Hyun Duk Kim

Generating comparative summaries of contradictory opinions in text

This paper presents a study of a novel summarization problem called contrastive opinion summarization (COS). Given two sets of positively and negatively opinionated sentences which are often the output of an existing opinion summarizer, COS aims to extract comparable sentences from each set of opinions and generate a comparative summary containing a set of contrastive sentence pairs. We formally formulate the problem as an optimization problem and propose two general methods for generating a comparative summary using the framework, both of which rely on measuring the content similarity and contrastive similarity of two sentences. We study several strategies to compute these two similarities. We also create a test data set for evaluating such a novel summarization problem. Experiment results on this test set show that the proposed methods are effective for generating comparative summaries of contradictory opinions.

Enriching text representation with frequent pattern mining for probabilistic topic modeling

Probabilistic topic models have been proven very useful for many text mining tasks. Although many variants of topic models have been proposed, most existing works are based on the bag-of-words representation of text in which word combination and order are generally ignored, resulting in inaccurate semantic representation of text. In this paper, we propose a general way to go beyond the bag-of-words representation for topic modeling by applying frequent pattern mining to discover frequent word patterns that can capture semantic associations between words and then using them as additional supplementary semantic units to augment the conventional bag-of-words representation. By viewing a topic model as a generative model for such augmented text data, we can go beyond the bag-of-words assumption to potentially capture more semantic associations between words. Since efficient algorithms for mining frequent word patterns are available, this general strategy for improving topic models can be applied to improve any topic models without substantially increasing the computational complexity of the model. Experiment results show that such a frequent pattern-based data enrichment approach can improve over two representative existing probabilistic topic models for the classification task. We also studied variations of frequent pattern usage in topic modeling and found that using compressed and closed patterns performs best.

Mining causal topics in text data: iterative topic modeling with time series feedback

Many applications require analyzing textual topics in conjunction with external time series variables such as stock prices. We develop a novel general text mining framework for discovering such causal topics from text. Our framework naturally combines any given probabilistic topic model with time-series causal analysis to discover topics that are both coherent semantically and correlated with time series data. We iteratively refine topics, increasing the correlation of discovered topics with the time series. Time series data provides feedback at each iteration by imposing prior distributions on parameters. Experimental results show that the proposed framework is effective.

Compact explanatory opinion summarization

In this paper, we propose a novel opinion summarization problem called compact explanatory opinion summarization (CEOS) which aims to extract within-sentence explanatory text segments from input opinionated texts to help users better understand the detailed reasons of sentiments. We propose and study general methods for identifying candidate boundaries and scoring the explanatoriness of text segments using Hidden Markov Models. We create new data sets and use a new evaluation measure to evaluate CEOS. Experimental results show that the proposed methods are effective for generating an explanatory opinion summary, outperforming a standard text summarization method.

InCaToMi: integrative causal topic miner between textual and non-textual time series data

Topic modeling is popular for text mining tasks. Recently, topic modeling has been combined with time lines when textual data is related to external non-textual time series data such as stock prices. However, no previous work has used the external non-textual time series data in the process of topic modeling. In this paper, we describe a novel text mining system, Integrative Causal Topic Miner (InCaToMi) that integrates textual and non-textual time series data. InCaToMi automatically finds causal relationships and topics using text data and external non-textual time series data using Granger Testing. Moreover, InCaToMi considers the non-textual time series data in the topic modeling process, using the time series data to iteratively improve modeling results through interactions between it and the textual data at both topic and word levels.

Aggregation of multiple judgments for evaluating ordered lists

Many tasks (e.g., search and summarization) result in an ordered list of items. In order to evaluate such an ordered list of items, we need to compare it with an ideal ordered list created by a human expert for the same set of items. To reduce any bias, multiple human experts are often used to create multiple ideal ordered lists. An interesting challenge in such an evaluation method is thus how to aggregate these different ideal lists to compute a single score for an ordered list to be evaluated. In this paper, we propose three new methods for aggregating multiple order judgments to evaluate ordered lists: weighted correlation aggregation, rank-based aggregation, and frequent sequential pattern-based aggregation. Experiment results on ordering sentences for text summarization show that all the three new methods outperform the state of the art average correlation methods in terms of discriminativeness and robustness against noise. Among the three proposed methods, the frequent sequential pattern-based method performs the best due to the flexible modeling of agreements and disagreements among human experts at various levels of granularity.

Information Retrieval with Time Series Query

We study a novel information retrieval problem, where the query is a time series for a given time period, and the retrieval task is to find relevant documents in a text collection of the same time period, which contain topics that are correlated with the query time series. This retrieval problem arises in many text mining applications where there is a need to analyze text data in order to discover potentially causal topics. To solve this problem, we propose and study multiple retrieval algorithms that use the general idea of ranking text documents based on how well their terms are correlated with the query time series. Experiment results show that the proposed retrieval algorithm can effectively help users find documents that are relevant to the time series queries, which can help users analyze the variation patterns of the time series.

Dietary Responses to a Hypertension Diagnosis: Evidence from the National Health and Nutrition Examination Survey (NHANES) 2007–2010

Dietary modification has been shown to substantially reduce blood pressure among people with hypertension. This article uses data from the 2007–2008 and 2009–2010 cross-sections of the National Health and Nutrition Examination Survey (NHANES) to examine the extent to which a hypertension diagnosis can influence individuals’ dietary choices. Several models were estimated to clarify the association of a hypertension diagnosis with dietary factors related to hypertension management. A comparison group of individuals at risk for developing hypertension was used. Results suggest that individuals who received a recent diagnosis of hypertension are more likely to have lower intakes of some dietary factors important in blood pressure management, including sodium. The results also highlight a discrepancy between added salt use and dietary sodium intake. While more recent hypertensive patients tended to consume lower levels of dietary sodium, patients diagnosed longer ago tended to use less added salt. Given that those diagnosed were more likely to have lower blood pressure profiles and improved diets, especially close to the time of diagnosis, the results of this study underscore the need for a prompt and accurate diagnosis of hypertension.