Transfer Learning for Sequence Labeling Using Source Model and Target Data

no code implementations • 14 Feb 2019 • Lingzhen Chen, Alessandro Moschitti

In this paper, we propose an approach for transferring the knowledge of a neural model for sequence labeling, learned from the source domain, to a new model trained on a target domain, where new label categories appear.

named-entity-recognition Named Entity Recognition +2

Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models

no code implementations • COLING 2018 • Lingzhen Chen, Aless Moschitti, ro

In this paper, we propose to use a sequence to sequence model for Named Entity Recognition (NER) and we explore the effectiveness of such model in a progressive NER setting {--} a Transfer Learning (TL) setting.

Feature Engineering named-entity-recognition +3

CIC-FBK Approach to Native Language Identification

no code implementations • WS 2017 • Ilia Markov, Lingzhen Chen, Carlo Strapparava, Grigori Sidorov

We present the CIC-FBK system, which took part in the Native Language Identification (NLI) Shared Task 2017.

General Classification LEMMA +1

Improving Native Language Identification by Using Spelling Errors

no code implementations • ACL 2017 • Lingzhen Chen, Carlo Strapparava, Vivi Nastase

We note that character n-grams from misspelled words are very indicative of the native language of the author.

Native Language Identification