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Found 17 papers, 11 papers with code
Structured Sentiment Analysis as Transition-based Dependency Parsing
Structured sentiment analysis (SSA) aims to automatically extract people's opinions from a text in natural language and adequately represent that information in a graph structure.
Shift-Reduce Task-Oriented Semantic Parsing with Stack-Transformers
In this article, we advance the research on shift-reduce semantic parsing for task-oriented dialog.
Transition-based Semantic Role Labeling with Pointer Networks
Semantic role labeling (SRL) focuses on recognizing the predicate-argument structure of a sentence and plays a critical role in many natural language processing tasks such as machine translation and question answering.
Discontinuous Grammar as a Foreign Language
In order to achieve deep natural language understanding, syntactic constituent parsing is a vital step, highly demanded by many artificial intelligence systems to process both text and speech.
Dependency Parsing with Bottom-up Hierarchical Pointer Networks
Dependency parsing is a crucial step towards deep language understanding and, therefore, widely demanded by numerous Natural Language Processing applications.
Reducing Discontinuous to Continuous Parsing with Pointer Network Reordering
Discontinuous constituent parsers have always lagged behind continuous approaches in terms of accuracy and speed, as the presence of constituents with discontinuous yield introduces extra complexity to the task.
Multitask Pointer Network for Multi-Representational Parsing
We propose a transition-based approach that, by training a single model, can efficiently parse any input sentence with both constituent and dependency trees, supporting both continuous/projective and discontinuous/non-projective syntactic structures.
Transition-based Semantic Dependency Parsing with Pointer Networks
Transition-based parsers implemented with Pointer Networks have become the new state of the art in dependency parsing, excelling in producing labelled syntactic trees and outperforming graph-based models in this task.
Enriched In-Order Linearization for Faster Sequence-to-Sequence Constituent Parsing
Sequence-to-sequence constituent parsing requires a linearization to represent trees as sequences.
Discontinuous Constituent Parsing with Pointer Networks
One of the most complex syntactic representations used in computational linguistics and NLP are discontinuous constituent trees, crucial for representing all grammatical phenomena of languages such as German.
Left-to-Right Dependency Parsing with Pointer Networks
We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building $n$ attachments, with $n$ being the length of the input sentence.
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Dependency Parsing
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Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing
In addition, by improving the performance of the state-of-the-art in-order shift-reduce parser, we achieve the best accuracy to date (92. 0 F1) obtained by a fully-supervised single-model greedy shift-reduce constituent parser on the WSJ benchmark.
A Dynamic Oracle for Linear-Time 2-Planar Dependency Parsing
We propose an efficient dynamic oracle for training the 2-Planar transition-based parser, a linear-time parser with over 99% coverage on non-projective syntactic corpora.
Faster Shift-Reduce Constituent Parsing with a Non-Binary, Bottom-Up Strategy
An increasingly wide range of artificial intelligence applications rely on syntactic information to process and extract meaning from natural language text or speech, with constituent trees being one of the most widely used syntactic formalisms.
Non-Projective Dependency Parsing with Non-Local Transitions
We present a novel transition system, based on the Covington non-projective parser, introducing non-local transitions that can directly create arcs involving nodes to the left of the current focus positions.
A Full Non-Monotonic Transition System for Unrestricted Non-Projective Parsing
Restricted non-monotonicity has been shown beneficial for the projective arc-eager dependency parser in previous research, as posterior decisions can repair mistakes made in previous states due to the lack of information.
