Named Entity Recognition Experiments on Turkish Texts

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Named Entity Recognition Experiments on Turkish Texts

Dilek Küçük

1

and Adnan Yazıcı

2

1

TÜBİTAK - Uzay Institute, Ankara - Turkey dilek.kucuk@uzay.tubitak.gov.tr

2

Dept. of Computer Engineering, METU, Ankara - Turkey

yazici@ceng.metu.edu.tr

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Outline

 Introduction

 Named Entity Recognition in Turkish

 Evaluation

 Evaluation on News Texts

 Evaluation on Child Stories and Historical Texts

 Evaluation on Video Texts

 Future Work

Conclusion

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Introduction [1]

 Named entity recognition (NER) is one of the main information extraction (IE) tasks

 recognition of names of people, locations,

organizations as well as temporal and numeric expressions in texts (Nadeau and Sekine, 2007).

 NER task is known to be a solved problem

especially for English with state-of-the-art

performance above 90 %.

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Introduction [2]

 NER research in Turkish is known to be rare.

 Language-independent IE system (Cucerzan and Yarowsky, 1999)

 Statistical name tagger for Turkish (Tür et al, 2003)

 Person name tagger for financial news texts (Bayraktar and Taşkaya-Temizel, 2008)

 Person mention extractor and a string matching based

coreference resolver (Küçük and Yazıcı, 2008)

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Introduction [3]

 In this study, we present a rule-based system for named entity recognition from Turkish texts.

 Proposed for the domain of news texts.

 Evaluated on

 Newswire texts

 Child stories and historical texts

 News video transcriptions

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Named Entity Recognition in Turkish [1]

 The domain is determined as news texts.

 News texts from METU Turkish corpus (Say et al., 2002) are examined.

 Capitalization and punctuation clues are not utilized

 Since they may be missing in automatic speech

recognition (ASR) outputs and texts obtained

from the Web.

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Named Entity Recognition in Turkish [2]

 A set of information sources has been compiled.

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Named Entity Recognition in Turkish [3]

 The lexical resources include

 a dictionary of person names in Turkish comprising about 8300 entries,

 a list of well-known political people,

 a list of well-known locations (the names of cities and towns) in Turkey as well as in the world,

 a list of well-known organizations in Turkey and

those in the world.

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Named Entity Recognition in Turkish [4]

 Pattern bases for the extraction of location/organization names as well as that of the numeric/temporal expressions.

 The system makes use of a simple morphological analyzer to

validate candidates.

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Evaluation [1]

 The system tags its output with Message

Understanding Conference (MUC) style named entity tags:

ENAMEX, TIMEX, and NUMEX

 An annotation tool is developed to annotate the evaluation texts with the same tags to create

answer sets.

 Evaluation is performed by comparing the

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Evaluation [2]

 The Annotation Tool

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Evaluation [3]

 Evaluation is performed in terms of precision, recall,

and f-measure

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Evaluation on News Text [1]

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Evaluation on News Text [2]

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Evaluation on News Text [3]

The precision of person name recognition using only a dictionary of person names turns out to be too low.

Savaş („war‟), barış („peace‟), özen („care‟)…

During location and organization name recognition, the system performs erroneous extractions.

anlatmanın yolu (the way to tell),

ilk üniversitesi (first university)…

Organization name recognition also suffers from the erroneous extractions in case of compound organization names.

İstanbul Üniversitesi Siyasal Bilgiler Fakültesi…

„İstanbul University Political Science Faculty‟

as „İstanbul Üniversitesi‟ and „Bilgiler Fakültesi‟

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Evaluation on News Text [4]

 As opposed to the statistical system (Tür et al., 2003), the rule based system considers numeric and temporal expressions

in addition to the person, location, organization names.

 The statistical system has been trained on a set of news articles with 492821 words (37277 NEs).

 The statistical system has been tested on a news article set of about 28000 words (2197 NEs) and has achieved a best performance of 91.56 % in f-measure.

 The rule-based system has been tested on a set of 20131 words (1591 NEs) and achieved an f-measure of 78.7 %.

The statistical system performs deeper language

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Evaluation on Child Stories and Historical Texts [1]

 The child stories set comprises two stories by the same author (Ilgaz, 2003a-b).

 The historical text includes the first three chapters of a

book describing five cities mostly on their historical basis

(Tanpınar, 2007).

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Evaluation on Child Stories and Historical Texts [2]

The main problem for child stories data set is the existence of foreign person names throughout the stories.

