Gene

Transition (genetics)

Biology

Exploit

Computer security

Sequence labeling

Economics

Biochemistry

Computer science

Relationship extraction

Semantics (computer science)

Natural language processing

Genetics

Sequence (biology)

Task (project management)

Conditional random field

Management

Artificial intelligence

Information extraction

Chemistry

Programming language

In this paper, we address the keyphrase extraction problem as sequence labeling and propose a model that jointly exploits the complementary strengths of Conditional Random Fields that capture label dependencies through a transition parameter matrix consisting of the transition probabilities from one label to the neighboring label, and Bidirectional Long Short Term Memory networks that capture hidden semantics in text through the long distance dependencies. Our results on three datasets of scholarly documents show that the proposed model substantially outperforms strong baselines and previous approaches for keyphrase extraction.

Bi-LSTM-CRF Sequence Labeling for Keyphrase Extraction from Scholarly Documents