A Novel Framework for Tweet segmentation and its Application to Named Entity Recognition

Twitter has become one of the most important communication channels with its ability providing the most up-to-date and newsworthy information. Considering wide use of twitter as the source of information, reaching an interesting tweet for user among a bunch of tweets is challenging. A huge amount of tweets sent per day by hundred millions of users, information overload is inevitable. For extracting information in large volume of tweets, Named Entity Recognition (NER), methods on formal texts. However, many applications in Information Retrieval (IR) and Natural Language Processing (NLP) suffer severely from the noisy and short nature of tweets. In this paper, we propose a novel framework for tweet segmentation in a batch mode, called HybridSeg by splitting tweets into meaningful segments, the semantic or context information is well preserved and easily extracted by the downstream applications. HybridSeg finds the optimal segmentation of a tweet by maximizing the sum of the stickiness scores of its candidate segments. The stickiness score considers the probability of a segment being a phrase in English (i.e., global context) and the probability of a segment being a phrase within the batch of tweets (i.e., local context). For the latter, we propose and evaluate two models to derive local context by considering the linguistic features and term-dependency in a batch of tweets, respectively. HybridSeg is also designed to iteratively learn from confident segments as pseudo feedback. As an application, we show that high accuracy is achieved in named entity recognition by applying segment-based part-of-speech (POS) tagging.