The performance drop for historical text is due to the

nonexistence of historical person names and organizations (such as the names of empires) in the lexical resources.

The results are in line with the well-known finding that rule-

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Evaluation on Video Texts [1]

 An important research area which can benefit from IE techniques is automatic multimedia annotation.

Several studies are carried out on employing especially NER output for semantic multimedia annotation.

Multimedia indexing system for English, German and Dutch football videos (Saggion et al., 2004)

Video annotation system for Italian news videos (Basili et al., 2005)

Automatic annotation system for BBC radio and TV news

(Dowman et al., 2005)

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Evaluation on Video Texts [2]

 We have compiled a video data set of Turkish news videos

 From the Web site of Turkish Radio and Television Company (TRT).

 Comprising 16 videos with a total duration of two hours.

 The videos are manually transcribed leading to a text of 9804 words

Since no general purpose automatic speech recognizer

exists for Turkish.

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Evaluation on Video Texts [3]

 The transcription text is annotated with named entity tags

resulting in 1090 named entities (256 person, 479 location, and 222 organization names, 70 numeric and 63 temporal expressions).

 Evaluation of the recognizer on the text resulted in a

precision of 73.3%, a recall of 77.0%, and so an f-measure of 75.1%.

 The results on video transcriptions are satisfactory for a first attempt of named entity recognition on genuine video texts

 It is significant step towards the employment of IE

techniques for semantic annotation of videos in Turkish.

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Future Work

 Future work based on the current study includes

 Improvement of the system benefiting from the error analyses.

 Extending the system to output finer grained named entity classes employing a named entity ontology.

 Employment of machine learning algorithms for the NER task

 The results can be compared with that of the rule

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Conclusion [1]

 Information extraction in Turkish is a rarely studied research area.

 In this study, we have presented a rule-based system for named entity recognition from Turkish texts.

 Initially engineered for news texts.

 Employs a set of lexical resources and pattern bases.

 Being a rule-based system, needs no training data.

 Evaluated on diverse text types including news texts, child stories, historical texts, and news video

transcriptions.

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Conclusion [2]

 The evaluation results for the news texts and news video transcriptions are satisfactory for a first attempt

 Yet, the results for child stories and historical texts are very low.

 In line with the finding that rule-based IE systems

suffer from considerable performance drop when

evaluated on other domains.

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References [1]

1.

Roberto Basili, Marco Cammisa, and Emanuale Donati. RitroveRAI: A web application for semantic indexing and hyperlinking of multimedia news. In Proceedings of International Semantic Web Conference, 2005.

2.

Özkan Bayraktar and Tuğba Taşkaya-Temizel. Person name extraction from Turkish Financial news text using local grammar based approach.

In Proceedings of the International Symposium on Computer and Information Sciences, 2008.

3.

Silviu Cucerzan and David Yarowsky. Language independent named

entity recognition combining morphological and contextual evidence. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, 1999.

4.

Mike Dowman, Valentin Tablan, Hamish Cunningham, and Borislav Popov. Web-assisted annotation, semantic indexing and search of

television and radio news. In Proceedings of the International Conference on World Wide Web (WWW), 2005.

5.

Rıfat Ilgaz. Bacaksız Kamyon Sürücüsü. Çınar Publications, 2003.

6.

Rıfat Ilgaz. Bacaksız Tatil Köyünde. Çınar Publications, 2003.

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References [2]

7.

Dilek Küçük and Adnan Yazıcı. Identification of coreferential chains in video texts for semantic annotation of news videos. In Proceedings of the International Symposium on Computer and Information Sciences, 2008.

8.

David Nadeau and Satoshi Sekine. “A Survey of Named Entity

Recognition and Classification”, Linguistica Investigationes, 2007, vol.

30, no. 1, pp.3-26.

9.

Bilge Say, Deniz Zeyrek, Kemal Oflazer, and Umut Özge. Development of a corpus and a treebank for present-day written Turkish. In Proceedings of the 11th International Conference of Turkish Linguistics (ICTL), 2002.

10.

Ahmet Hamdi Tanpınar. Beş Şehir. Dergah Publications, 2007.

11.

Horacio Saggion, Hamish Cunningham, Kalina Bontcheva, Diana

Maynard, Oana Hamza, and Yorick Wilks. Multimedia indexing through multi-source and multi-language information extraction: MUMIS project.

Data and Knowledge Engineering, 48:247-264, 2004.

12.

Gökhan Tür, Dilek Hakkani-Tür, and Kemal Oflazer. A statistical

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