu: resources, conversions and learning. In: Proceedings of The 12th Language Resources and Evaluation Conference, pp. 5202–5207 (2020)
37.
Kamran Malik, M.; Ahmed, T.; Sulger, S.; Bögel, T.; Gulzar, A.; Raza, G.; Hussain, S.; Butt, M.: Transliterating Urdu for a Broad-Coverage Urdu/Hindi LFG Grammar. In: LREC 2010, Seventh International Conference on Language Resources and Evaluation, pp. 2921–2927 (2010)
38.
Jespersen, O.: A Modern English Grammar on Historical Principles, vol. 3. Routledge (2013)
39.
Gómez, I.P.: Nominal Modifiers in Noun Phrase Structure: Evidence from Contemporary English. University of Santiago de Compostela (2010)
40.
Bharati, A.; Sangal, R.; Sharma, D.M.; Bai, L.: Anncorra: Annotating Corpora Guidelines for POS and Chunk Annotation for Indian Languages. LTRC-TR31, 1–38 (2006)
41.
Bhatt, R.; Farudi, A.; Rambow, O.: Hindi-Urdu Phrase Structure Annotation Guidelines (2013)
42.
Anwar, B.: Urdu-English code switching: the use of Urdu phrases and clauses in Pakistani English (a non-native variety). Int J Language Stud 3(4) (2009)
43.
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960)CrossRef
44.
Adeeba, F.; Akram, Q.; Khalid, H.; Hussain, S.: Cle Urdu Books N-Grams. In: Conference on Language and Technology (2014)
45.
46.
Peters, M.E.; Neumann, M.; Iyyer, M.; Gardner, M.; Clark, C.; Lee, K.; Zettlemoyer, L.: Deep contextualized word representations. arXiv preprint arXiv:​1802.​05365 (2018)
Metadaten
Titel
Improving Phrase Chunking by using Contextualized Word Embeddings for a Morphologically Rich Language
verfasst von
Toqeer Ehsan
Javairia Khalid
Saadia Ambreen
Asad Mustafa
Sarmad Hussain
Publikationsdatum
02.12.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Arabian Journal for Science and Engineering / Ausgabe 8/2022
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-021-06343-7

Weitere Artikel der Ausgabe 8/2022

Arabian Journal for Science and Engineering 8/2022 Zur Ausgabe

Research Article-Computer Engineering and Computer Science

A Distributed Data Storage Strategy Based on LOPs

Research Article-Computer Engineering and Computer Science

IRText: An Item Response Theory-Based Approach for Text Categorization

Research Article-Computer Engineering and Computer Science

A New Approach for Congestive Heart Failure and Arrhythmia Classification Using Angle Transformation with LSTM

Research Article-Computer Engineering and Computer Science

A Ship Detection Method in Complex Background Via Mixed Attention Model

Research Article-Computer Engineering and Computer Science

QCA-Based Adder for Redundant Binary Signed Digit Numbers

Research Article-Computer Engineering and Computer Science

A Hybrid Approach of Spotted Hyena Optimization Integrated with Quadratic Approximation for Training Wavelet Neural Network

Premium Partner

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

    Zur Marktübersicht
    Bildnachweise