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Found 11 papers, 2 papers with code
Identifying Named Entities as they are Typed
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 with Partially Annotated Training Data
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.
Zero-Shot Open Entity Typing as Type-Compatible Grounding
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.
A Semi-Markov Structured Support Vector Machine Model for High-Precision Named Entity Recognition
Named entity recognition (NER) is the backbone of many NLP solutions.
STCP: Simplified-Traditional Chinese Conversion and Proofreading
This paper aims to provide an effective tool for conversion between Simplified Chinese and Traditional Chinese.
Cheap Translation for Cross-Lingual Named Entity Recognition
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.
Illinois Cross-Lingual Wikifier: Grounding Entities in Many Languages to the English Wikipedia
The cross-lingual NER model is a language-independent model which can extract named entity mentions in the text of any language in Wikipedia.
Concept Grounding to Multiple Knowledge Bases via Indirect Supervision
We also show that considering multiple knowledge bases together has an advantage over grounding concepts to each knowledge base individually.
