Explore BiLSTM-CRF-Based Models for Open Relation Extraction

• URL: http://arxiv.org/abs/2104.12333v2
• Date: Tue, 9 Jul 2024 12:06:39 GMT
• Title: Explore BiLSTM-CRF-Based Models for Open Relation Extraction
• Authors: Tao Ni, Qing Wang, Gabriela Ferraro,
• Abstract summary: We develop several Open RE models based on the bidirectional LSTM-CRF (BiLSTM-CRF) neural network and different contextualized word embedding methods. We select the best combination of tagging scheme, word embedder, and BiLSTM-CRF network to achieve an Open RE model with a remarkable ability extracting on multiple-relation sentences.
• Score: 5.7380564196163855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting multiple relations from text sentences is still a challenge for current Open Relation Extraction (Open RE) tasks. In this paper, we develop several Open RE models based on the bidirectional LSTM-CRF (BiLSTM-CRF) neural network and different contextualized word embedding methods. We also propose a new tagging scheme to solve overlapping problems and enhance models' performance. From the evaluation results and comparisons between models, we select the best combination of tagging scheme, word embedder, and BiLSTM-CRF network to achieve an Open RE model with a remarkable extracting ability on multiple-relation sentences.

Related papers

• Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models [23.68266151581951]
  Retrieval-Augmented Generation (RAG) has been shown to enhance the factual accuracy of Large Language Models (LLMs) Existing methods often suffer from limited reasoning capabilities in effectively using the retrieved evidence. We introduce a novel framework, Open-RAG, designed to enhance reasoning capabilities in RAG with open-source LLMs.
  arXiv  Detail & Related papers  (2024-10-02T17:37:18Z)
• Relational Extraction on Wikipedia Tables using Convolutional and Memory Networks [6.200672130699805]
  Relation extraction (RE) is the task of extracting relations between entities in text. We introduce a new model consisting of Convolutional Neural Network (CNN) and Bidirectional-Long Short Term Memory (BiLSTM) network to encode entities.
  arXiv  Detail & Related papers  (2023-07-11T22:36:47Z)
• Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery [52.95935278819512]
  We conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDIALOG curated in our work. Inspired by causal discovery algorithms, we propose a novel model-agnostic method for training and inference of response generation model.
  arXiv  Detail & Related papers  (2023-03-02T06:33:48Z)
• Enriching Relation Extraction with OpenIE [70.52564277675056]
  Relation extraction (RE) is a sub-discipline of information extraction (IE) In this work, we explore how recent approaches for open information extraction (OpenIE) may help to improve the task of RE. Our experiments over two annotated corpora, KnowledgeNet and FewRel, demonstrate the improved accuracy of our enriched models.
  arXiv  Detail & Related papers  (2022-12-19T11:26:23Z)
• Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling [2.741266294612776]
  We introduce a new annotated corpus of Spanish newswire rich in unassimilated lexical borrowings. We use it to evaluate how several sequence labeling models (CRF, BiLSTM-CRF, and Transformer-based models) perform.
  arXiv  Detail & Related papers  (2022-03-30T09:46:51Z)
• Bidirectional LSTM-CRF Attention-based Model for Chinese Word Segmentation [2.3991565023534087]
  We propose a Bidirectional LSTM-CRF Attention-based Model for Chinese word segmentation. Our model performs better than the baseline methods modeling by other neural networks.
  arXiv  Detail & Related papers  (2021-05-20T11:46:53Z)
• A Correspondence Variational Autoencoder for Unsupervised Acoustic Word Embeddings [50.524054820564395]
  We propose a new unsupervised model for mapping a variable-duration speech segment to a fixed-dimensional representation. The resulting acoustic word embeddings can form the basis of search, discovery, and indexing systems for low- and zero-resource languages.
  arXiv  Detail & Related papers  (2020-12-03T19:24:42Z)
• Joint Extraction of Entity and Relation with Information Redundancy Elimination [0.0]
  We propose a joint extraction model to solve the problem of redundant information and overlapping relations of the entity and relation extraction model. This model can directly extract multiple pairs of related entities without generating unrelated information. We also propose a recurrent neural network named-LSTM that enhances the ability of recurrent units to model sentences.
  arXiv  Detail & Related papers  (2020-11-27T05:47:26Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Multi-Fact Correction in Abstractive Text Summarization [98.27031108197944]
  Span-Fact is a suite of two factual correction models that leverages knowledge learned from question answering models to make corrections in system-generated summaries via span selection. Our models employ single or multi-masking strategies to either iteratively or auto-regressively replace entities in order to ensure semantic consistency w.r.t. the source text. Experiments show that our models significantly boost the factual consistency of system-generated summaries without sacrificing summary quality in terms of both automatic metrics and human evaluation.
  arXiv  Detail & Related papers  (2020-10-06T02:51:02Z)
• Model Fusion via Optimal Transport [64.13185244219353]
  We present a layer-wise model fusion algorithm for neural networks. We show that this can successfully yield "one-shot" knowledge transfer between neural networks trained on heterogeneous non-i.i.d. data.
  arXiv  Detail & Related papers  (2019-10-12T22:07:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.