Contextual text mining

With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the author(s) of a biomedical publication, or implicit, such as the positive or negative sentiment that an author had when she wrote a product review; there may also be complex context such as the social network of the authors. Many applications require analysis of topic patterns over different contexts. For instance, analysis of search logs in the context of the user can reveal how we can improve the quality of a search engine by optimizing the search results according to particular users; analysis of customer reviews in the context of positive and negative sentiments can help the user summarize public opinions about a product; analysis of blogs or scientific publications in the context of a social network can facilitate discovery of more meaningful topical communities. Since context information significantly affects the choices of topics and language made by authors, in general, it is very important to incorporate it into analyzing and mining text data. In general, modeling the context in text, discovering contextual patterns of language units and topics from text, a general task which we refer to as Contextual Text Mining, has widespread applications in text mining. In this thesis, we provide a novel and systematic study of contextual text mining, which is a new paradigm of text mining treating context information as the ``first-class citizen.'' We formally define the problem of contextual text mining and its basic tasks, and propose a general framework for contextual text mining based on generative modeling of text. This conceptual framework provides general guidance on text mining problems with context information and can be instantiated into many real tasks, including the general problem of contextual topic analysis. We formally present a functional framework for contextual topic analysis, with a general contextual topic model and its various versions, which can effectively solve the text mining problems in a lot of real world applications. We further introduce general components of contextual topic analysis, by adding priors to contextual topic models to incorporate prior knowledge, regularizing contextual topic models with dependency structure of context, and postprocessing contextual patterns to extract refined patterns. The refinements on the general contextual topic model naturally lead to a variety of probabilistic models which incorporate different types of context and various assumptions and constraints. These special versions of the contextual topic model are proved effective in a variety of real applications involving topics and explicit contexts, implicit contexts, and complex contexts. We then introduce a postprocessing procedure for contextual patterns, by generating meaningful labels for multinomial context models. This method provides a general way to interpret text mining results for real users. By applying contextual text mining in the ``context'' of other text information management tasks, including ad hoc text retrieval and web search, we further prove the effectiveness of contextual text mining techniques in a quantitative way with large scale datasets. The framework of contextual text mining not only unifies many explorations of text analysis with context information, but also opens up many new possibilities for future research directions in text mining.

Biliteracy effects on phonological awareness, oral language proficiency and reading skills in Taiwanese Mandarin-English bilingual children

The present study examined the effect of learning to read a heritage language on Taiwanese Mandarin-English bilingual children’s Chinese and English phonological awareness, Chinese and English oral language proficiency, and English reading skills. Participants were 40 Taiwanese Mandarin-English bilingual children and 20 English monolingual children in the U.S. Based on their performance on a Chinese character reading test, the bilingual participants were divided into two groups: the Chinese Beginning Reader and Chinese Nonreader groups. A single child categorized as a Chinese Advanced Reader also participated. Children received phonological awareness tasks, produced oral narrative samples from a wordless picture book, and took standardized English reading subtests. The bilingual participants received measures in both English and Chinese, whereas English monolingual children received only English measures. Additional demographic information was collected from a language background survey filled out by parents. Results of two MANOVAs indicated that the Chinese Beginning Reader group outperformed the Chinese Nonreader and English Monolingual groups on some phonological awareness measures and the English nonword reading test. In an oral narrative production task in English, the English Monolingual group produced a greater total number of words (TNW) and more different words (NDW) than the Chinese Nonreader group. Multiple regression analyses were conducted to determine whether bilingual children’s Chinese character reading ability would still account for a unique amount of variance in certain outcome variables, independent of nonverbal IQ and other potential demographic or performance variables and to clarify the direction of causality for bilingual children’s performance in the three domains. These results suggested that learning to read in a heritage language directly or indirectly enhances bilingual children’s ability in phonological awareness and certain English reading skills. It also appears that greater oral language proficiency in Chinese promotes early reading in the heritage language. Advanced heritage reading may produce even larger gains. Practical implications of learning a heritage language in the U.S. are discussed.