Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions
원문 초록
Temporal and numerical expression understanding is of great importance in many downstream Natural Language Processing (NLP) and Information Retrieval (IR) tasks. However, much previous work covers only a few sub-types and focuses only on entity extraction, which severely limits the usability of identified mentions. In order for such entities to be useful in downstream scenarios, coverage and granularity of sub-types are important; and, even more so, providing resolution into concrete values that can be manipulated. Furthermore, most previous work addresses only a handful of languages. Here we describe a multi-lingual evaluation dataset - NTX - covering diverse temporal and numerical expressions across 14 languages and covering extraction, normalization, and resolution. Along with the dataset we provide a robust rule-based system as a strong baseline for comparisons against other models to be evaluated in this dataset. Data and code are available at \url{https://aka.ms/NTX}.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Efficiency-Effectiveness Reranking FLOPs for LLM-based Rerankers
Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models
JointRank: Rank Large Set with Single Pass
A Hierarchical Deep Learning Approach for Minority Instrument Detection
From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
토론
이 논문과 연결된 포럼 글이 아직 없습니다.