Named Entity Sequence Classification

TL;DR

This paper introduces Named Entity Sequence Classification (NESC), a method to assign confidence levels to detected named entities in text, using neural networks to improve reliability in applications like content recommendation.

Contribution

It proposes framing confidence estimation in NER as a binary classification problem using RNNs, specifically applied to Tweets for high-confidence entity detection.

Findings

Effective confidence levels for named entities in Tweets

Use of RNNs improves confidence estimation accuracy

High-confidence entity detection enables better downstream applications

Abstract

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.