natural-language-processing-6

Speculation Detection

3260 papers • 126 benchmarks • 313 datasets

Identifying information in text that is speculative as opposed to factual information.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in speculation-detection-6

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find speculation-detection-6 models and implementations

adityak6798/Transformers-For-Negati…

2 papers 31

Datasets

No datasets available.

Subtasks

Speculation Scope Resolution

Most implemented papers

Multitask Learning of Negation and Speculation using Transformers

Aditya P. Khandelwal, Benita Kathleen Britto•Sat Oct 31 2020

It is shown that this Multitask Learning approach outperforms the single task learning approach, and new state-of-the-art results on Negation and Speculation Scope Resolution on the BioScope Corpus and the SFU Review Corpus are reported.

25

Content

0

Paper Graph

Resolving the Scope of Speculation and Negation using Transformer-Based Architectures

Aditya P. Khandelwal, Benita Kathleen Britto•Wed Jan 08 2020

Three popular transformer-based architectures, BERT, XLNet and RoBERTa are applied to negation detection and scope resolution, on two publicly available datasets, BioScope Corpus and SFU Review Corpus, reporting substantial improvements over previously reported results.

13 0

Paper Graph

Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection

Aixin Sun, Kuicai Dong, Jung-jae Kim, Xiaoli Li•Sat May 06 2023

This paper formally defines the research problem of tuple-level speculation detection and conducts a detailed data analysis on the LSOIE dataset which contains labels for speculative tuples to determine whether an extracted tuple is speculative.

2 0

Paper Graph

Adding a benchmark result helps the community track progress.