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Information Extraction

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하위 태스크: 16
평가표: 1
보존된 평가 기록: 1

Information extraction is the task of automatically extracting structured information from unstructured and / or semi-structured machine-readable documents and other electronically represented sources (Source: Wikipedia).

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/atharsefid/scibertsum

공식 코드

SciBERTSUM: Extractive Summarization for Scientific Documents

https://github.com/aub-mind/araBERT

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AraBERT: Transformer-based Model for Arabic Language Understanding

https://github.com/avramandrei/DeftEval

공식 코드

UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction

https://github.com/ayoubhammal/ckss4ner

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Few-Shot Domain Adaptation for Named-Entity Recognition via Joint Constrained k-Means and Subspace Selection

https://github.com/azminewasi/banglaautokg

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BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering

https://github.com/badripatro/mamba360

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Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges

https://github.com/baoguangsheng/ctx-rewriter-for-summ

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Contextualized Rewriting for Text Summarization

https://github.com/baozuyi/MLMA

공식 코드

Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations

https://github.com/bauerem/legal_memsum

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Legal Extractive Summarization of U.S. Court Opinions

https://github.com/bekou/multihead_joint_entity_relation_extraction

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Adversarial training for multi-context joint entity and relation extraction

https://github.com/beyonderxx/miner

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MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective

https://github.com/beyondguo/genius

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GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation

https://github.com/beyondguo/llm-tuning

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Sample Design Engineering: An Empirical Study of What Makes Good Downstream Fine-Tuning Samples for LLMs

https://github.com/bflashcp3f/deer

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Label-Guided In-Context Learning for Named Entity Recognition

https://github.com/bids-xu-lab/clinical_entity_recognition_using_gpt_models

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Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering

https://github.com/bionlproc/end-to-end-combdrugext

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End-to-End $n$-ary Relation Extraction for Combination Drug Therapies

https://github.com/bionlproc/twitter-covid-qa-extraction

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COVID-19 event extraction from Twitter via extractive question answering with continuous prompts

https://github.com/bionlu-coling2024/biomed-ner-intent_detection

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Intent Detection and Entity Extraction from BioMedical Literature

https://github.com/bishanyang/EventEntityExtractor

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Joint Extraction of Events and Entities within a Document Context

https://github.com/bladedancer957/finer

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Federated Incremental Named Entity Recognition

https://github.com/blazejdolicki/multilingual-analysis

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Analysing The Impact Of Linguistic Features On Cross-Lingual Transfer

https://github.com/blcuicall/nlp-crowdsourcing

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Cost-efficient Crowdsourcing for Span-based Sequence Labeling: Worker Selection and Data Augmentation

https://github.com/blester125/constrained-decoding

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Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers

https://github.com/bloomberg/kbir_keybart

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Learning Rich Representation of Keyphrases from Text

https://github.com/bltlab/seqscore

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SeqScore: Addressing Barriers to Reproducible Named Entity Recognition Evaluation

https://github.com/bo1929/anelfop

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Focusing on Potential Named Entities During Active Label Acquisition

https://github.com/boleima/topro

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ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks

https://github.com/bonaventuredossou/mlm_al

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AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages

https://github.com/bonaventuredossou/multitask_fon

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FonMTL: Towards Multitask Learning for the Fon Language

https://github.com/boschresearch/adversarial_meta_embeddings

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FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations

https://github.com/boschresearch/boschai-cnc-shared-task-ranlp2023

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BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation

https://github.com/botondbarta/hunsum

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From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization

https://github.com/bowang-lab/joint-ner-and-re

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End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models

https://github.com/bplank/DaNplus

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DaN+: Danish Nested Named Entities and Lexical Normalization

https://github.com/bplank/danish_ner_transfer

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Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in Danish

https://github.com/br-ai-ns-institute/zero-shotner

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From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts

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