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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018)
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Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
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Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only ...
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
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On the Limitations of Unsupervised Bilingual Dictionary Induction ...
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Scoring Lexical Entailment with a Supervised Directional Similarity Network ...
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Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP ...
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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain ...
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Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation ...
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Investigating the cross-lingual translatability of VerbNet-style classification. ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain. ...
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Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain.
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Abstract:
BACKGROUND: Bilingual lexicon induction (BLI) is an important task in the biomedical domain as translation resources are usually available for general language usage, but are often lacking in domain-specific settings. In this article we consider BLI as a classification problem and train a neural network composed of a combination of recurrent long short-term memory and deep feed-forward networks in order to obtain word-level and character-level representations. RESULTS: The results show that the word-level and character-level representations each improve state-of-the-art results for BLI and biomedical translation mining. The best results are obtained by exploiting the synergy between these word-level and character-level representations in the classification model. We evaluate the models both quantitatively and qualitatively. CONCLUSIONS: Translation of domain-specific biomedical terminology benefits from the character-level representations compared to relying solely on word-level representations. It is beneficial to take a deep learning approach and learn character-level representations rather than relying on handcrafted representations that are typically used. Our combined model captures the semantics at the word level while also taking into account that specialized terminology often originates from a common root form (e.g., from Greek or Latin).
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Keyword:
Data Mining; Deep Learning; Humans; Knowledge Bases; Multilingualism; Natural Language Processing; Semantics
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URL: https://www.repository.cam.ac.uk/handle/1810/288980 https://doi.org/10.17863/CAM.36243
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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction
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