Boosting Entity Mention Detection for Targetted Twitter Streams with Global Contextual Embeddings

TL;DR

This paper introduces EMD Globalizer, a framework that enhances entity mention detection in Twitter streams by leveraging global context embeddings, significantly improving detection accuracy over existing methods.

Contribution

The paper proposes a novel global embedding approach that improves entity mention detection in microblog streams by aggregating context across messages, outperforming existing systems.

Findings

Enhances existing EMD systems by an average of 25.61% in accuracy.

Leverages global context embeddings to improve detection of entity mentions.

Adds minimal computational overhead to current methods.

Abstract

Microblogging sites, like Twitter, have emerged as ubiquitous sources of information. Two important tasks related to the automatic extraction and analysis of information in Microblogs are Entity Mention Detection (EMD) and Entity Detection (ED). The state-of-the-art EMD systems aim to model the non-literary nature of microblog text by training upon offline static datasets. They extract a combination of surface-level features -- orthographic, lexical, and semantic -- from individual messages for noisy text modeling and entity extraction. But given the constantly evolving nature of microblog streams, detecting all entity mentions from such varying yet limited context of short messages remains a difficult problem. To this end, we propose a framework named EMD Globalizer, better suited for the execution of EMD learners on microblog streams. It deviates from the processing of isolated microblog messages by existing EMD systems, where learned knowledge from the immediate context of a message is used to suggest entities. After an initial extraction of entity candidates by an EMD system, the proposed framework leverages occurrence mining to find additional candidate mentions that are missed during this first detection. Aggregating the local contextual representations of these mentions, a global embedding is drawn from the collective context of an entity candidate within a stream. The global embeddings are then utilized to separate entities within the candidates from false positives. All mentions of said entities from the stream are produced in the framework's final outputs. Our experiments show that EMD Globalizer can enhance the effectiveness of all existing EMD systems that we tested (on average by 25.61%) with a small additional computational overhead.