ArticleComputational intelligence and neuroscience2021
A Sentence-Level Joint Relation Classification Model Based on Reinforcement Learning.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- A review of reinforcement learning for natural language processing and applications in healthcare.Journal of the American Medical Informatics Association : JAMIA · 2024Review
- ChatGPT, GPT-4, and Other Large Language Models: The Next Revolution for Clinical Microbiology?Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
- Automatic knowledge extraction from Chinese electronic medical records and rheumatoid arthritis knowledge graph construction.Quantitative imaging in medicine and surgery · 2023Article
- Utilizing Entity-Based Gated Convolution and Multilevel Sentence Attention to Improve Distantly Supervised Relation Extraction.Computational intelligence and neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Relation classification is an important semantic processing task in the field of natural language processing (NLP). Data sources generally adopt remote monitoring strategies to automatically generate large-scale training data, which inevitably causes label noise problems. At the same time, another challenge is that important information can appear at any place in the sentence. This paper presents a sentence-level joint relation classification model. The model has two modules: a reinforcement learning (RL) agent and a joint network model. In particular, we combine bidirectional long short-term memory (Bi-LSTM) and attention mechanism as a joint model to process the text features of sentences and classify the relation between two entities. At the same time, we introduce an attention mechanism to discover hidden information in sentences. The joint training of the two modules solves the noise problem in relation extraction, sentence-level information extraction, and relation classification. Experimental results demonstrate that the model can effectively deal with data noise and achieve better relation classification performance at the sentence level.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.