Work

Unsupervised Linguistically-Driven Reliable Dependency Parses Detection and Self-Training for Adaptation to the Biomedical Domain

Year: 2013

Type: conference-paper

Abstract: In this paper, a new self-training method for domain adaptation is illustrated, where the selection of reliable parses is carried out by an unsupervised linguistically-driven algorithm, ULISSE. The method has been tested on biomedical texts with results showing a significant improvement with respect to considered baselines, which demonstrates its ability to capture both reliability of parses and domain-specificity of linguistic constructions.

Source: CNR ExploRA

Authors: Felice Dell’Orletta⋄, Giulia Venturi⋄, Simonetta Montemagni⋄

Institution: National Research Council

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2251936842

FWCI: 0.679

Cites: 22

Cited by: 4

Related to: 10