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Document-level Closed Information Extraction

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하위 태스크: 0
평가표: 3
보존된 평가 기록: 3

Document-level closed information extraction (DocIE) is a subtask of information extraction that seeks to extract a set of triplets, or facts, of the form (subject, relation, object) from unstructured texts that are fully linked to a reference knowledge base, i.e., consistent with a predefined set of entities and relations from a knowledge base. DocIE entails tasks such as mention detection, entity typing, named entity recognition, entity disambiguation, entity linking, coreference resolution, and document-level relation extraction. DocIE is more challenging than sentence-level closed information extraction as it involves capturing long-range dependencies effectively to extract relations between entities that are further apart from each other in the text. Another difference is that DocIE necessitates a coreference resolution stage to group all the different mentions in the document referring to the same entity. DocIE is crucial for applications such as knowledge graph construction, question answering, knowledge discovery, or text summarization.

Source: REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking

데이터셋

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DocRED

DocRED (Document-Level Relation Extraction Dataset) is a relation extraction dataset constructed from Wikipedia and Wikidata. Each document in the dataset is human-annotated with named entity mentions, coreference information, intra- and inter-sentence relations, and supporting evidence. DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document. Along with the human-annotated data, the dataset provides large-scale distantly supervised data. DocRED contains 132,375 entities and 56,354 relational facts annotated on 5,053 Wikipedia documents. In addition to the human-annotated data, the dataset provides large-scale distantly supervised data over 101,873 documents. Source: DocRED: A Large-Scale Document-Level Relation Extraction Dataset Image Source: DocRED: A Large-Scale Document-Level Relation Extraction Dataset

DWIE

The 'Deutsche Welle corpus for Information Extraction' (DWIE) is a multi-task dataset that combines four main Information Extraction (IE) annotation sub-tasks: (i) Named Entity Recognition (NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv) Entity Linking. DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document. Source: https://arxiv.org/abs/2009.12626

DocRED-IE

The DocRED Information Extraction (DocRED-IE) dataset extends the DocRED dataset for the Document-level Closed Information Extraction (DocIE) task. DocRED-IE is a multi-task dataset and allows for 5 subtasks: (i) Document-level Relation Extraction, (ii) Mention Detection, (iii) Entity Typing, (iv) Entity Disambiguation, (v) Coreference Resolution, as well as combinations thereof such as Named Entity Recognition (NER) or Entity Linking. The DocRED-IE dataset also allows for the end-to-end tasks of: (i) DocIE and (ii) Joint Entity and Relation Extraction. DocRED-IE comprises sentence-level and document-level facts, thereby describing short as well as long-range interactions within an entire document.

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