SemEval 2017 Task 10
Related Papers
EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION
AbstractThis paper describes our approach to the SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications, specifically to Subtask (B): Classification of identified keyphrases. We explored three different …...
Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator
AbstractThis paper describes our participation in SemEval-2017 Task 10. We competed in Subtask 1 and 2 which consist respectively in identifying all the key phrases in scientific publications and label …...
LABDA at SemEval-2017 Task 10: Extracting Keyphrases from Scientific Publications by combining the BANNER tool and the UMLS Semantic Network
AbstractThis paper describes the system presented by the LABDA group at SemEval 2017 Task 10 ScienceIE, specifically for the subtasks of identification and classification of keyphrases from scientific articles. For …...
MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks
AbstractOver 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. Artificial neural networks have …...
NTNU-1@ScienceIE at SemEval-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random Fields
AbstractWe 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 …...
NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents
AbstractThis 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 …...
PKU_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge
AbstractThis paper presents a system that participated in SemEval 2017 Task 10 (subtask A and subtask B): Extracting Keyphrases and Relations from Scientific Publications (Augenstein et al., 2017). Our proposed …...
TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers
AbstractThis paper describes our TTI-COIN system that participated in SemEval-2017 Task 10. We investigated appropriate embeddings to adapt a neural end-to-end entity and relation extraction system LSTM-ER to this task. …...