Turkish Language Resources: Morphological Parser, Morphological Disambiguator and Web Corpus

Sak, Haşim; Güngör, Tunga; Saraçlar, Murat

doi:10.1007/978-3-540-85287-2_40

Cited by 96 publications

(79 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We apply morphological analyse and disambiguation processes on data after tokenization. Oflazer's tool [21] is used for morphological analyse and Sak's tool [22] for morphological disambiguation. Morphological process is used for to extract stem, inflectional suffixes, part of speech, noun case and proper name case information of tokens, all of these information are used when training the model.…”

Section: Datasets and Methodsmentioning

confidence: 99%

Recognizing Named Entities in Turkish Tweets

Eken¹,

Tantuğ²

2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

show abstract

Section: Datasets and Methodsmentioning

confidence: 99%

Recognizing Named Entities in Turkish Tweets

Eken¹,

Tantuğ²

2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

show abstract

“…These parses are generated by a morphological analyzer [6,7]. In morphologically rich languages like Turkish, the number of possible parses for a given word is generally more than one.…”

Section: Morphological Disambiguationmentioning

confidence: 99%

A Novel Approach to Morphological Disambiguation for Turkish

Görgün

Yıldız

2011

Computer and Information Sciences II

View full text Add to dashboard Cite

In this paper, we propose a classification based approach to the morphological disambiguation for Turkish language. Due to complex morphology in Turkish, any word can get unlimited number of affixes resulting very large tag sets. The problem is defined as choosing one of parses of a word not taking the existing root word into consideration. We trained our model with well-known classifiers using WEKA toolkit and tested on a common test set. The best performance achieved is 95.61% by J48 Tree classifier.

show abstract

“…In order to use advanced queries, users should know the tag-set we used during part-of-speech tagging. The tag set has the following tags (Sak et al, 2008) (Sezer, 2016): Noun (noun), Verb (verb), Adj (adjective), Conj (conjunction), Det (determiner), Adv (adverb), Postp (postposition), Pron (pronoun), Num (number), Ques (question suffix), Interj (interjection), Punc (punctuation), Dup (duplication), abbr (abbreviation), intAbbr (internet abbreviation), YY (misspelling), emoticon (smileys), intEmphasis (internet emphasis), intSlang (internet slang) and UnDef (undefinite).…”

Section: Running a Querymentioning

confidence: 99%

TS Corpus Project: An online Turkish Dictionary and TS DIY Corpus

Sezer¹

2017

Eur. j. lang. lit. stud.

View full text Add to dashboard Cite

TS Corpus is a free and independent project that aims building Turkish corpora, NLP tools and linguistic datasets. Since 2011, 10 corpora, various NLP tools, a large dataset and an online dictionary has been released. This paper focuses on the "online dictionary" and " TS do-it-yourself corpus" released by the project. The dictionary data is based on TDK (Turkish Language Society) Contemporary Dictionary. However, the dictionary published serves many enhanced functions at user interface level. But, the main importance of the study is about the results presented to the users upon their queries; the presentation of collocations and tri-grams of the key word searched for. The collocations are harvested from a large Turkish corpus, +760 million tokens and the tri-grams were generated from Turkish Wikipedia pages. The do-it-yourself corpus (TS DIY Corpus), allows users to build their own corpora, modify or delete the uploaded texts and run queries. Users may run queries in different modes, such as "as is", "starting/ending with" or including; besides advanced query option allows users to run queries with part-of-speech tags and lemmas. The results are given in KWIC (keyword in context) format. Various text classification options such as pubdate, author, domain, genre etc. could be selected during corpus creation. As the number of available Turkish corpora is limited, TS DIY Corpus is applicant to be a useful, wellknown and largely used software for the scholars and researchers who wants to use a Turkish corpus or study over Turkish texts of their own.

show abstract

Turkish Language Resources: Morphological Parser, Morphological Disambiguator and Web Corpus

Cited by 96 publications

References 8 publications

Recognizing Named Entities in Turkish Tweets

Recognizing Named Entities in Turkish Tweets

A Novel Approach to Morphological Disambiguation for Turkish

TS Corpus Project: An online Turkish Dictionary and TS DIY Corpus

Contact Info

Product

Resources

About