otherentity ID: turkish

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Morphological Disambiguation by Voting Constraints

Authors: nan

Publication: TIPSTER (1997)

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We present a constraint-based morpholog- ical disambiguation system in which indi- vidual constraints vote on matching mor- phological parses, and disambiguation of all the tokens in a sentence is performed …...

43884

Deriving the Predicate-Argument Structure for a Free Word Order Language

Authors: nan

Publication: TIPSTER (1998)

DOI: nan

In relatively free word order languages, grammat- ical functions are intricately related to case mark- ing. Assuming an ordered representation of the predicate-argument structure, this work proposes a Combinatory Categorial …...

35264

Dependency Parsing with an Extended Finite State Approach

Authors: nan

Publication: TIPSTER (1999)

DOI: nan

This paper presents a dependency parsing scheme using an extended finite state approach. The parser augments input representation with "channels" so that links representing syntactic dependency rela- tions among words …...

35092

Effective Use of Linguistic Features for Sentiment Analysis of Korean

Authors: nan

Publication: TIPSTER (2010)

DOI: nan

. In this paper, we propose a new linguistic approach for sentiment analysis of Korean. In order to overcome shortcomings of previous works confined to statistical methods, we make effective …...

19094

Syntax-to-Morphology Mapping in Factored Phrase-Based Statistical Machine Translation from English to Turkish

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Publication: TIPSTER (2010)

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We present a novel scheme to apply fac- tored phrase-based SMT to a language pair with very disparate morphological struc- tures. Our approach relies on syntac- tic analysis on the …...

36165

Word Alignment for English-Turkish Language Pair

Authors: nan

Publication: TIPSTER (2012)

DOI: nan

AbstractWord alignment is an important step for machine translation systems. Although the alignment performance between grammatically similar languages is reported to be very high in many studies, the case is …...

13815

Morfessor FlatCat: An HMM-Based Method for Unsupervised and Semi-Supervised Learning of Morphology

Authors: nan

Publication: TIPSTER (2014)

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Morfessor is a family of methods for learning morphological segmentations of words based on unannotated data. We introduce a new variant of Morfessor, FlatCat, that applies a hid- den Markov …...

5777

Predicting Dialect Variation in Immigrant Contexts Using Light Verb Constructions

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Languages spoken by immigrants change due to contact with the local languages. Capturing these changes is problematic for current language technologies, which are typically developed for speakers of the standard …...

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An Unsupervised Method for Uncovering Morphological Chains

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AbstractMost state-of-the-art systems today produce morphological analysis based only on orthographic patterns. In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words. …...

27179

Rapid Development of Morphological Analyzers for Typologically Diverse Languages

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AbstractThe Low Resource Language research conducted under DARPA’s Broad Operational Language Translation (BOLT) program required the rapid creation of text corpora of typologically diverse languages (Turkish, Hausa, and Uzbek) which …...

15728

Towards Improving Low-Resource Speech Recognition Using Articulatory and Language Features

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AbstractIn an increasingly globalized world, there is a rising demand for speech recognition systems. Systems for languages like English, German or French do achieve a decent performance, but there exists …...

20749

An Evaluation of Two Vocabulary Reduction Methods for Neural Machine Translation

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Neural machine translation (NMT) models are conventionally trained with fixed-size vocabu- laries to control the computational complexity and the quality of the learned word represen- tations. This, however, limits the …...

29132

Using Morphological Knowledge in Open-Vocabulary Neural Language Models

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AbstractLanguages with productive morphology pose problems for language models that generate words from a fixed vocabulary. Although character-based models allow any possible word type to be generated, they are linguistically …...

20904

Cross-Lingual Word Embeddings for Morphologically Rich Languages

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AbstractCross-lingual word embedding models learn a shared vector space for two or more languages so that words with similar meaning are represented by similar vectors regardless of their language. Although …...

19677

ANDES at SemEval-2020 Task 12: A Jointly-trained BERT Multilingual Model for Offensive Language Detection

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Publication: TIPSTER (2020)

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AbstractThis paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive Language Detection. We jointly-trained a single model by fine-tuning Multilingual BERT to tackle the task across all the proposed …...

24466

KS@LTH at SemEval-2020 Task 12: Fine-tuning Multi- and Monolingual Transformer Models for Offensive Language Detection

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AbstractThis paper describes the KS@LTH system for SemEval-2020 Task 12 OffensEval2: Multilingual Offensive Language Identification in Social Media. We compare mono- and multilingual models based on fine-tuning pre-trained transformer models …...

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Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages

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AbstractUnsupervised translation has reached impressive performance on resource-rich language pairs such as English-French and English-German. However, early studies have shown that in more realistic settings involving low-resource, rare languages, unsupervised …...

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DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages

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AbstractWe introduce DivEMT, the first publicly available post-editing study of Neural Machine Translation (NMT) over a typologically diverse set of target languages. Using a strictly controlled setup, 18 professional translators …...

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Quantifying Synthesis and Fusion and their Impact on Machine Translation

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AbstractTheoretical work in morphological typology offers the possibility of measuring morphological diversity on a continuous scale. However, literature in Natural Language Processing (NLP) typically labels a whole language with a …...

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Improving Translation Quality for Low-Resource Inuktitut with Various Preprocessing Techniques

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AbstractNeural machine translation has been shown to outperform all other machine translation paradigms when trained in a high-resource setting. However, it still performs poorly when dealing with low-resource languages, for …...

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