NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents
This paper presents our relation extraction system for subtask C of SemEval-2017 Task 10: ScienceIE. Assuming that the keyphrases are already annotated in the input data, our work explores a wide range of linguistic features, applies various feature selection techniques, optimizes the hyper parameters and class weights and experiments with different problem formulations (single classification model vs individual classifiers for each keyphrase type, single-step classifier vs pipeline classifier for hyponym relations). Performance of five popular classification algorithms are evaluated for each problem formulation along with feature selection. The best setting achieved an F1 score of 71.0{\%} for synonym and 30.0{\%} for hyponym relation on the test data.
Code (0)
등록된 구현이 없습니다.
Tasks
feature selectionGeneral ClassificationKnowledge Base CompletionRelationRelation ExtractionSimilar Papers 제목 키워드 기반
NTNU-1@ScienceIE at SemEval-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random Fields
We present NTNU{'}s systems for Task A (prediction of keyphrases) and Task B (labelling as Material, Process or Task) at SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications (Augenstein …
Dependency ParsingNamed Entity Recognition (NER)Relation ExtractionMIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks
Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge. In particular, one may infer from them relations between scientific concepts, such as synonyms and hyponyms. Artif…
ArticlesRelationRelation ExtractionFlytxt\_NTNU at SemEval-2018 Task 8: Identifying and Classifying Malware Text Using Conditional Random Fields and Na\"\ive Bayes Classifiers
Cybersecurity risks such as malware threaten the personal safety of users, but to identify malware text is a major challenge. The paper proposes a supervised learning approach to identifying malware sentences given a doc…
NTNUSentEval at SemEval-2016 Task 4: Combining General Classifiers for Fast Twitter Sentiment Analysis
The NTNU System at SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random Fields
This study describes the design of the NTNU system for the ScienceIE task at the SemEval 2017 workshop. We use self-defined feature templates and multiple conditional random fields with extracted features to identify key…
Multi-Task Learning