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Found 69 papers, 34 papers with code
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.
Spanish Resource Grammar version 2023
We also present the grammar's coverage and overgeneration on a small portion of a learner corpus, an entirely new research line with respect to the SRG.
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.
Experimenting with UD Adaptation of an Unsupervised Rule-based Approach for Sentiment Analysis of Mexican Tourist Texts
This paper summarizes the results of experimenting with Universal Dependencies (UD) adaptation of an Unsupervised, Compositional and Recursive (UCR) rule-based approach for Sentiment Analysis (SA) submitted to the Shared Task at Rest-Mex 2023 (Team Olga/LyS-SALSA) (within the IberLEF 2023 conference).
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.
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 Impact of Edge Displacement Vaserstein Distance on UD Parsing Performance
We contribute to the discussion on parsing performance in NLP by introducing a measurement that evaluates the differences between the distributions of edge displacement (the directed distance of edges) seen in training and test data.
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.
A machine transliteration tool between Uzbek alphabets
Machine transliteration, as defined in this paper, is a process of automatically transforming written script of words from a source alphabet into words of another target alphabet within the same language, while preserving their meaning, as well as pronunciation.
SimRelUz: Similarity and Relatedness scores as a Semantic Evaluation dataset for Uzbek language
Semantic relatedness between words is one of the core concepts in natural language processing, thus making semantic evaluation an important task.
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.
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 distance minimization predicts compression
Dependency distance minimization (DDm) is a well-established principle of word order.
Splitting EUD graphs into trees: A quick and clatty approach
We present the system submission from the FASTPARSE team for the EUD Shared Task at IWPT 2021.
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.
What Taggers Fail to Learn, Parsers Need the Most
We present an error analysis of neural UPOS taggers to evaluate why using gold standard tags has such a large positive contribution to parsing performance while using predicted UPOS tags either harms performance or offers a negligible improvement.
Bertinho: Galician BERT Representations
The experiments show that our models, especially the 12-layer one, outperform the results of mBERT in most tasks.
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.
On the Frailty of Universal POS Tags for Neural UD Parsers
We present an analysis on the effect UPOS accuracy has on parsing performance.
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.
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.
The optimality of syntactic dependency distances
Here we recast the problem of the optimality of the word order of a sentence as an optimization problem on a spatial network where the vertices are words, arcs indicate syntactic dependencies and the space is defined by the linear order of the words in the sentence.
Bounds of the sum of edge lengths in linear arrangements of trees
A fundamental problem in network science is the normalization of the topological or physical distance between vertices, that requires understanding the range of variation of the unnormalized distances.
Efficient EUD Parsing
We present the system submission from the FASTPARSE team for the EUD Shared Task at IWPT 2020.
Distilling Neural Networks for Greener and Faster Dependency Parsing
The carbon footprint of natural language processing research has been increasing in recent years due to its reliance on large and inefficient neural network implementations.
Enriched In-Order Linearization for Faster Sequence-to-Sequence Constituent Parsing
Sequence-to-sequence constituent parsing requires a linearization to represent trees as sequences.
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.
Cross-Lingual Word Embeddings for Turkic Languages
Our experiments confirm that the obtained bilingual dictionaries outperform previously-available ones, and that word embeddings from a low-resource language can benefit from resource-rich closely-related languages when they are aligned together.
Inherent Dependency Displacement Bias of Transition-Based Algorithms
Empirical studies have shown that performance varies across different treebanks in such a way that one algorithm outperforms another on one treebank and the reverse is true for a different treebank.
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.
Towards robust word embeddings for noisy texts
Research on word embeddings has mainly focused on improving their performance on standard corpora, disregarding the difficulties posed by noisy texts in the form of tweets and other types of non-standard writing from social media.
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.
Memory limitations are hidden in grammar
The ability to produce and understand an unlimited number of different sentences is a hallmark of human language.
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.
Anti dependency distance minimization in short sequences. A graph theoretic approach
Dependency distance minimization (DDm) is a word order principle favouring the placement of syntactically related words close to each other in sentences.
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.
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.
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.
Ranked #11 on
Dependency Parsing
on Penn Treebank
Viable Dependency Parsing as Sequence Labeling
We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels.
Comparing Neural- and N-Gram-Based Language Models for Word Segmentation
The resulting system analyzes the text input with no word boundaries one token at a time, which can be a character or a byte, and uses the information gathered by the language model to determine if a boundary must be placed in the current position or not.
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.
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.
Global Transition-based Non-projective Dependency Parsing
Shi, Huang, and Lee (2017) obtained state-of-the-art results for English and Chinese dependency parsing by combining dynamic-programming implementations of transition-based dependency parsers with a minimal set of bidirectional LSTM features.
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.
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.
Improving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers
We generalize Cohen, G\'omez-Rodr\'iguez, and Satta's (2011) parser to a family of non-projective transition-based dependency parsers allowing polynomial-time exact inference.
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 dependency look at the reality of constituency
A comment on "Neurophysiological dynamics of phrase-structure building during sentence processing" by Nelson et al (2017), Proceedings of the National Academy of Sciences USA 114(18), E3669-E3678.
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.
Generic Axiomatization of Families of Noncrossing Graphs in Dependency Parsing
We present a simple encoding for unlabeled noncrossing graphs and show how its latent counterpart helps us to represent several families of directed and undirected graphs used in syntactic and semantic parsing of natural language as context-free languages.
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.
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.
On the relation between dependency distance, crossing dependencies, and parsing. Comment on "Dependency distance: a new perspective on syntactic patterns in natural languages" by Haitao Liu et al
Liu et al. (2017) provide a comprehensive account of research on dependency distance in human languages.
Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis
We present a novel unsupervised approach for multilingual sentiment analysis driven by compositional syntax-based rules.
The scarcity of crossing dependencies: a direct outcome of a specific constraint?
The structure of a sentence can be represented as a network where vertices are words and edges indicate syntactic dependencies.
Liberating language research from dogmas of the 20th century
A commentary on the article "Large-scale evidence of dependency length minimization in 37 languages" by Futrell, Mahowald & Gibson (PNAS 2015 112 (33) 10336-10341).
Crossings as a side effect of dependency lengths
The syntactic structure of sentences exhibits a striking regularity: dependencies tend to not cross when drawn above the sentence.
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.
