SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and
            Relations from Scientific Publications

Augenstein, Isabelle; Das, Mrinal Kanti; Riedel, Sebastian; Vikraman, Lakshmi; McCallum, Andrew

doi:10.18653/v1/s17-2091

Cited by 265 publications

(233 citation statements)

References 26 publications

(23 reference statements)

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“…Further details about the dataset and the description of the keyphrase types can be accessed in (Augenstein et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation

Berend

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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In this paper we introduce our system participating at the 2017 SemEval shared task on keyphrase extraction from scientific documents. We aimed at the creation of a keyphrase extraction approach which relies on as little external resources as possible. Without applying any hand-crafted external resources, and only utilizing a transformed version of word embeddings trained at Wikipedia, our proposed system manages to perform among the best participating systems in terms of precision.

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“…Further details about the dataset and the description of the keyphrase types can be accessed in (Augenstein et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation

Berend

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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“…Task C of ScienceIE at SemEval-2017(Augenstein et al, 2017) concerns identifying sentence level 'SYNONYM-OF' (or 'same-as') and 'HYPONYM-OF' ('is-a') relations among three types of keyphrases: PROCESS (PR), TASK (TA) and MATERIAL (MA) in scientific documents. The 'SYNONYM-OF' relation is symmetric, whereas the 'HYPONYM-OF' relation is directed.…”

Section: Problem Descriptionmentioning

confidence: 99%

NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents

Barik

Marsi

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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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 F 1 score of 71.0% for synonym and 30.0% for hyponym relation on the test data.

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“…The valuable contribution of the ScienceIE challenge was to provide an annotated corpus for train- (Augenstein et al, 2017).…”

Section: Datasetmentioning

confidence: 99%

LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network

Suárez-Paniagua

Segura-Bedmar

Martı́nez

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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In this paper, we describe our participation at the subtask of extraction of relationships between two identified keyphrases. This task can be very helpful in improving search engines for scientific articles. Our approach is based on the use of a convolutional neural network (CNN) trained on the training dataset. This deep learning model has already achieved successful results for the extraction relationships between named entities. Thus, our hypothesis is that this model can be also applied to extract relations between keyphrases. The official results of the task show that our architecture obtained an F1-score of 0.38% for Keyphrases Relation Classification. This performance is lower than the expected due to the generic preprocessing phase and the basic configuration of the CNN model, more complex architectures are proposed as future work to increase the classification rate.

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SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

Cited by 265 publications

References 26 publications

SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation

SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation

NTNU-2 at SemEval-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents

LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network

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