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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 ...
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 ...
Assessment of Pre-Trained Models Across Languages and Grammars
(Association for Computational Linguistics, 2023-11)
[Absctract]: We present an approach for assessing how
multilingual large language models (LLMs)
learn syntax in terms of multi-formalism syntactic structures. We aim to recover constituent
and dependency structures by ...
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 ...
On the Use of Parsing for Named Entity Recognition
(MDPI, 2021-01-25)
[Abstract] Parsing is a core natural language processing technique that can be used to obtain the structure underlying sentences in human languages. Named entity recognition (NER) is the task of identifying the entities ...