Semi-supervised Bootstrapping approach for Named Entity Recognition

TL;DR

This paper introduces a semi-supervised approach for Named Entity Recognition that leverages small training data and pattern-based bootstrapping to improve entity identification across languages.

Contribution

It presents a novel semi-supervised bootstrapping method that uses pattern scoring to enhance NER performance with limited labeled data.

Findings

Achieved an average F-measure of 75% on English and Tamil datasets.

Effective in handling name variants and ambiguities in entity recognition.

Applicable to multiple languages with minimal supervision.

Abstract

The aim of Named Entity Recognition (NER) is to identify references of named entities in unstructured documents, and to classify them into pre-defined semantic categories. NER often aids from added background knowledge in the form of gazetteers. However using such a collection does not deal with name variants and cannot resolve ambiguities associated in identifying the entities in context and associating them with predefined categories. We present a semi-supervised NER approach that starts with identifying named entities with a small set of training data. Using the identified named entities, the word and the context features are used to define the pattern. This pattern of each named entity category is used as a seed pattern to identify the named entities in the test set. Pattern scoring and tuple value score enables the generation of the new patterns to identify the named entity categories. We have evaluated the proposed system for English language with the dataset of tagged (IEER) and untagged (CoNLL 2003) named entity corpus and for Tamil language with the documents from the FIRE corpus and yield an average f-measure of 75% for both the languages.