LCGbank: A Corpus of Syntactic Analyses Based on Proof Nets (2024.lrec-main)

Copied to clipboard

Challenge: Recent studies have focused on statistical syntactic parsing with proof nets . however, there has been a paucity of corpora in formalisms for which proof net is applicable .
Approach: They propose a corpus of syntactic analyses based on Lambek categorial grammar . they leverage the relationship between LCG and CCG to address this problem .
Outcome: The proposed method exploits the relationship between LCG and CCG to build an English-language corpus of syntactic analyses based on proof nets . the results suggest that the proposed method is weakly context-free equivalent and NP-complete .

Similar Papers

Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation (P19-1)

Copied to clipboard

Challenge: Existing methods for Combinatory Categorial Grammar (CCG) parsing are limited to a specific parser architecture, making it non-trivial to apply to current parsers.
Approach: They propose a domain adaptation method for Combinatory Categorial Grammar (CCG) they propose to generate CCG corpora using cheaper dependency trees.
Outcome: The proposed method improves on speech conversation and math problems.
Development of a Multilingual CCG Treebank via Universal Dependencies Conversion (2022.lrec-1)

Copied to clipboard

Challenge: Combinatory Categorial Grammar (CCG) is a lexicalized grammar formalism that can capture both syntactic and semantic information.
Approach: They propose an algorithm to convert UD treebanks to CCG treebank and propose future extensions.
Outcome: The proposed algorithm performs lexical, sentential, and syntactic rule coverage analysis, as well as CCG parsing experiments.
Integrating Headedness Information into an Auto-generated Multilingual CCGbank for Improved Semantic Interpretation (2024.lrec-main)

Copied to clipboard

Challenge: Combinatory Categorial Grammar is a grammar formalism that provides a transparent interface between syntax and semantics.
Approach: They propose an algorithm that adds semantic representations to existing CCG derivations by combining them with predefined combinatory rules.
Outcome: The proposed method produces bare CCG derivations without any accompanying semantic representations and limits its general applicability.
A Generative Model for Lambek Categorial Sequents (2024.lrec-main)

Copied to clipboard

Challenge: generative models such as PLC+ generate grammatical sentences with a high probability of being grammatized.
Approach: They propose a generative model, PLC+, for generating Lambek Categorial Grammar(LCG) sequents.
Outcome: The proposed model generates Lambek Categorial Grammar(LCG) sequents and is more robust to probabilistic context-free grammars.
From Informal to Formal – Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofs (2025.acl-long)

Copied to clipboard

Challenge: Recent studies in formal mathematical reasoning have shown an unstoppable growth trend.
Approach: They constructed 18k high-quality instruction-response pairs across five mainstream formal specification languages and evaluated them against ten open-sourced LLMs.
Outcome: The proposed model compared instruction-response pairs across five formal specification languages and found that the LLMs were good at writing proof segments when given either the code, or the detailed description of proof steps.
Reforging : A Method for Constructing a Linguistically Valid Japanese CCG Treebank (2024.eacl-srw)

Copied to clipboard

Challenge: Existing treebanks for Combinatory Categorial Grammar (CCG) are insufficient for linguistic validity of CCG .
Approach: They propose to combine ABCTreebank and lightblue to generate a linguistically valid Japanese CCG treebank with detailed information by filtering lightblu's lexical items using ABCTtreebank.
Outcome: The proposed method generates a linguistically valid Japanese CCG treebank with detailed information by combining the strengths of ABCTreebank and lightblue.
Valency-Augmented Dependency Parsing (D18-1)

Copied to clipboard

Challenge: valency analysis is a complex task that requires a large number of subcategorizations, such as the number and types of syntactic dependents.
Approach: They propose a parsing approach that explicitly models the number and types of syntactic dependents as valency patterns and a probabilistic model for tagging them.
Outcome: The proposed approach outperforms the state-of-the-art labeled attachment score on 53 treebanks representing 41 languages and outperformed the previous state- of-the art labeles by 0.7.
Strong Equivalence of TAG and CCG (2021.tacl-1)

Copied to clipboard

Challenge: Tree-adjoining grammar and combinatory categorial grammar have the same expressive power on trees.
Approach: Tree-adjoining grammar (TAG) and combinatory categorial grammar (CCG) are well-established grammars with the same expressive power on strings.
Outcome: The proposed grammars have the same expressive power on trees as classical grammars and can express a limited amount of cross-serial dependencies and have the constant growth property.
Development of a General-Purpose Categorial Grammar Treebank (2020.lrec-1)

Copied to clipboard

Challenge: 'general-purpose' categorial grammar treebank is not tailored to specific variants of CG, but rather offers a theory-neutral linguistic resource that can be converted to different versions of 'type-logical grammar' .
Approach: They propose a general-purpose categorial grammar treebank for Japanese that is not tailored to a specific variant of CG but rather offers a theory-neutral resource which can be converted to different versions of GC relatively easily.
Outcome: The proposed treebank improves on the existing Japanese CG treebank on the treatment of certain linguistic phenomena (passives, causatives, and control/raising predicates).
BERT-Proof Syntactic Structures: Investigating Errors in Discontinuous Constituency Parsing (2021.findings-acl)

Copied to clipboard

Challenge: Recent results show that pretrained language models can be used for many tasks with high accuracy and high performance.
Approach: They propose two methods for automatically analysing discontinuous parsers' errors.
Outcome: The proposed methods characterize errors of a state-of-the-art transition-based discontinuous parser and provide an overview of the contribution of BERT to this task.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations