Learning to Extract Structured Entities Using Language Models

• URL: http://arxiv.org/abs/2402.04437v4
• Date: Tue, 18 Jun 2024 22:11:27 GMT
• Title: Learning to Extract Structured Entities Using Language Models
• Authors: Haolun Wu, Ye Yuan, Liana Mikaelyan, Alexander Meulemans, Xue Liu, James Hensman, Bhaskar Mitra,
• Abstract summary: Recent advances in machine learning have significantly impacted the field of information extraction. We reformulate the task to be entity-centric, enabling the use of diverse metrics that can provide more insights. We introduce a new model that harnesses the power of Language Models (LMs) for enhanced effectiveness and efficiency.
• Score: 52.281701191329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in machine learning have significantly impacted the field of information extraction, with Language Models (LMs) playing a pivotal role in extracting structured information from unstructured text. Prior works typically represent information extraction as triplet-centric and use classical metrics such as precision and recall for evaluation. We reformulate the task to be entity-centric, enabling the use of diverse metrics that can provide more insights from various perspectives. We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP (AESOP) metric, designed to appropriately assess model performance. Later, we introduce a new model that harnesses the power of LMs for enhanced effectiveness and efficiency by decomposing the extraction task into multiple stages. Quantitative and human side-by-side evaluations confirm that our model outperforms baselines, offering promising directions for future advancements in structured entity extraction.

Related papers

• Leveraging Large Language Models for Mobile App Review Feature Extraction [4.879919005707447]
  This study explores the hypothesis that encoder-only large language models can enhance feature extraction from mobile app reviews. By leveraging crowdsourced annotations from an industrial context, we redefine feature extraction as a supervised token classification task. Empirical evaluations demonstrate that this method improves the precision and recall of extracted features and enhances performance efficiency.
  arXiv  Detail & Related papers  (2024-08-02T07:31:57Z)
• Contextualization Distillation from Large Language Model for Knowledge Graph Completion [51.126166442122546]
  We introduce the Contextualization Distillation strategy, a plug-in-and-play approach compatible with both discriminative and generative KGC frameworks. Our method begins by instructing large language models to transform compact, structural triplets into context-rich segments. Comprehensive evaluations across diverse datasets and KGC techniques highlight the efficacy and adaptability of our approach.
  arXiv  Detail & Related papers  (2024-01-28T08:56:49Z)
• Instruct and Extract: Instruction Tuning for On-Demand Information Extraction [86.29491354355356]
  On-Demand Information Extraction aims to fulfill the personalized demands of real-world users. We present a benchmark named InstructIE, inclusive of both automatically generated training data, as well as the human-annotated test set. Building on InstructIE, we further develop an On-Demand Information Extractor, ODIE.
  arXiv  Detail & Related papers  (2023-10-24T17:54:25Z)
• Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction [57.854498238624366]
  We propose a retrieval-augmented approach, which retrieves schema-aware Reference As Prompt (RAP) for data-efficient knowledge graph construction. RAP can dynamically leverage schema and knowledge inherited from human-annotated and weak-supervised data as a prompt for each sample.
  arXiv  Detail & Related papers  (2022-10-19T16:40:28Z)
• DeepStruct: Pretraining of Language Models for Structure Prediction [64.84144849119554]
  We pretrain language models on a collection of task-agnostic corpora to generate structures from text. Our structure pretraining enables zero-shot transfer of the learned knowledge that models have about the structure tasks. We show that a 10B parameter language model transfers non-trivially to most tasks and obtains state-of-the-art performance on 21 of 28 datasets.
  arXiv  Detail & Related papers  (2022-05-21T00:58:22Z)
• Modeling Multi-Granularity Hierarchical Features for Relation Extraction [26.852869800344813]
  We propose a novel method to extract multi-granularity features based solely on the original input sentences. We show that effective structured features can be attained even without external knowledge.
  arXiv  Detail & Related papers  (2022-04-09T09:44:05Z)
• Incorporating Linguistic Knowledge for Abstractive Multi-document Summarization [20.572283625521784]
  We develop a neural network based abstractive multi-document summarization (MDS) model. We process the dependency information into the linguistic-guided attention mechanism. With the help of linguistic signals, sentence-level relations can be correctly captured.
  arXiv  Detail & Related papers  (2021-09-23T08:13:35Z)
• Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making [22.755892575582788]
  Entity Matching aims at recognizing entity records that denote the same real-world object. We propose a novel EM framework that consists of Heterogeneous Information Fusion (HIF) and Key Attribute Tree (KAT) Induction. Our method is highly efficient and outperforms SOTA EM models in most cases.
  arXiv  Detail & Related papers  (2021-06-08T08:27:31Z)
• A Span Extraction Approach for Information Extraction on Visually-Rich Documents [2.3131309703965135]
  We present a new approach to improve the capability of language model pre-training on visually-rich documents (VRDs) Firstly, we introduce a new IE model that is query-based and employs the span extraction formulation instead of the commonly used sequence labelling approach. We also propose a new training task which focuses on modelling the relationships between semantic entities within a document.
  arXiv  Detail & Related papers  (2021-06-02T06:50:04Z)
• A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
  We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn) Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-04T14:59:32Z)

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.