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Increasing NLP Parsing Efficiency with Chunking
(M D P I AG, 2018-09-19)
[Abstract] We introduce a “Chunk-and-Pass” parsing technique influenced by a psycholinguistic model, where linguistic information is processed not word-by-word but rather in larger chunks of words. We present preliminary ...
Sequence Tagging for Fast Dependency Parsing
(2019)
[Abstract]
Dependency parsing has been built upon the idea of using parsing methods based on shift-reduce or graph-based algorithms in order to identify binary dependency relations between the words in a sentence. In this ...
Parsing as Pretraining
(2020)
[Abstract] Recent analyses suggest that encoders pretrained for language
modeling capture certain morpho-syntactic structure.
However, probing frameworks for word vectors still do not report
results on standard setups ...
How important is syntactic parsing accuracy? An empirical evaluation on rule-based sentiment analysis
(Springer, 2019)
[Abstract]: Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language ...
A non-projective greedy dependency parser with bidirectional LSTMs
(Association for Computational Linguistics, 2017-08)
[Abstract]: The LyS-FASTPARSE team present BIST-COVINGTON, a neural implementation of the Covington (2001) algorithm for non-projective dependency parsing. The bidirectional LSTM approach by Kiperwasser and Goldberg (2016) ...