Search Results for author: Chen-Tse Tsai

Found 11 papers, 2 papers with code

Identifying Named Entities as they are Typed

no code implementations EACL 2021 Ravneet Arora, Chen-Tse Tsai, Daniel Preotiuc-Pietro

However, the typical experimental setup for evaluating Named Entity Recognition (NER) systems is not directly applicable to systems that process text in real time as the text is being typed.

Named Entity Recognition NER

Named Entity Recognition with Partially Annotated Training Data

no code implementations CONLL 2019 Stephen Mayhew, Snigdha Chaturvedi, Chen-Tse Tsai, Dan Roth

Supervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated.

Named Entity Recognition NER

Zero-Shot Open Entity Typing as Type-Compatible Grounding

1 code implementation EMNLP 2018 Ben Zhou, Daniel Khashabi, Chen-Tse Tsai, Dan Roth

We evaluate our system on a broad range of datasets, including standard fine-grained and coarse-grained entity typing datasets, and also a dataset in the biological domain.

Entity Typing NER

STCP: Simplified-Traditional Chinese Conversion and Proofreading

no code implementations IJCNLP 2017 Jiarui Xu, Xuezhe Ma, Chen-Tse Tsai, Eduard Hovy

This paper aims to provide an effective tool for conversion between Simplified Chinese and Traditional Chinese.

Cheap Translation for Cross-Lingual Named Entity Recognition

no code implementations EMNLP 2017 Stephen Mayhew, Chen-Tse Tsai, Dan Roth

Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e. g., the availability of Wikipedia in the target language.

Cross-Lingual NER Named Entity Recognition +2

Illinois Cross-Lingual Wikifier: Grounding Entities in Many Languages to the English Wikipedia

no code implementations COLING 2016 Chen-Tse Tsai, Dan Roth

The cross-lingual NER model is a language-independent model which can extract named entity mentions in the text of any language in Wikipedia.

Cross-Lingual NER Entity Linking +2

Concept Grounding to Multiple Knowledge Bases via Indirect Supervision

no code implementations TACL 2016 Chen-Tse Tsai, Dan Roth

We also show that considering multiple knowledge bases together has an advantage over grounding concepts to each knowledge base individually.

Entity Linking

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