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A comprehensive survey on automatic knowledge graph construction
Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …
knowledge. To this end, much effort has historically been spent extracting informative fact …
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
This article surveys and organizes research works in a new paradigm in natural language
processing, which we dub “prompt-based learning.” Unlike traditional supervised learning …
processing, which we dub “prompt-based learning.” Unlike traditional supervised learning …
Prompting gpt-3 to be reliable
Large language models (LLMs) show impressive abilities via few-shot prompting.
Commercialized APIs such as OpenAI GPT-3 further increase their use in real-world …
Commercialized APIs such as OpenAI GPT-3 further increase their use in real-world …
Large language models are few-shot clinical information extractors
A long-running goal of the clinical NLP community is the extraction of important variables
trapped in clinical notes. However, roadblocks have included dataset shift from the general …
trapped in clinical notes. However, roadblocks have included dataset shift from the general …
Unified structure generation for universal information extraction
Information extraction suffers from its varying targets, heterogeneous structures, and
demand-specific schemas. In this paper, we propose a unified text-to-structure generation …
demand-specific schemas. In this paper, we propose a unified text-to-structure generation …
Knowledge enhanced contextual word representations
Contextual word representations, typically trained on unstructured, unlabeled text, do not
contain any explicit grounding to real world entities and are often unable to remember facts …
contain any explicit grounding to real world entities and are often unable to remember facts …
Superglue: A stickier benchmark for general-purpose language understanding systems
In the last year, new models and methods for pretraining and transfer learning have driven
striking performance improvements across a range of language understanding tasks. The …
striking performance improvements across a range of language understanding tasks. The …
Learning span-level interactions for aspect sentiment triplet extraction
Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs
triplets of an aspect target, its associated sentiment, and the corresponding opinion term …
triplets of an aspect target, its associated sentiment, and the corresponding opinion term …
Spanbert: Improving pre-training by representing and predicting spans
We present SpanBERT, a pre-training method that is designed to better represent and
predict spans of text. Our approach extends BERT by (1) masking contiguous random spans …
predict spans of text. Our approach extends BERT by (1) masking contiguous random spans …
Span-based joint entity and relation extraction with transformer pre-training
We introduce SpERT, an attention model for span-based joint entity and relation extraction.
Our key contribution is a light-weight reasoning on BERT embeddings, which features entity …
Our key contribution is a light-weight reasoning on BERT embeddings, which features entity …