Turkish verbal multiword expressions corpus

Berk, Gözde; Erden, Berna; Güngör, Tunga

doi:10.1109/siu.2018.8404583

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…They cannot stand in the sentence on their own and are mostly used in combination with nouns (e.g., yardım et, sakin ol), forming light verb constructions (LVC). In LVC, light verbs' primary role is to verbalize nouns and make it possible to add grammatical information such as tense, mood, and person (Akşehirli, 2013;Berk et al, 2018). For these reasons, in this study, it is considered that the presence of light verb constructions might affect the processing of Turkish speech formulas.…”

Section: Light Verb Constructionsmentioning

confidence: 99%

“…Half of the 40 speech formulas included light verbs (e.g., et-, ol-), and 20 formulas contained non-light verbs (e.g., sor-, as-). Various criteria (Akşehirli, 2013;Berk et al, 2018) were taken into account in determining whether verbs in formulas were light or non-light. Speech formulas which included light or nonlight verbs were as follows:…”

Section: The Inclusion Of Light Verbsmentioning

confidence: 99%

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Türkçede Konuşma Kalıplarının İşlemlenmesi: Maskelenmiş Çağrıştırma Çalışması

Göymen

Aygüneş

2020

Dilbilim Araştırmaları Dergisi

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Studies have indicated that formulaic sequences are processed significantly faster than newly created phrases; however, the source of this processing advantage has not been sufficiently investigated in the literature. The Holistic Approach justifies this processing advantage for formulaic sequences with the argument that they are processed and stored as single units without being decomposed into their constituents. On the contrary, Distributed Representation argues against holistic processing. It proposes instead that formulaic sequences are processed through their parts as in novel non-formulaic phrases. Their constituents form a mutual association in the sense that the mental activation of a component part activates the other, thus leading to faster processing. The present study reports findings from a masked priming experiment investigating Turkish speech formulas' online processing in native processing. Results show that speech formulas and their matched novel phrases are processed similarly, as evidenced by no significant difference in reaction times. These findings support Distributed Representation in the processing of formulaic sequences. Results also suggest that non-transparent formulas are processed more slowly than transparent ones.

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Section: Light Verb Constructionsmentioning

confidence: 99%

Section: The Inclusion Of Light Verbsmentioning

confidence: 99%

Türkçede Konuşma Kalıplarının İşlemlenmesi: Maskelenmiş Çağrıştırma Çalışması

Göymen

Aygüneş

2020

Dilbilim Araştırmaları Dergisi

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“…Language-specific PARSEME corpus description papers not covered here can provide details, e.g. for Basque , Chinsese (Jiang et al, 2018), English , Irish , Italian (Monti and di Buono, 2019), Polish , Portuguese (Ramisch et al, 2018b), Romanian (Barbu Mititelu et al, 2019), Turkish (Berk et al, 2018b;Ozturk et al, 2022), among others. Domains Corpus domain may play an important role in MWEI.…”

Section: Corpus Constitution and Selectionmentioning

confidence: 99%

A Survey of MWE Identification Experiments: The Devil is in the Details

Ramisch,

Walsh,

Blanchard

et al. 2023

Proceedings of the 19th Workshop on Multiword Expressions (MWE 2023)

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Multiword expression (MWE) identification has been the focus of numerous research papers, especially in the context of the DiMSUM and PARSEME Shared Tasks (STs). This survey analyses 40 MWE identification papers with experiments on data from these STs. We look at corpus selection, pre-and post-processing, MWE encoding, evaluation metrics, statistical significance, and error analyses. We find that these aspects are usually considered minor and/or omitted in the literature. However, they may considerably impact the results and the conclusions drawn from them. Therefore, we advocate for more systematic descriptions of experimental conditions to reduce the risk of misleading conclusions drawn from poorly designed experimental setup.

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“…In the study, a new Turkish test collection was created considering the labelling rules stated in the "parseme" Joint Study 1.0 This collection was created by bringing together articles and columns in many fields. The collection is intended to be an effective resource for machine experience, n-gram based language modelling, and syntactic divergence (Berk et al, 2018). The determination of different emotions in a text together with the assets they affect is called Target Based Emotion Analysis.…”

Section: Studies On Turkish Natural Language Processingmentioning

confidence: 99%

Challenges Encountered in Turkish Natural Language Processing Studies

Tohma¹,

Kutlu²

2020

Natural and Engineering Sciences

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Natural language processing is a branch of computer science that combines artificial intelligence with linguistics. It aims to analyze a language element such as writing or speaking with software and convert it into information. Considering that each language has its own grammatical rules and vocabulary diversity, the complexity of the studies in this field is somewhat understandable. For instance, Turkish is a very interesting language in many ways. Examples of this are agglutinative word structure, consonant/vowel harmony, a large number of productive derivational morphemes (practically infinite vocabulary), derivation and syntactic relations, a complex emphasis on vocabulary and phonological rules. In this study, the interesting features of Turkish in terms of natural language processing are mentioned. In addition, summary info about natural language processing techniques, systems and various sources developed for Turkish are given.

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Turkish verbal multiword expressions corpus

Cited by 4 publications

References 4 publications

Türkçede Konuşma Kalıplarının İşlemlenmesi: Maskelenmiş Çağrıştırma Çalışması

Türkçede Konuşma Kalıplarının İşlemlenmesi: Maskelenmiş Çağrıştırma Çalışması

A Survey of MWE Identification Experiments: The Devil is in the Details

Challenges Encountered in Turkish Natural Language Processing Studies

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