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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 ...
Towards fast natural language parsing: FASTPARSE ERC Starting Grant
(Sociedad Española para el Procesamiento del Lenguaje Natural (SEPLN), 2017-09)
[Abstract:] The goal of the FASTPARSE project (Fast Natural Language Parsing for
Large-Scale NLP), funded by the European Research Council (ERC), is to achieve
a breakthrough in the speed of natural language syntactic ...
Sequence Labeling Parsing by Learning across Representations
(Association for Computational Linguistics, 2019-07)
[Absctract]: We use parsing as sequence labeling as a common framework to learn across constituency and dependency syntactic abstractions. To do so, we cast the problem as multitask learning (MTL). First, we show that ...
Cross-lingual Inflection as a Data Augmentation Method for Parsing
(Association for Computational Linguistics, 2022-05)
[Absctract]: We propose a morphology-based method for low-resource (LR) dependency parsing. We train a morphological inflector for target LR languages, and apply it to related rich-resource (RR) treebanks to create ...
4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees
(Association for Computational Linguistics, 2023-12)
[Absctract]: We introduce an encoding for parsing as sequence labeling that can represent any projective dependency tree as a sequence of 4-bit labels, one per word. The bits in each word’s label represent (1) whether it ...
Viable Dependency Parsing as Sequence Labeling
(Association for Computational Linguistics (ACL), 2019-06)
[Abstract]: We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, ...
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) ...