Named Entity Sequence Classification
This addresses the need for reliable confidence estimates in NER for applications like content recommendations, but it is incremental as it builds on existing NER and neural network methods.
The paper tackles the problem of assigning reliable confidence levels to detected named entities, framing it as Named Entity Sequence Classification (NESC) using NER and recurrent neural networks for binary classification. The result is an approach applied to Tweet texts that identifies named entities with high confidence levels.
Named Entity Recognition (NER) aims at locating and classifying named entities in text. In some use cases of NER, including cases where detected named entities are used in creating content recommendations, it is crucial to have a reliable confidence level for the detected named entities. In this work we study the problem of finding confidence levels for detected named entities. We refer to this problem as Named Entity Sequence Classification (NESC). We frame NESC as a binary classification problem and we use NER as well as recurrent neural networks to find the probability of candidate named entity is a real named entity. We apply this approach to Tweet texts and we show how we could find named entities with high confidence levels from Tweets.