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Utilising knowledge graph embeddings for data-to-text generation
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Enhancing multiple-choice question answering with causal knowledge
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NUIG-DSI at the WebNLG+ challenge: Leveraging transfer learning for RDF-to-text generation
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Bilingual lexicon induction across orthographically-distinct under-resourced Dravidian languages
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In: Chakravarthi, Bharathi Raja orcid:0000-0002-4575-7934 , Rajasekaran, Navaneethan, Arcan, Mihael orcid:0000-0002-3116-621X , McGuinness, Kevin orcid:0000-0003-1336-6477 , O'Connor, Noel E. orcid:0000-0002-4033-9135 and McCrae, John P. orcid:0000-0002-7227-1331 (2020) Bilingual lexicon induction across orthographically-distinct under-resourced Dravidian languages. In: 7th Workshop on NLP for Similar Languages, Varieties and Dialects, 13 Dec 2020, Barcelona, Spain (Online). (2020)
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Leveraging orthographic information to improve machine translation of under-resourced languages
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NUIG at TIAD: Combining unsupervised NLP and graph metrics for translation inference
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Aspects of Terminological and Named Entity Knowledge within Rule-Based Machine Translation Models for Under-Resourced Neural Machine Translation Scenarios ...
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Bilingual Lexicon Induction across Orthographically-distinct Under-Resourced Dravidian Languages ...
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Bilingual Lexicon Induction across Orthographically-distinct Under-Resourced Dravidian Languages ...
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Comparison of Different Orthographies for Machine Translation of Under-Resourced Dravidian Languages
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TIAD 2019 Shared Task: Leveraging knowledge graphs with neural machine translation for automatic multilingual dictionary generation
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The ESSOT system goes wild: an easy way for translating ontologies
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Generating linked-data based domain-specific sentiment lexicons from legacy language and semantic resources
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Abstract:
We present a methodology for legacy language resource adaptation that generates domain-specific sentiment lexicons organized around domain entities described with lexical information and sentiment words described in the context of these entities. We explain the steps of the methodology and we give a working example of our initial results. The resulting lexicons are modelled as Linked Data resources by use of established formats for Linguistic Linked Data (lemon, NIF) and for linked sentiment expressions (Marl), thereby contributing and linking to existing Language Resources in the Linguistic Linked Open Data cloud. Keywords: domain specific lexicon, entity extraction and linking, sentiment analysis ; This work has been funded by the European project EUROSENTIMENT under grant no. 296277. ; non-peer-reviewed
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Keyword:
Domain specific lexicon; Entity extraction; Linking; sentiment analysis
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URL: http://hdl.handle.net/10379/14909
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Automatic enrichment of terminological resources: the IATE RDF example
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Avtomatsko pridobivanje besednih zvez iz korpusa z uporabo leksikona SSJ
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Inferring translation candidates for multilingual dictionary generation with multi-way neural machine translation
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