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Lexical Semantic Knowledge and Information Extraction ...
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Abstract:
In the recent decade there has been a sharp increase in the utilization of machine/deep learning models for the development of Natural Language Processing (NLP) applications, especially focused on language understanding, with end-goals targeted at, but not limited to: information retrieval, machine translation, sentiment analysis, question answering, etc. These applications call for the need of better models for in-depth understanding of the language structures which in turn help development of automated routines that may acquire vast variety of unstructured data from web resources, process the data and convert it to the desired information content. In the recent past many different models have been developed for language-specific information extraction and a better understanding of semantic aspects of the language, however, yet there are some challenges associated that need to be addressed for the improved utility of these models in the down-streaming tasks. In this thesis, we propose new models with the ...
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Keyword:
deep learning; Distant Supervision; FGNET
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URL: https://dx.doi.org/10.26190/unsworks/2283
http://hdl.handle.net/1959.4/70870
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Boosting Supervised Neural Relation Extraction with Distant Supervision
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In: http://rave.ohiolink.edu/etdc/view?acc_num=osu1524095334803486 (2018)
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