Morphological Processing for Turkish

Oflazer, Kemal

doi:10.1007/978-3-319-90165-7_2

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…For morphologically complex languages like Turkish, the analysis can be performed by language dependent rule-based systems such as finite-state transducers that encode morphophonemics and morphotactics (Koskenniemi, 1981(Koskenniemi, , 1983Karttunen and Wittenburg, 1983). The first rule-based analyzer for Turkish was developed in Oflazer (1994), we used an updated version of this analyzer (Oflazer, 2018) when creating our new Turkish data set.…”

Section: Analysis and Disambiguationmentioning

confidence: 99%

Morphological Analysis Using a Sequence Decoder

Ekin

Dayanik

Yüret

2019

Transactions of the Association for Computational Linguistics

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We introduce Morse, a recurrent encoder-decoder model that produces morphological analyses of each word in a sentence. The encoder turns the relevant information about the word and its context into a fixed size vector representation and the decoder generates the sequence of characters for the lemma followed by a sequence of individual morphological features. We show that generating morphological features individually rather than as a combined tag allows the model to handle rare or unseen tags and to outperform whole-tag models. In addition, generating morphological features as a sequence rather than, for example, an unordered set allows our model to produce an arbitrary number of features that represent multiple inflectional groups in morphologically complex languages. We obtain state-of-the-art results in nine languages of different morphological complexity under low-resource, high-resource, and transfer learning settings. We also introduce TrMor2018, a new high-accuracy Turkish morphology data set. Our Morse implementation and the TrMor2018 data set are available online to support future research. 1 See https://github.com/ai-ku/Morse.jl for a Morse implementation in Julia/Knet (Yuret, 2016 ) and https://github.com/ai-ku/TrMor2018 for the new Turkish data set.

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Section: Analysis and Disambiguationmentioning

confidence: 99%

Morphological Analysis Using a Sequence Decoder

Ekin

Dayanik

Yüret

2019

Transactions of the Association for Computational Linguistics

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“…It is claimed that many AI models cannot yield the same success rate found in English-based corpus because of agglutinative features of Turkish ( _ Istanbul Barosu, 2019). In agglutinative languages like Turkish, word forms consist of morphemes concatenated to a root morpheme or to other morphemes, which creates challenges for applications of natural language processing (NLP) techniques (Oflazer, 2018). However, using sentiment analysis, an NLP technique, researchers achieved 75% accuracy on binary classification of movie reviews in Turkish (Gezici & Yanıkoglu, 2018).…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence to Predict Decisions of the Turkish Constitutional Court

Sert

Yıldırım²,

Haşlak

2021

Social Science Computer Review

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This article draws on an artificial intelligence (AI) technique to predict whether an individual application regarding the Turkish Constitutional Court's public morality and freedom of expression cases leding to a “violation” or a “nonviolation” decision. To this end, four different data sets have been composed, preclassification and fundamental word embeddings steps have been made on each data set. Multilayer perceptron, which is based on artificial neural networks, has been used for the prediction of the case decisions. We have predicted the court’s decisions on these cases with the high success rates (average accuracy of 90%) by using the subject or reasoning sections of texts of the cases as data. The subject section of the cases constituting only a very small part of the data has yielded the highest accuracy. The article has demonstrated that a basic AI technique can be successful in achieving accurate predictions even with relatively small data sets derived from well-structured court rulings.

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Morphological Processing for Turkish

Cited by 2 publications

References 7 publications

Morphological Analysis Using a Sequence Decoder

Morphological Analysis Using a Sequence Decoder

Using Artificial Intelligence to Predict Decisions of the Turkish Constitutional Court

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