pith. sign in

arxiv: cs/0306050 · v1 · pith:BQL7ASMDnew · submitted 2003-06-12 · 💻 cs.CL

Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition

Erik F. Tjong Kim Sang , Fien De Meulder This is my paper
classification 💻 cs.CL
keywords taskconll-2003entitylanguage-independentnamedrecognitionsharedbackground
0
0 comments X
read the original abstract

We describe the CoNLL-2003 shared task: language-independent named entity recognition. We give background information on the data sets (English and German) and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Soft Head Selection for Injecting ICL-Derived Task Embeddings

    cs.CL 2025-07 conditional novelty 7.0

    SITE applies soft gradient-based head selection to inject ICL-derived task embeddings, outperforming prior embedding adaptation and few-shot ICL across generation, reasoning, and NLU tasks on 12 LLMs from 4B to 70B pa...

  2. LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

    cs.CV 2023-03 conditional novelty 7.0

    LLaMA-Adapter turns frozen LLaMA 7B into a capable instruction follower using only 1.2M new parameters and zero-init attention, matching Alpaca while extending to image-conditioned reasoning on ScienceQA and COCO.

  3. BCL: Bayesian In-Context Learning Framework for Information Extraction

    cs.CL 2026-06 unverdicted novelty 6.0

    BCL introduces a particle-filtering Bayesian update framework to systematically refine label representations in in-context learning for information extraction, claiming consistent gains over prior methods.

  4. An Agentic Workflow for Detecting Personally Identifiable Information in Crash Narratives

    cs.CR 2026-04 unverdicted novelty 6.0

    A hybrid agentic workflow using Presidio for structured PII and fine-tuned LLMs plus verification for names, addresses, and identifiers detects PII in crash narratives at 0.82 precision and 0.94 recall.

  5. LIMO: Less is More for Reasoning

    cs.CL 2025-02 unverdicted novelty 6.0

    LIMO achieves 63.3% on AIME24 and 95.6% on MATH500 via supervised fine-tuning on roughly 1% of the data used by prior models, supporting the claim that minimal strategic examples suffice when pre-training has already ...

  6. When Active Learning Falls Short: An Empirical Study on Chemical Reaction Extraction

    cs.LG 2026-04 unverdicted novelty 5.0

    Active learning for chemical reaction extraction frequently produces non-monotonic learning curves and fails to deliver stable gains over random sampling because of strong pretraining, structured CRF decoding, and lab...

  7. Beyond the Basics: Leveraging Large Language Model for Fine-Grained Medical Entity Recognition

    cs.AI 2026-04 conditional novelty 4.0

    Fine-tuned LLaMA3 with LoRA reaches 81.24% F1 on 18-category fine-grained medical entity recognition, beating zero-shot by 63.11% and few-shot by 35.63%.

  8. To Tune or Not To Tune? How About the Best of Both Worlds?

    cs.CL 2019-07 unverdicted novelty 3.0

    A sequential fine-tuning strategy for pre-trained language models reports modest accuracy gains of 4.7%, 0.99%, and 0.72% on semantic similarity, sequence labeling, and text classification tasks.