Constituent Parsing as Sequence Labeling

Gómez-Rodríguez, Carlos; Vilares, David

doi:10.18653/v1/d18-1162

Cited by 56 publications

(46 citation statements)

References 27 publications

(35 reference statements)

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“…Recent research has shown that constituency parsing can be reduced to sequence tagging, a structured prediction problem where for each input token a single label output is generated [7]. To do so, the syntactic trees need to be linearized through an encoding method, as shown in Figure 1a.…”

Section: Methodsmentioning

confidence: 99%

Sequence Tagging for Fast Dependency Parsing

Strzyz

Vilares

Gómez-Rodríguez

2019

The 2nd XoveTIC Conference (XoveTIC 2019)

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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 study we adopt a radically different approach and cast full dependency parsing as a pure sequence tagging task. In particular, we apply a linearization function to the tree that results in an output label for each token that conveys information about the word’s dependency relations. We then follow a supervised strategy and train a bidirectional long short-term memory network to learn to predict such linearized trees. Contrary to the previous studies attempting this, the results show that this approach not only leads to accurate but also fast dependency parsing. Furthermore, we obtain even faster and more accurate parsers by recasting the problem as multitask learning, with a twofold objective: to reduce the output vocabulary and also to exploit hidden patterns coming from a second parsing paradigm (constituent grammars) when used as an auxiliary task.

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Section: Methodsmentioning

confidence: 99%

Sequence Tagging for Fast Dependency Parsing

Strzyz

Vilares

Gómez-Rodríguez

2019

The 2nd XoveTIC Conference (XoveTIC 2019)

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“…In this context, the closest work to ours is the reduction proposed by Gómez-Rodríguez and Vilares (2018), who cast continuous constituent parsing as sequence labeling. 3 In the next sections we build on top of their work and: (i) analyze why their approach cannot handle discontinuous phrases, (ii) extend it to handle such phenomena, and (iii) train functional sequence labeling discontinuous parsers.…”

Section: Related Workmentioning

confidence: 99%

“…Related to these research aspects, this work explores the feasibility of discontinuous parsing under the sequence labeling paradigm, inspired by Gómez-Rodríguez and Vilares (2018)'s work on fast and simple continuous constituent parsing. We will focus on tackling the limitations of their encoding functions when it comes to analyzing discontinuous structures, and include an empirical comparison against existing parsers.…”

Section: Introductionmentioning

confidence: 99%

Discontinuous Constituent Parsing as Sequence Labeling

Vilares

Gómez-Rodríguez

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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This paper reduces discontinuous parsing to sequence labeling. It first shows that existing reductions for constituent parsing as labeling do not support discontinuities. Second, it fills this gap and proposes to encode tree discontinuities as nearly ordered permutations of the input sequence. Third, it studies whether such discontinuous representations are learnable. The experiments show that despite the architectural simplicity, under the right representation, the models are fast and accurate. 1

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“…Rei [28] considers that these patterns are useful for improving accuracy on sequence labeling tasks. Strzyz et al [32] use sequence labeling for constituency [11] and dependency parsing [33] combined with multi-task learning to learn across syntactic representations. They show that adding a parsing paradigm as an auxiliary loss consistently improves the performance on the other paradigm.…”

Section: Sequence Labelingmentioning

confidence: 99%

A hybrid representation-based simile component extraction

Zhang

et al. 2020

Neural Comput & Applic

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Simile, a special type of metaphor, can help people to express their ideas more clearly. Simile component extraction is to extract tenors and vehicles from sentences. This task has a realistic significance since it is useful for building cognitive knowledge base. With the development of deep neural networks, researchers begin to apply neural models to component extraction. Simile components should be in cross-domain. According to our observations, words in cross-domain always have a different concept. Thus, concept is important when identifying whether two words are simile components or not. However, existing models do not integrate concept into their models. It is difficult for these models to identify the concept of a word. What's more, corpus about simile component extraction is limited. There are a number of rare words or unseen words and the representations of these words are always nor proper enough. Exiting models can hardly extract simile components accurately when there are low frequency words in sentences. To solve

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Constituent Parsing as Sequence Labeling

Cited by 56 publications

References 27 publications

Sequence Tagging for Fast Dependency Parsing

Sequence Tagging for Fast Dependency Parsing

Discontinuous Constituent Parsing as Sequence Labeling

A hybrid representation-based simile component extraction

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