Teacher:In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Teacher: Now, understand the problem? Solve this instance: Interactive topic models are powerful tools for those seeking to understand large collections of text. However, existing sampling-based interactive topic modeling approaches scale poorly to large data sets. Anchor methods, which use a single word to uniquely identify a topic, offer the speed needed for interactive work but lack both a mechanism to inject prior knowledge and lack the intuitive semantics needed for user-facing applications. We propose combinations of words as anchors, going beyond existing single word anchor algorithmsan approach we call "Tandem Anchors". We begin with a synthetic investigation of this approach then apply the approach to interactive topic modeling in a user study and compare it to interactive and non-interactive approaches. Tandem anchors are faster and more intuitive than existing interactive approaches. Topic models distill large collections of text into topics, giving a high-level summary of the thematic structure of the data without manual annotation. In addition to facilitating discovery of topical trends (Gardner et al., 2010), topic modeling is used for a wide variety of problems including document classification (Rubin et al., 2012), information retrieval (Wei and Croft, 2006), author identification (Rosen-Zvi et al., 2004), and sentiment analysis (Titov and McDonald, 2008). However, the most compelling use of topic models is to help users understand large datasets (Chuang et al., 2012). Interactive topic modeling (Hu et al., 2014) allows non-experts to refine automatically generated topics, making topic models less of a "take it or leave it" proposition. Including humans input during training improves the quality of the model and allows users to guide topics in a specific way, custom tailoring the model for a specific downstream task or analysis. The downside is that interactive topic modeling is slowalgorithms typically scale with the size of the corpusand requires non-intuitive information from the user in the form of must-link and cannot-link constraints (Andrzejewski et al., 2009). We address these shortcomings of interactive topic modeling by using an interactive version of the anchor words algorithm for topic models. The anchor algorithm (Arora et al., 2013) is an alternative topic modeling algorithm which scales with the number of unique word types in the data rather than the number of documents or tokens (Section 1). This makes the anchor algorithm fast enough for interactive use, even in web-scale document collections. A drawback of the anchor method is that anchor wordswords that have high probability of being in a single topicare not intuitive. We extend the anchor algorithm to use multiple anchor words in tandem (Section 2). Tandem anchors not only improve interactive refinement, but also make the underlying anchor-based method more intuitive. For interactive topic modeling, tandem anchors produce higher quality topics than single word anchors (Section 3). Tandem anchors provide a framework for fast interactive topic modeling: users improve and refine an existing model through multiword anchors (Section 4). Compared to existing methods such as Interactive Topic Models (Hu et al., 2014), our method is much faster.
Student:
Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling