Open Text Semantic Parsing Using FrameNet and WordNet
By Lei Shi, Rada Mihalcea. 2004
27 citations recorded by the source corpus.
This paper describes a rule-based semantic parser that relies on a frame dataset (FrameNet), and a semantic network (WordNet), to identify semantic relations between words in open text, as well as shallow semantic features associated with concepts in the text. Parsing semantic structures allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Jason Aaron Edward Weston
- Yoshua Bengio
- Antoine Bordes
- Mohit Bansal
- Jianfeng Gao
- Kyunghyun Cho
- Christopher D. Manning
- Percy Liang
Citations in the corpus
- Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing (2012, methodology): “wards learning to associate a MR to any kind of natural language sentence (Shi and Mihalcea, 2004; Poon and Domingos, 2009).”