Entity Typing
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Entity Typing is an important task in text analysis. Assigning types (e.g., person, location, organization) to mentions of entities in documents enables effective structured analysis of unstructured text corpora. The extracted type information can be used in a wide range of ways (e.g., serving as primitives for information extraction and knowledge base (KB) completion, and assisting question answering). Traditional Entity Typing systems focus on a small set of coarse types (typically fewer than 10). Recent studies work on a much larger set of fine-grained types which form a tree-structured hierarchy (e.g., actor as a subtype of artist, and artist is a subtype of person).
Source: Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
Image Credit: Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CogComp/zoe
공식 코드https://github.com/HKUST-KnowComp/IFETEL
공식 코드https://github.com/HKUST-KnowComp/MLMET
공식 코드Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model
https://github.com/HKUST-KnowComp/cfet
공식 코드https://github.com/Hi-ZenanXu/Syntax-Enhanced_Pre-trained_Model
공식 코드https://github.com/JunhengH/joie-kdd19
공식 코드https://github.com/Kuaipedia/Kuaipedia
공식 코드Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia
https://github.com/MurtyShikhar/Hierarchical-Typing
공식 코드Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
https://github.com/THU-KEG/KEPLER
공식 코드KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
https://github.com/ZeweiChu/EntEval
공식 코드EntEval: A Holistic Evaluation Benchmark for Entity Representations
https://github.com/alexa/refined
공식 코드ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
https://github.com/alibaba-nlp/seqgpt
공식 코드SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding
https://github.com/alipay/parameter_inference_efficient_pie
공식 코드https://github.com/allenai/kb
공식 코드https://github.com/amazon-research/ReFinED
공식 코드ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
https://github.com/amazon-science/e2e-docie
공식 코드REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
https://github.com/bheinzerling/bpemb
공식 코드BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages
https://github.com/cambridgeltl/sw_study
공식 코드A Systematic Study of Leveraging Subword Information for Learning Word Representations
https://github.com/cciiplab/cet
공식 코드https://github.com/cciiplab/denoisefet
공식 코드Automatic Noisy Label Correction for Fine-Grained Entity Typing
https://github.com/chanzuckerberg/MedMentions
공식 코드Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
https://github.com/ckgconstruction/bdcp
공식 코드Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification
https://github.com/clairebarale/probing_legal_entity_types
공식 코드Do Language Models Learn about Legal Entity Types during Pretraining?
https://github.com/ctongfei/hierarchical-typing
공식 코드https://github.com/deezer/net-ner-probing
공식 코드Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition
https://github.com/donnyslin/KCAT
공식 코드https://github.com/fmtumbuka/eacl_encore
공식 코드EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains
https://github.com/gyeongmin47/kochet
공식 코드KoCHET: a Korean Cultural Heritage corpus for Entity-related Tasks
https://github.com/hananekth/facets_concepts_embeddings
공식 코드Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings
https://github.com/hldai/fivefine
공식 코드From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained
https://github.com/iesl/leopard
공식 코드Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
https://github.com/jiangshdd/learningtoselect
공식 코드https://github.com/kawatami/dner
공식 코드https://github.com/knowledge-computing/geolm
공식 코드GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
https://github.com/louchao98/nner_as_parsing
공식 코드Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.