Luchen Tan

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Given a topic with title, narrative and description, we start by building a language model for the topic. The top 1000 tweets were retrieved from Twitter commercial search engine by applying the title of the topic as a query. We exploit pseudo relevance feedback technologies to estimate probability distributions of each term in the topic, then comparing(More)
Compared with carefully edited prose, the language of social media is informal in the extreme. The application of NLP techniques in this context may require a better understanding of word usage within social media. In this paper, we compute a word embedding for a corpus of tweets, comparing it to a word embedding for Wikipedia. After learning a(More)
—Rank similarity measures provide a method for quantifying differences between search engine results without the need for relevance judgments. For example, the providers of a search service might use such measures to estimate the impact of a proposed algorithmic change across a large number of queries — perhaps millions — identifying those queries where the(More)
Given a current news article, we wish to create a succinct query reflecting its content, which may be used to follow the news story over a period of days, or even weeks. In part, the need for succinct queries is occasioned by limitations of commercial social media search engines, which can perform poorly with longer queries. We start by applying established(More)
How do we evaluate systems that filter social media streams and send users updates via push notifications on their mobile phones? Such notifications must be relevant, timely, and novel. In this paper, we explore various evaluation metrics for this task, focusing specifically on measuring relevance. We begin with an analysis of metrics deployed at the TREC(More)
Push notifications from social media provide a method to keep up-to-date on topics of personal interest. To be effective, notifications must achieve a balance between pushing too much and pushing too little. Push too little and the user misses important updates; push too much and the user is overwhelmed by unwanted information. Using data from the TREC 2015(More)
We examine the effects of different latency penalties in the evaluation of push notification systems, as operationalized in the TREC 2015 Microblog track evaluation. The purpose of this study is to inform the design of metrics for the TREC 2016 Real-Time Summarization track, which is largely mod-eled after the TREC 2015 evaluation design.
We present an assessment platform for gathering online relevance judgments for mobile push notifications that will be deployed in the newly-created TREC 2016 Real-Time Summarization (RTS) track. There is emerging interest in building systems that filter social media streams such as tweets to identify interesting and novel content in real time, putatively(More)
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