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Found 23 papers, 10 papers with code
Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction
A relation tuple consists of two entities and the relation between them, and often such tuples are found in unstructured text.
Ranked #1 on
Relation Extraction
on NYT24
Deep Neural Networks for Relation Extraction
Relation extraction from text is an important task for automatic knowledge base population.
Joint Entity and Relation Extraction
Knowledge Base Population
+1
PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment.
Effective Attention Modeling for Neural Relation Extraction
Relation extraction is the task of determining the relation between two entities in a sentence.
Aspect Sentiment Triplet Extraction Using Reinforcement Learning
We first focus on sentiments expressed in a sentence, then identify the target aspect and opinion terms for that sentiment.
Ranked #6 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
Aspect Sentiment Triplet Extraction
reinforcement-learning
+2
FinRED: A Dataset for Relation Extraction in Financial Domain
Relation extraction models trained on a source domain cannot be applied on a different target domain due to the mismatch between relation sets.
A Generative Approach for Financial Causality Extraction
Causality represents the foremost relation between events in financial documents such as financial news articles, financial reports.
A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents
Distantly supervised datasets for relation extraction mostly focus on sentence-level extraction, and they cover very few relations.
tagE: Enabling an Embodied Agent to Understand Human Instructions
At its core, our system employs an inventive neural network model designed to extract a series of tasks from complex task instructions expressed in natural language.
CATaLog Online: A Web-based CAT Tool for Distributed Translation with Data Capture for APE and Translation Process Research
We present a free web-based CAT tool called CATaLog Online which provides a novel and user-friendly online CAT environment for post-editors/translators.
CATaLog Online: Porting a Post-editing Tool to the Web
The tool features a number of editing and log functions similar to the desktop version of CATaLog enhanced with several new features that we describe in detail in this paper.
Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey
Recently, with the advances made in continuous representation of words (word embeddings) and deep neural architectures, many research works are published in the area of relation extraction and it is very difficult to keep track of so many papers.
RTE: A Tool for Annotating Relation Triplets from Text
In this work, we present a Web-based annotation tool `Relation Triplets Extractor' \footnote{https://abera87. github. io/annotate/} (RTE) for annotating relation triplets from the text.
Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering
Distantly supervised models are very popular for relation extraction since we can obtain a large amount of training data using the distant supervision method without human annotation.
Investigating the Generative Approach for Question Answering in E-Commerce
In this paper, we investigate the suitability of the generative approach for PQA.
Exploring Generative Models for Joint Attribute Value Extraction from Product Titles
Attribute values of the products are an essential component in any e-commerce platform.
Using Sentence-level Classification Helps Entity Extraction from Material Science Literature
In the last few years, several attempts have been made on extracting information from material science research domain.
A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions
Predicting difficulty of questions is crucial for technical interviews.
Unsupervised Generation of Long-form Technical Questions from Textbook Metadata using Structured Templates
Semi-structured metadata of a textbook — the table of contents and the index — provide rich cues for technical question generation.
90% F1 Score in Relational Triple Extraction: Is it Real ?
This approach leads to overall performance improvement in these models within the realistic experimental setting.
Ranked #11 on
Relation Extraction
on NYT
Do the Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks?
Joint models, which capture interactions across triples, are the more recent development, and have been shown to outperform pipeline models for sentence-level extraction tasks.
Adapting Pre-trained Generative Models for Extractive Question Answering
Pre-trained Generative models such as BART, T5, etc.
Extractive Question-Answering
Long Form Question Answering
+1
MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction
Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entities, such as conductivity, voltage, etc.
