Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Kemal Oflazer"'
Publikováno v:
SIU
Semantic textual similarity is the task of determining how similar two texts are. In this study, we present the first Turkish evaluation benchmark dataset for semantic textual similarity. We created the dataset by translating the English STS benchmar
Publikováno v:
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021).
ROUGE is a widely used evaluation metric in text summarization. However, it is not suitable for the evaluation of abstractive summarization systems as it relies on lexical overlap between the gold standard and the generated summaries. This limitation
Publikováno v:
Computación y Sistemas. 24
This paper presents a neural network classifier approach to detecting precise within-document (WD) and cross-document (CD) event coreference clusters effectively using only event mention based features. Our approach does not rely on any event argumen
Publikováno v:
Natural Language Engineering. 23:535-559
Sentiment analysis has attracted a lot of research interest in recent years, especially in the context of social media. While most of this research has focused on English, there is ample data and interest in the topic for many other languages, as wel
Autor:
Murat Saraclar, Kemal Oflazer
Publikováno v:
Turkish Natural Language Processing ISBN: 9783319901633
We present a short survey and exposition of some of the important aspects of Turkish that have proved to be interesting and challenging for natural language and speech processing. Most of the challenges stem from the complex morphology of Turkish and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d888fa035ff874c2a6a0be67bffe3bcb
https://doi.org/10.1007/978-3-319-90165-7_1
https://doi.org/10.1007/978-3-319-90165-7_1
Publikováno v:
Turkish Natural Language Processing ISBN: 9783319901633
Machine translation is one of the most important applications of natural language processing. The last 25 years have seen tremendous progress in machine translation, enabled by the development of statistical techniques and availability of large-scale
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae9a214089952fe7996e45f20a3d50d2
https://doi.org/10.1007/978-3-319-90165-7_10
https://doi.org/10.1007/978-3-319-90165-7_10
Publikováno v:
Turkish Natural Language Processing ISBN: 9783319901633
Morphological disambiguation is the task of determining the contextually correct morphological parses of tokens in a sentence. A morphological disambiguator takes in a set of morphological parses for each token, generated by a morphological analyzer,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a4a9181269c1822d0f9fedf4cb71fde3
https://doi.org/10.1007/978-3-319-90165-7_3
https://doi.org/10.1007/978-3-319-90165-7_3
Publikováno v:
Turkish Natural Language Processing ISBN: 9783319901633
Named-entity recognition is an important task for many other natural language processing tasks and applications such as information extraction, question answering, sentiment analysis, machine translation, etc. Over the last decades named-entity recog
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b1be39311604aad7484cb882afb3a61
https://doi.org/10.1007/978-3-319-90165-7_6
https://doi.org/10.1007/978-3-319-90165-7_6
Autor:
Özlem Çetinoğlu, Kemal Oflazer
Publikováno v:
Turkish Natural Language Processing ISBN: 9783319901633
In this chapter we present a large scale, deep grammar for Turkish based on the Lexical-Functional Grammar formalism. In dealing with the rich derivational morphology of Turkish, we follow an approach based on morphological units that are larger than
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3ee8d33d50835f7d65205ac469086c1
https://doi.org/10.1007/978-3-319-90165-7_9
https://doi.org/10.1007/978-3-319-90165-7_9
Autor:
Kemal Oflazer
Publikováno v:
Turkish Natural Language Processing ISBN: 9783319901633
This chapter presents an overview of Turkish morphology followed by the architecture of a state-of-the-art wide coverage morphological analyzer for Turkish implemented using the Xerox Finite State Tools. It covers the morphophonological and morphogra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a70c8a8f51e9e2e9dcf3eee88711d0a0
https://doi.org/10.1007/978-3-319-90165-7_2
https://doi.org/10.1007/978-3-319-90165-7_2