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Titlebook: Human Language Technology. Challenges for Computer Science and Linguistics; 7th Language and Tec Zygmunt Vetulani,Joseph Mariani,Marek Kubi

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發(fā)表于 2025-3-21 16:04:29 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Human Language Technology. Challenges for Computer Science and Linguistics
副標題7th Language and Tec
編輯Zygmunt Vetulani,Joseph Mariani,Marek Kubis
視頻videohttp://file.papertrans.cn/430/429285/429285.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Human Language Technology. Challenges for Computer Science and Linguistics; 7th Language and Tec Zygmunt Vetulani,Joseph Mariani,Marek Kubi
描述.This book constitutes the refereed proceedings of the 7h Language and Technology Conference: Challenges for Computer Science and Linguistics, LTC 2015, held in Poznan, Poland, in November 2015...The 31 revised papers presented in this volume were carefully reviewed and selected from 108 submissions. The papers selected to this volume belong to various fields of: ?Speech Processing; Multiword Expressions; Parsing; Language Resources and Tools; Ontologies and Wordnets; Machine Translation; Information and Data Extraction; Text Engineering and Processing; Applications in Language Learning; Emotions, Decisions and Opinions; Less-Resourced Languages..
出版日期Conference proceedings 2018
關鍵詞artificial intelligence; classification; computational linguistics; information extraction; information
版次1
doihttps://doi.org/10.1007/978-3-319-93782-3
isbn_softcover978-3-319-93781-6
isbn_ebook978-3-319-93782-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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發(fā)表于 2025-3-21 22:31:36 | 只看該作者
Automatic Transcription and Subtitling of Slovak Multi-genre Audiovisual Recordings. Preliminary results show a significant decrease in word error rate relatively from 2.40% to 47.10% for an individual speaker in fully automatic transcription and subtitling of Slovak parliament speech, broadcast news or TEDx talks.
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Neural Networks Revisited for Proper Name Retrieval from Diachronic Documentsransformation. This model allows to better take into account lexical and semantic word relationships. In the context of broadcast news transcription and in terms of recall, experimental results show a good ability of the proposed model to select new relevant proper names.
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Reinvestigating the Classification Approach to the Article and Preposition Error Correctionrained word classes acquired by unsupervised learning as a substitution for commonly used part-of-speech tags. Our best models significantly outperform the top systems from CoNLL-2014 Shared Task in terms of article and preposition error correction.
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Methods of Linking Linguistic Resources for Semantic Role Labelingoyed a logical reasoning device to facilitate the linking procedure. We present results and discuss the challenges and pitfalls that arose from this undertaking. We also compare our rule-based approach with that of using a state-of-the-art English semantic role labeler pipeline for the thematic role transferring task.
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