Dependency Parsing of Turkish

Eryiğit, Gülşen; Nivre, Joakim; Oflazer, Kemal

doi:10.1162/coli.2008.34.4.627

Cited by 39 publications

(34 citation statements)

References 64 publications

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“…For the parsing experiments, we have concatenated IGs into word forms to get a word-based tokenization and used a reduced version of the part-of-speech tagset given by the treebank, very similar to the reduced tagset used in the parser of Eryigit and Oflazer (2006). For each word, we use the part-of-speech of each IG and in addition include the case and possessive information if the stem is a noun or pronoun.…”

Section: Turkishmentioning

confidence: 99%

MaltParser: A language-independent system for data-driven dependency parsing

et al. 2007

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Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible manner. Experimental evaluation confirms that MaltParser can achieve robust, efficient and accurate parsing for a wide range of languages without language-specific enhancements and with rather limited amounts of training data.

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Section: Turkishmentioning

confidence: 99%

MaltParser: A language-independent system for data-driven dependency parsing

et al. 2007

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“…We believe that morphological features of the context words will be also informative in morpheme tagging task because Eryigit et al [9] shows that using Table 8. Comparison of our model with suffix based tagger [19] and the perceptron algorithm [16] on the datasets obtained from Metu Sabancı Turkish Treebank.…”

Section: Discussionmentioning

confidence: 99%

Turkish PoS Tagging by Reducing Sparsity with Morpheme Tags in Small Datasets

Can

Üstün

Kurfalı

2018

Computational Linguistics and Intelligent Text Processing

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Sparsity is one of the major problems in natural language processing. The problem becomes even more severe in agglutinating languages that are highly prone to be inflected. We deal with sparsity in Turkish by adopting morphological features for part-of-speech tagging. We learn inflectional and derivational morpheme tags in Turkish by using conditional random fields (CRF) and we employ the morpheme tags in part-of-speech (PoS) tagging by using hidden Markov models (HMMs) to mitigate sparsity. Results show that using morpheme tags in PoS tagging helps alleviate the sparsity in emission probabilities. Our model outperforms other hidden Markov model based PoS tagging models for small training datasets in Turkish. We obtain an accuracy of 94.1% in morpheme tagging and 89.2% in PoS tagging on a 5K training dataset.

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“…Given a question, the question is first parsed using a Turkish dependency parser (Eryiğit, Nivre and Oflazer 2008). The accuracy of the parser is around eighty percent in terms of word-to-word attachment score.…”

Section: Question Analysis and Representationmentioning

confidence: 99%

A closed-domain question answering framework using reliable resources to assist students

Derici¹,

Aydın²,

Yenialaca³

et al. 2018

Nat. Lang. Eng.

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This paper describes a question answering framework that can answer student questions given in natural language. We suggest a methodology that makes use of reliable resources only, provides the answer in the form of a multi-document summary for both factoid and open-ended questions, and produces an answer also from foreign resources by translating into the native language. The resources are compiled using a question database in the selected domains based on reliability and coverage metrics. A question is parsed using a dependency parser, important parts are extracted by rule-based and statistical methods, the question is converted into a representation, and a query is built. Documents relevant to the query are retrieved from the set of resources. The documents are summarized and the answers to the question together with other relevant information about the topic of the question are shown to the user. A summary answer from the foreign resources is also built by the translation of the input question and the retrieved documents. The proposed approach was applied to the Turkish language and it was tested with several experiments and a pilot study. The experiments have shown that the summaries returned include the answer for about 50–60 percent of the questions. The data bank built for factoid and open-ended questions in the two domains covered is made publicly available.

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Dependency Parsing of Turkish

Cited by 39 publications

References 64 publications

MaltParser: A language-independent system for data-driven dependency parsing

MaltParser: A language-independent system for data-driven dependency parsing

Turkish PoS Tagging by Reducing Sparsity with Morpheme Tags in Small Datasets

A closed-domain question answering framework using reliable resources to assist students

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