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Found 8 papers, 6 papers with code
Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing
Different linearizations have been proposed to cast dependency parsing as sequence labeling and solve the task as: (i) a head selection problem, (ii) finding a representation of the token arcs as bracket strings, or (iii) associating partial transition sequences of a transition-based parser to words.
Bracketing Encodings for 2-Planar Dependency Parsing
We present a bracketing-based encoding that can be used to represent any 2-planar dependency tree over a sentence of length n as a sequence of n labels, hence providing almost total coverage of crossing arcs in sequence labeling parsing.
A Unifying Theory of Transition-based and Sequence Labeling Parsing
We define a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees.
Towards Making a Dependency Parser See
We explore whether it is possible to leverage eye-tracking data in an RNN dependency parser (for English) when such information is only available during training, i. e., no aggregated or token-level gaze features are used at inference time.
Sequence Labeling Parsing by Learning Across Representations
We use parsing as sequence labeling as a common framework to learn across constituency and dependency syntactic abstractions.
Speeding Up Natural Language Parsing by Reusing Partial Results
This paper proposes a novel technique that applies case-based reasoning in order to generate templates for reusable parse tree fragments, based on PoS tags of bigrams and trigrams that demonstrate low variability in their syntactic analyses from prior data.
Viable Dependency Parsing as Sequence Labeling
We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels.
