nips nips2011 nips2011-129 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: David Newman, Edwin V. Bonilla, Wray Buntine
Abstract: Topic models have the potential to improve search and browsing by extracting useful semantic themes from web pages and other text documents. When learned topics are coherent and interpretable, they can be valuable for faceted browsing, results set diversity analysis, and document retrieval. However, when dealing with small collections or noisy text (e.g. web search result snippets or blog posts), learned topics can be less coherent, less interpretable, and less useful. To overcome this, we propose two methods to regularize the learning of topic models. Our regularizers work by creating a structured prior over words that reflect broad patterns in the external data. Using thirteen datasets we show that both regularizers improve topic coherence and interpretability while learning a faithful representation of the collection of interest. Overall, this work makes topic models more useful across a broader range of text data. 1
Reference: text
sentIndex sentText sentNum sentScore
1 au Abstract Topic models have the potential to improve search and browsing by extracting useful semantic themes from web pages and other text documents. [sent-7, score-0.796]
2 When learned topics are coherent and interpretable, they can be valuable for faceted browsing, results set diversity analysis, and document retrieval. [sent-8, score-0.908]
3 However, when dealing with small collections or noisy text (e. [sent-9, score-0.393]
4 web search result snippets or blog posts), learned topics can be less coherent, less interpretable, and less useful. [sent-11, score-0.845]
5 To overcome this, we propose two methods to regularize the learning of topic models. [sent-12, score-0.634]
6 Our regularizers work by creating a structured prior over words that reflect broad patterns in the external data. [sent-13, score-0.329]
7 Using thirteen datasets we show that both regularizers improve topic coherence and interpretability while learning a faithful representation of the collection of interest. [sent-14, score-1.358]
8 Overall, this work makes topic models more useful across a broader range of text data. [sent-15, score-0.756]
9 1 Introduction Topic modeling holds much promise for improving the ways users search, discover, and organize online content by automatically extracting semantic themes from collections of text documents. [sent-16, score-0.754]
10 Learned topics can be useful in user interfaces for ad-hoc document retrieval [18]; driving faceted browsing [14]; clustering search results [19]; or improving display of search results by increasing result diversity [10]. [sent-17, score-1.215]
11 When the text being modeled is plentiful, clear and well written (e. [sent-18, score-0.18]
12 large collections of abstracts from scientific literature), learned topics are usually coherent, easily understood, and fit for use in user interfaces. [sent-20, score-0.64]
13 However, topics are not always consistently coherent, and even with relatively well written text, one can learn topics that are a mix of concepts or hard to understand [1, 6]. [sent-21, score-0.775]
14 This problem is exacerbated for content that is sparse or noisy, such as blog posts, tweets, or web search result snippets. [sent-22, score-0.436]
15 Take for instance the task of learning categories in clustering search engine results. [sent-23, score-0.169]
16 A few searches with Carrot2, Yippee, or WebClust quickly demonstrate that consistently learning meaningful topic facets is a difficult task [5]. [sent-24, score-0.642]
17 Our goal in this paper is to improve the coherence, interpretability and ultimate usability of learned topics. [sent-25, score-0.348]
18 To achieve this we propose Q UAD -R EG and C ONV-R EG, two new methods for regularizing topic models, which produce more coherent and interpretable topics. [sent-26, score-0.918]
19 Our work is predicated on recent evidence that a pointwise mutual information-based score (PMI-Score) is highly correlated with human-judged topic coherence [15, 16]. [sent-27, score-1.136]
20 We develop two Bayesian regularization formulations that are designed to improve PMI-Score. [sent-28, score-0.094]
21 We experiment with five search result datasets from 7M Blog posts, four search result datasets from 1M News articles, and four datasets of Google search results. [sent-29, score-0.462]
22 Using these thirteen datasets, our experiments demonstrate that both regularizers consistently improve topic coherence and interpretability, as measured separately by PMI-Score and human judgements. [sent-30, score-1.307]
23 To the best of our knowledge, our models are the first to address the problem of learning topics when dealing with limited and/or noisy text content. [sent-31, score-0.601]
24 This work opens up new application areas for topic modeling. [sent-32, score-0.584]
25 1 2 Topic Coherence and PMI-Score Topics learned from a statistical topic model are formally a multinomial distribution over words, and are often displayed by printing the 10 most probable words in the topic. [sent-33, score-0.874]
26 These top-10 words usually provide sufficient information to determine the subject area and interpretation of a topic, and distinguish one topic from another. [sent-34, score-0.682]
27 However, topics learned on sparse or noisy text data are often less coherent, difficult to interpret, and not particularly useful. [sent-35, score-0.676]
28 Some of these noisy topics can be vaguely interpretable, but contain (in the top-10 words) one or two unrelated words – while other topics can be practically incoherent. [sent-36, score-0.898]
29 In this paper we wish to improve topic models learned on document collections where the text data is sparse and/or noisy. [sent-37, score-1.06]
30 We postulate that using additional (possibly external) data will regularize the learning of the topic models. [sent-38, score-0.658]
31 Topic coherence – meaning semantic coherence – is a human judged quality that depends on the semantics of the words, and cannot be measured by model-based statistical measures that treat the words as exchangeable tokens. [sent-40, score-1.092]
32 Fortunately, recent work has demonstrated that it is possible to automatically measure topic coherence with near-human accuracy [16, 15] using a score based on pointwise mutual information (PMI). [sent-41, score-1.117]
33 In that work they showed (using 6000 human evaluations) that the PMI-Score broadly agrees with human-judged topic coherence. [sent-42, score-0.657]
34 The PMI-Score is motivated by measuring word association between all pairs of words in the top-10 topic words. [sent-43, score-0.705]
35 PMI-Score is defined as follows: 1 PMI-Score(w) = PMI(wi , wj ), ij ∈ {1 . [sent-44, score-0.024]
wordName wordTfidf (topN-words)
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