Papers Semantic Role Labeling
“Semantic Role Labeling” 태그가 달린 논문 630편 · 필터 해제
A Systematic Evaluation of Traditional Privacy Policy Analysis Tools Against LLMs
The advent of LLMs has significantly changed the research on privacy policy and data compliance analysis by enabling tasks that previously required specialized, domain-specific tools. However, it remains unclear to what …
Semantic Role LabelingThreatCore: A Benchmark for Explicit and Implicit Threat Detection
Threat detection in Natural Language Processing lacks consistent definitions and standardized benchmarks, and is often conflated with broader phenomena such as toxicity, hate speech, or offensive language. In this work, …
Semantic Role LabelingRevisiting Semantic Role Labeling: Efficient Structured Inference with Dependency-Informed Analysis
Semantic Role Labeling (SRL) provides an explicit representation of predicate-argument structure, capturing linguistically grounded relations such as who did what to whom. While recent NLP progress has been dominated by …
Semantic Role LabelingGROUNDEDKG-RAG: Grounded Knowledge Graph Index for Long-document Question Answering
Retrieval-augmented generation (RAG) systems have been widely adopted in contemporary large language models (LLMs) due to their ability to improve generation quality while reducing the required input context length. In t…
Semantic Role LabelingQuestion AnsweringEffective QA-driven Annotation of Predicate-Argument Relations Across Languages
Explicit representations of predicate-argument relations form the basis of interpretable semantic analysis, supporting reasoning, generation, and evaluation. However, attaining such semantic structures requires costly an…
Semantic Role LabelingFrom Semantic Roles to Opinion Roles: SRL Data Extraction for Multi-Task and Transfer Learning in Low-Resource ORL
This report presents a detailed methodology for constructing a high-quality Semantic Role Labeling (SRL) dataset from the Wall Street Journal (WSJ) portion of the OntoNotes 5.0 corpus and adapting it for Opinion Role Lab…
Semantic Role LabelingTransfer LearningOpinion MiningHSGM: Hierarchical Segment-Graph Memory for Scalable Long-Text Semantics
Semantic parsing of long documents remains challenging due to quadratic growth in pairwise composition and memory requirements. We introduce \textbf{Hierarchical Segment-Graph Memory (HSGM)}, a novel framework that decom…
Semantic Role LabelingEvent ExtractionSemantic ParsingAMR ParsingThe Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning
Context recognition (SR) is a fundamental task in computer vision that aims to extract structured semantic summaries from images by identifying key events and their associated entities. Specifically, given an input image…
Semantic Role LabelingSituation RecognitionMulti-Label LearningClassifier Language Models: Unifying Sparse Finetuning and Adaptive Tokenization for Specialized Classification Tasks
Semantic text classification requires the understanding of the contextual significance of specific tokens rather than surface-level patterns or keywords (as in rule-based or statistical text classification), making large…
Semantic Role LabelingText ClassificationIntent DetectionExploring In-Context Learning for Frame-Semantic Parsing
Frame Semantic Parsing (FSP) entails identifying predicates and labeling their arguments according to Frame Semantics. This paper investigates the use of In-Context Learning (ICL) with Large Language Models (LLMs) to per…
Semantic Role LabelingSemantic ParsingIdentifying economic narratives in large text corpora -- An integrated approach using Large Language Models
As interest in economic narratives has grown in recent years, so has the number of pipelines dedicated to extracting such narratives from texts. Pipelines often employ a mix of state-of-the-art natural language processin…
ArticlesSemantic Role LabelingvalidFRASE: Structured Representations for Generalizable SPARQL Query Generation
Translating natural language questions into SPARQL queries enables Knowledge Base querying for factual and up-to-date responses. However, existing datasets for this task are predominantly template-based, leading models t…
Semantic Role LabelingActive Data Sampling and Generation for Bias Remediation
Adequate sampling space coverage is the keystone to effectively train trustworthy Machine Learning models. Unfortunately, real data do carry several inherent risks due to the many potential biases they exhibit when gathe…
FairnessSemantic Role LabelingSemantic Role Labeling: A Systematical Survey
Semantic role labeling (SRL) is a central natural language processing (NLP) task aiming to understand the semantic roles within texts, facilitating a wide range of downstream applications. While SRL has garnered extensiv…
Semantic Role LabelingSurveySemantic Role Labeling of NomBank Partitives
This article is about Semantic Role Labeling for English partitive nouns (5%/REL of the price/ARG1; The price/ARG1 rose 5 percent/REL) in the NomBank annotated corpus. Several systems are described using traditional and …
Semantic Role LabelingWhen and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context
We introduce a neural architecture finetuned for the task of scenario context generation: The relevant location and time of an event or entity mentioned in text. Contextualizing information extraction helps to scope the …
Data AugmentationDecoderEpidemiologyKnowledge Graphs+1Unlocking Korean Verbs: A User-Friendly Exploration into the Verb Lexicon
The Sejong dictionary dataset offers a valuable resource, providing extensive coverage of morphology, syntax, and semantic representation. This dataset can be utilized to explore linguistic information in greater depth. …
Semantic Role LabelingSentenceA New Method for Cross-Lingual-based Semantic Role Labeling
Semantic role labeling is a crucial task in natural language processing, enabling better comprehension of natural language. However, the lack of annotated data in multiple languages has posed a challenge for researchers.…
Semantic Role LabelingEffectively Leveraging CLIP for Generating Situational Summaries of Images and Videos
Situation recognition refers to the ability of an agent to identify and understand various situations or contexts based on available information and sensory inputs. It involves the cognitive process of interpreting data …
Semantic Role LabelingVideo CaptioningDAHRS: Divergence-Aware Hallucination-Remediated SRL Projection
Semantic role labeling (SRL) enriches many downstream applications, e.g., machine translation, question answering, summarization, and stance/belief detection. However, building multilingual SRL models is challenging due …
fr-enHallucinationMachine TranslationQuestion Answering+1