KnowGraph@IITK at SemEval-2021 Task 11: Building Knowledge Graph for NLP Research
Research in Natural Language Processing is making rapid advances, resulting in the publication of a large number of research papers. Finding relevant research papers and their contribution to the domain is a challenging problem. In this paper, we address this challenge via the SemEval 2021 Task 11: NLPContributionGraph, by developing a system for a research paper contributions-focused knowledge graph over Natural Language Processing literature. The task is divided into three sub-tasks: extracting contribution sentences that show important contributions in the research article, extracting phrases from the contribution sentences, and predicting the information units in the research article together with triplet formation from the phrases. The proposed system is agnostic to the subject domain and can be applied for building a knowledge graph for any area. We found that transformer-based language models can significantly improve existing techniques and utilized the SciBERT-based model. Our first sub-task uses Bidirectional LSTM (BiLSTM) stacked on top of SciBERT model layers, while the second sub-task uses Conditional Random Field (CRF) on top of SciBERT with BiLSTM. The third sub-task uses a combined SciBERT based neural approach with heuristics for information unit prediction and triplet formation from the phrases. Our system achieved F1 score of 0.38, 0.63 and 0.76 in end-to-end pipeline testing, phrase extraction testing and triplet extraction testing respectively.
Code (0)
등록된 구현이 없습니다.
Tasks
TripletMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
KnowGraph@IITK at SemEval-2021 Task 11: Building KnowledgeGraph for NLP Research
Research in Natural Language Processing is making rapid advances, resulting in the publication of a large number of research papers. Finding relevant research papers and their contribution to the domain is a challenging …
TripletKnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data
Graph-based anomaly detection is pivotal in diverse security applications, such as fraud detection in transaction networks and intrusion detection for network traffic. Standard approaches, including Graph Neural Networks…
Anomaly DetectionFraud DetectionIntrusion DetectionLogical ReasoningIITK at SemEval-2020 Task 10: Transformers for Emphasis Selection
This paper describes the system proposed for addressing the research problem posed in Task 10 of SemEval-2020: Emphasis Selection For Written Text in Visual Media. We propose an end-to-end model that takes as input the t…
Humor@IITK at SemEval-2021 Task 7: Large Language Models for Quantifying Humor and Offensiveness
Humor and Offense are highly subjective due to multiple word senses, cultural knowledge, and pragmatic competence. Hence, accurately detecting humorous and offensive texts has several compelling use cases in Recommendati…
Recommendation SystemsIITK@LCP at SemEval 2021 Task 1: Classification for Lexical Complexity Regression Task
This paper describes our contribution to SemEval 2021 Task 1: Lexical Complexity Prediction. In our approach, we leverage the ELECTRA model and attempt to mirror the data annotation scheme. Although the task is a regress…
General ClassificationLexical Complexity PredictionregressionTask 2