Entity Extraction using GAN
21 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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Latest papers
Robust Benchmarking for Machine Learning of Clinical Entity Extraction
We reformulate the annotation framework for clinical entity extraction to factor in these issues to allow for robust end-to-end system benchmarking.
Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction
The opinion entity extraction unit and the relation detection unit are developed as two channels to extract opinion entities and relations simultaneously.
Named Entity Extraction with Finite State Transducers
We describe a named entity tagging system that requires minimal linguistic knowledge and can be applied to more target languages without substantial changes.
MT-Clinical BERT: Scaling Clinical Information Extraction with Multitask Learning
Clinical notes contain an abundance of important but not-readily accessible information about patients.
Common-Knowledge Concept Recognition for SEVA
We build a common-knowledge concept recognition system for a Systems Engineer's Virtual Assistant (SEVA) which can be used for downstream tasks such as relation extraction, knowledge graph construction, and question-answering.
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks
This paper contributes to solving problems related to ambiguity in PICO sentence prediction tasks, as well as highlighting how annotations for training named entity recognition systems are used to train a high-performing, but nevertheless flexible architecture for question answering in systematic review automation.
Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science
This system meets computational information and knowledge management (CIKM) requirements of metadata-driven payload extraction, named entity extraction, and relationship extraction from text.
CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning
The model is extremely weak at differing the head and tail entity, resulting in inaccurate entity extraction.
A Unified MRC Framework for Named Entity Recognition
Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
EATEN: Entity-aware Attention for Single Shot Visual Text Extraction
Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts.