Foreground and background text in retrieval

Our hypothesis is that certain clauses have foreground functions in text, while other clauses have background functions and that these functions are expressed or reflected in the syntactic structure of the clause. Presumably these clauses will have differing utility for automatic approaches to text understanding; a summarization system might want to utilize background clauses to capture commonalities between numbers of documents while an indexing system might use foreground clauses in order to capture specific characteristics of a certain document

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

What Users Ask a Search Engine: Analyzing One Billion Russian Question Queries

We analyze the question queries submitted to a large commercial web search engine to get insights about what people ask, and to better tailor the search results to the users’ needs. Based on a dataset of about one billion question queries submitted during the year 2012, we investigate askers’ querying behavior with the support of automatic query categorization. While the importance of question queries is likely to increase, at present they only make up 3–4% of the total search traffic. Since questions are such a small part of the query stream and are more likely to be unique than shorter queries, clickthrough information is typically rather sparse. Thus, query categorization methods based on the categories of clicked web documents do not work well for questions. As an alternative, we propose a robust question query classification method that uses the labeled questions from a large community question answering platform (CQA) as a training set. The resulting classifier is then transferred to the web search questions. Even though questions on CQA platforms tend to be different to web search questions, our categorization method proves competitive with strong baselines with respect to classification accuracy. To show the scalability of our proposed method we apply the classifiers to about one billion question queries and discuss the trade-offs between performance and accuracy that different classification models offer. Our findings reveal what people ask a search engine and also how this contrasts behavior on a CQA platform

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system