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Found 22 papers, 1 papers with code
Constructing A Dataset of Support and Attack Relations in Legal Arguments in Court Judgements using Linguistic Rules
Argumentation mining is a growing area of research and has several interesting practical applications of mining legal arguments.
Weakly Supervised Extraction of Tasks from Text
In this paper, we propose a novel problem of automatic extraction of tasks from text.
Weak Supervision using Linguistic Knowledge for Information Extraction
The patterns in our patterns specification language are then matched on the ETF text rather than raw text to extract various entity mentions.
Techniques for Jointly Extracting Entities and Relations: A Survey
Traditionally, relation extraction is carried out after entity extraction in a "pipeline" fashion, so that relation extraction only focuses on determining whether any semantic relation exists between a pair of extracted entity mentions.
Knowledge-based Extraction of Cause-Effect Relations from Biomedical Text
We propose a knowledge-based approach for extraction of Cause-Effect (CE) relations from biomedical text.
Extracting N-ary Cross-sentence Relations using Constrained Subsequence Kernel
Most of the past work in relation extraction deals with relations occurring within a sentence and having only two entity arguments.
Extraction of Message Sequence Charts from Narrative History Text
In this paper, we advocate the use of Message Sequence Chart (MSC) as a knowledge representation to capture and visualize multi-actor interactions and their temporal ordering.
Extraction of Message Sequence Charts from Software Use-Case Descriptions
We apply this tool to extract MSCs from several real-life software use-case descriptions and show that it performs better than the existing techniques.
Identification of Alias Links among Participants in Narratives
Identification of distinct and independent participants (entities of interest) in a narrative is an important task for many NLP applications.
Multi-Task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets
Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care.
Co-training for Extraction of Adverse Drug Reaction Mentions from Tweets
Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care.
Relation Extraction : A Survey
In this paper, we survey several important supervised, semi-supervised and unsupervised RE techniques.
End-to-End Relation Extraction using Markov Logic Networks
Our end-to-end relation extraction performance is better than 2 out of 3 previous results reported on the ACE 2004 dataset.
Topics and Label Propagation: Best of Both Worlds for Weakly Supervised Text Classification
We propose a Label Propagation based algorithm for weakly supervised text classification.
Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals
All techniques are illustrated using a real-life dataset of supervisor assessment and peer feedback text produced during the PA of 4528 employees in a large multi-national IT company.
Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction
Current methods in ADR mention extraction relies on supervised learning methods, which suffers from labeled data scarcity problem.
Measuring Topic Coherence through Optimal Word Buckets
Measuring topic quality is essential for scoring the learned topics and their subsequent use in Information Retrieval and Text classification.
End-to-end Relation Extraction using Neural Networks and Markov Logic Networks
End-to-end relation extraction refers to identifying boundaries of entity mentions, entity types of these mentions and appropriate semantic relation for each pair of mentions.
