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Found 38 papers, 20 papers with code
From Partial to Strictly Incremental Constituent Parsing
We study incremental constituent parsers to assess their capacity to output trees based on prefix representations alone.
4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees
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.
On the Challenges of Fully Incremental Neural Dependency Parsing
Since the popularization of BiLSTMs and Transformer-based bidirectional encoders, state-of-the-art syntactic parsers have lacked incrementality, requiring access to the whole sentence and deviating from human language processing.
Assessment of Pre-Trained Models Across Languages and Grammars
We present an approach for assessing how multilingual large language models (LLMs) learn syntax in terms of multi-formalism syntactic structures.
Contrasting Linguistic Patterns in Human and LLM-Generated Text
We conduct a quantitative analysis contrasting human-written English news text with comparable large language model (LLM) output from 4 LLMs from the LLaMa family.
Another Dead End for Morphological Tags? Perturbed Inputs and Parsing
The usefulness of part-of-speech tags for parsing has been heavily questioned due to the success of word-contextualized parsers.
Parsing linearizations appreciate PoS tags - but some are fussy about errors
PoS tags, once taken for granted as a useful resource for syntactic parsing, have become more situational with the popularization of deep learning.
The Fragility of Multi-Treebank Parsing Evaluation
Treebank selection for parsing evaluation and the spurious effects that might arise from a biased choice have not been explored in detail.
Cross-lingual Inflection as a Data Augmentation Method for Parsing
We propose a morphology-based method for low-resource (LR) dependency parsing.
LyS_ACoruña at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency Parsing
This paper addressed the problem of structured sentiment analysis using a bi-affine semantic dependency parser, large pre-trained language models, and publicly available translation models.
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.
On the logistical difficulties and findings of Jopara Sentiment Analysis
This paper addresses the problem of sentiment analysis for Jopara, a code-switching language between Guarani and Spanish.
Bertinho: Galician BERT Representations
The experiments show that our models, especially the 12-layer one, outperform the results of mBERT in most tasks.
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.
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.
Discontinuous Constituent Parsing as Sequence Labeling
Second, it fills this gap and proposes to encode tree discontinuities as nearly ordered permutations of the input sequence.
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.
Artificially Evolved Chunks for Morphosyntactic Analysis
We introduce a language-agnostic evolutionary technique for automatically extracting chunks from dependency treebanks.
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.
HEAD-QA: A Healthcare Dataset for Complex Reasoning
We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning.
Harry Potter and the Action Prediction Challenge from Natural Language
We explore the challenge of action prediction from textual descriptions of scenes, a testbed to approximate whether text inference can be used to predict upcoming actions.
Better, Faster, Stronger Sequence Tagging Constituent Parsers
Combining these techniques, we clearly surpass the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebanks, and reduce their parsing time even further.
Viable Dependency Parsing as Sequence Labeling
We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels.
Transition-based Parsing with Lighter Feed-Forward Networks
They also show how the size of the embeddings can be notably reduced.
Constituent Parsing as Sequence Labeling
For each word w_t, it generates a label that encodes: (1) the number of ancestors in the tree that the words w_t and w_{t+1} have in common, and (2) the nonterminal symbol at the lowest common ancestor.
Grounding the Semantics of Part-of-Day Nouns Worldwide using Twitter
The usage of part-of-day nouns, such as 'night', and their time-specific greetings ('good night'), varies across languages and cultures.
A Transition-based Algorithm for Unrestricted AMR Parsing
Non-projective parsing can be useful to handle cycles and reentrancy in AMR graphs.
Detecting Perspectives in Political Debates
We explore how to detect people{'}s perspectives that occupy a certain proposition.
Towards Syntactic Iberian Polarity Classification
Lexicon-based methods using syntactic rules for polarity classification rely on parsers that are dependent on the language and on treebank guidelines.
A non-projective greedy dependency parser with bidirectional LSTMs
In the all treebanks category (LAS and UAS) we ranked 16th and 12th.
How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis
Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech.
Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis
We present a novel unsupervised approach for multilingual sentiment analysis driven by compositional syntax-based rules.
EN-ES-CS: An English-Spanish Code-Switching Twitter Corpus for Multilingual Sentiment Analysis
Code-switching texts are those that contain terms in two or more different languages, and they appear increasingly often in social media.
One model, two languages: training bilingual parsers with harmonized treebanks
We introduce an approach to train lexicalized parsers using bilingual corpora obtained by merging harmonized treebanks of different languages, producing parsers that can analyze sentences in either of the learned languages, or even sentences that mix both.
