Pith. sign in

REVIEW 2 cited by

A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1711.07010 v5 pith:FZT4QE5I submitted 2017-11-19 cs.CL

Jingjing Xu , Ji Wen , Xu Sun , Qi Su This is my paper · ORCID
classification cs.CL
keywords datasettaggingchineseliteraturebuilddiscourse-levelentityextraction
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Named Entity Recognition and Relation Extraction for Chinese literature text is regarded as the highly difficult problem, partially because of the lack of tagging sets. In this paper, we build a discourse-level dataset from hundreds of Chinese literature articles for improving this task. To build a high quality dataset, we propose two tagging methods to solve the problem of data inconsistency, including a heuristic tagging method and a machine auxiliary tagging method. Based on this corpus, we also introduce several widely used models to conduct experiments. Experimental results not only show the usefulness of the proposed dataset, but also provide baselines for further research. The dataset is available at https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. FlexNER: A Flexible LSTM-CNN Stack Framework for Named Entity Recognition

    cs.CL 2019-08 conditional novelty 6.0 of 10

    Swapping same-type entities inside the training data, and training a second network on the augmented sentences, yields small, consistent gains on English NER and larger gains on a small cell-line corpus.

  2. Mitigating Stylistic Biases of Machine Translation Systems via Monolingual Corpora Only

    cs.CL 2025-07 reject novelty 5.0 of 10

    Babel detects and repairs stylistic mismatches in machine translation outputs using a style detector and a diffusion-based applicator trained on monolingual corpora.

Pith tools