Term Extraction

36 papers with code • 2 benchmarks • 4 datasets

Term Extraction, or Automated Term Extraction (ATE), is about extraction domain-specific terms from natural language text. For example, the sentence “We meta-analyzed mortality using random-effect models” contains the domain-specific single-word terms "meta-analyzed", "mortality" and the multi-word term "random-effect models".


Use these libraries to find Term Extraction models and implementations

Most implemented papers

A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction

yangheng95/LCF-ATEPC 17 Dec 2019

Aspect-based sentiment analysis (ABSA) task is a multi-grained task of natural language processing and consists of two subtasks: aspect term extraction (ATE) and aspect polarity classification (APC).

An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis

lixin4ever/E2E-TBSA ACL 2019

Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence.

A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis

XL2248/DREGCN 4 Apr 2020

The aspect-based sentiment analysis (ABSA) task remains to be a long-standing challenge, which aims to extract the aspect term and then identify its sentiment orientation. In previous approaches, the explicit syntactic structure of a sentence, which reflects the syntax properties of natural language and hence is intuitively crucial for aspect term extraction and sentiment recognition, is typically neglected or insufficiently modeled.

SemRe-Rank: Improving Automatic Term Extraction By Incorporating Semantic Relatedness With Personalised PageRank

ziqizhang/semrerank 9 Nov 2017

Extensively evaluated with 13 state-of-the-art base ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all base methods and across all datasets, with up to 15 percentage points when measured by the Precision in the top ranked K candidate terms (the average for a set of K's), or up to 28 percentage points in F1 measured at a K that equals to the expected real terms in the candidates (F1 in short).

An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis

XL2248/IKTN Findings (EMNLP) 2021

Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification, which are typically handled in a separate or joint manner.

Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction

chiayewken/Span-ASTE ACL 2021

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.

PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis

yangheng95/pyabsa 2 Aug 2022

The advancement of aspect-based sentiment analysis (ABSA) has urged the lack of a user-friendly framework that can largely lower the difficulty of reproducing state-of-the-art ABSA performance, especially for beginners.

GROBID: Combining Automatic Bibliographic Data Recognition and Term Extraction for Scholarship Publications

kermitt2/grobid Research and Advanced Technology for Digital Libraries 2009

Based on state of the art machine learning techniques, GROBID (GeneRation Of BIbliographic Data) performs reliable bibliographic data extractions from scholar articles combined with multi-level term extractions.

JATE 2.0: Java Automatic Term Extraction with Apache Solr

ziqizhang/jate LREC 2016

Automatic Term Extraction (ATE) or Recognition (ATR) is a fundamental processing step preceding many complex knowledge engineering tasks.

Aspect-Based Relational Sentiment Analysis Using a Stacked Neural Network Architecture

santhoshmani888/Aspect-Based-sentiment-analysis 19 Sep 2017

We present a novel neural architecture for sentiment analysis as a relation extraction problem that addresses this problem by dividing it into three subtasks: i) identification of aspect and opinion terms, ii) labeling of opinion terms with a sentiment, and iii) extraction of relations between opinion terms and aspect terms.