Term Extraction
40 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".
Benchmarks
These leaderboards are used to track progress in Term Extraction
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Libraries
Use these libraries to find Term Extraction models and implementations
PET
Most implemented papers
Aspect Term Extraction with History Attention and Selective Transformation
Aspect Term Extraction (ATE), a key sub-task in Aspect-Based Sentiment Analysis, aims to extract explicit aspect expressions from online user reviews.
Improving Aspect Term Extraction with Bidirectional Dependency Tree Representation
ArrowLuo/BiDTree
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The key idea is to explicitly incorporate both representations gained separately from the bottom-up and top-down propagation on the given dependency syntactic tree.
Aspect Sentiment Model for Micro Reviews
This paper aims at an aspect sentiment model for aspect-based sentiment analysis (ABSA) focused on micro reviews.
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction
This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity co-extraction.
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains
We perform an interdisciplinary large-scale evaluation for detecting lexical semantic divergences in a diachronic and in a synchronic task: semantic sense changes across time, and semantic sense changes across domains.
Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision
Lack of labeled training data is a major bottleneck for neural network based aspect and opinion term extraction on product reviews.
Feature-Less End-to-End Nested Term Extraction
CooDL/NestedTermExtraction
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In this paper, we proposed a deep learning-based end-to-end method on the domain specified automatic term extraction (ATE), it considers possible term spans within a fixed length in the sentence and predicts them whether they can be conceptual terms.
My Approach = Your Apparatus? Entropy-Based Topic Modeling on Multiple Domain-Specific Text Collections
Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers.
Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) involves three subtasks, i. e., aspect term extraction, opinion term extraction, and aspect-level sentiment classification.
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis
ArrowLuo/GRACE
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Findings of the Association for Computational Linguistics 2020
Specifically, a cascaded labeling module is developed to enhance the interchange between aspect terms and improve the attention of sentiment tokens when labeling sentiment polarities.
