Knowledge-based Query Expansion in Real-Time Microblog Search

Since the length of microblog texts, such as tweets, is strictly limited to 140 characters, traditional Information Retrieval techniques suffer from the vocabulary mismatch problem severely and cannot yield good performance in the context of microblogosphere. To address this critical challenge, in this paper, we propose a new language modeling approach for microblog retrieval by inferring various types of context information. In particular, we expand the query using knowledge terms derived from Freebase so that the expanded one can better reflect users' search intent. Besides, in order to further satisfy users' real-time information need, we incorporate temporal evidences into the expansion method, which can boost recent tweets in the retrieval results with respect to a given topic. Experimental results on two official TREC Twitter corpora demonstrate the significant superiority of our approach over baseline methods.Comment: 9 pages, 9 figure

spatio temporal contextualization of queries for microtexts in social media mathematical modeling

Abstract In this paper, we present our ongoing project on query contextualization by integrating all possible IoT-based data sources. Most importantly, mobile users are regarded as the IoT sensors which can be the textual data sources with spatio-temporal contexts. Given a large amount of text streams, it has been difficult for the traditional information retrieval systems to conduct the searching tasks. The goal of this work is i ) to understand and process microtexts in social media (e.g., Twitter and Facebook), and ii ) to reformulate the queries for searching for relevant microtexts in these social media

Hashtag and highest scored terms for expanding query

Communicating in short messages, such as using micro blogs, was becoming more popular currently. Twitter https://twitter.com supports micro blogs and retrieval of the blogs by users.To retrieve Twitter documents, we need specific strategies due to its specific characteristics.One new strategy for improving the effectiveness of twitter document retrieval is using the query expansion technique.This paper elaborates query expansion in twitter document retrieval by using the hashtag. We compared the effectiveness of query expansion in four different scenarios: the baseline result using no query expansion, highest scored term in terms of frequency-inverse document frequency (tfidf), maximum hashtag occurance, and combination of the highest scored-term and the maximum hashtag.The results show that the combination of the maximum term in tfidf and the maximum hashtag performs better in retrieving relevant documents than the baseline