Re-examining the potential effectiveness of interactive query expansion

Much attention has been paid to the relative effectiveness of interactive query expansion versus automatic query expansion. Although interactive query expansion has the potential to be an effective means of improving a search, in this paper we show that, on average, human searchers are less likely than systems to make good expansion decisions. To enable good expansion decisions, searchers must have adequate instructions on how to use interactive query expansion functionalities. We show that simple instructions on using interactive query expansion do not necessarily help searchers make good expansion decisions and discuss difficulties found in making query expansion decisions

Target Apps Selection: Towards a Unified Search Framework for Mobile Devices

With the recent growth of conversational systems and intelligent assistants such as Apple Siri and Google Assistant, mobile devices are becoming even more pervasive in our lives. As a consequence, users are getting engaged with the mobile apps and frequently search for an information need in their apps. However, users cannot search within their apps through their intelligent assistants. This requires a unified mobile search framework that identifies the target app(s) for the user's query, submits the query to the app(s), and presents the results to the user. In this paper, we take the first step forward towards developing unified mobile search. In more detail, we introduce and study the task of target apps selection, which has various potential real-world applications. To this aim, we analyze attributes of search queries as well as user behaviors, while searching with different mobile apps. The analyses are done based on thousands of queries that we collected through crowdsourcing. We finally study the performance of state-of-the-art retrieval models for this task and propose two simple yet effective neural models that significantly outperform the baselines. Our neural approaches are based on learning high-dimensional representations for mobile apps. Our analyses and experiments suggest specific future directions in this research area.Comment: To appear at SIGIR 201

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

The Influence of Commercial Intent of Search Results on Their Perceived Relevance

We carried out a retrieval effectiveness test on the three major web search engines (i.e., Google, Microsoft and Yahoo). In addition to relevance judgments, we classified the results according to their commercial intent and whether or not they carried any advertising. We found that all search engines provide a large number of results with a commercial intent. Google provides significantly more commercial results than the other search engines do. However, the commercial intent of a result did not influence jurors in their relevance judgments

A Comparative analysis: QA evaluation questions versus real-world queries

This paper presents a comparative analysis of user queries to a web search engine, questions to a Q&A service (answers.com), and questions employed in question answering (QA) evaluations at TREC and CLEF. The analysis shows that user queries to search engines contain mostly content words (i.e. keywords) but lack structure words (i.e. stopwords) and capitalization. Thus, they resemble natural language input after case folding and stopword removal. In contrast, topics for QA evaluation and questions to answers.com mainly consist of fully capitalized and syntactically well-formed questions. Classification experiments using a na¨ıve Bayes classifier show that stopwords play an important role in determining the expected answer type. A classification based on stopwords is considerably more accurate (47.5% accuracy) than a classification based on all query words (40.1% accuracy) or on content words (33.9% accuracy). To simulate user input, questions are preprocessed by case folding and stopword removal. Additional classification experiments aim at reconstructing the syntactic wh-word frame of a question, i.e. the embedding of the interrogative word. Results indicate that this part of questions can be reconstructed with moderate accuracy (25.7%), but for a classification problem with a much larger number of classes compared to classifying queries by expected answer type (2096 classes vs. 130 classes). Furthermore, eliminating stopwords can lead to multiple reconstructed questions with a different or with the opposite meaning (e.g. if negations or temporal restrictions are included). In conclusion, question reconstruction from short user queries can be seen as a new realistic evaluation challenge for QA systems

Evolving Lucene search queries for text classification

We describe a method for generating accurate, compact, human understandable text classifiers. Text datasets are indexed using Apache Lucene and Genetic Programs are used to construct Lucene search queries. Genetic programs acquire fitness by producing queries that are effective binary classifiers for a particular category when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from classification tasks

Towards an automated query modification assistant

Users who need several queries before finding what they need can benefit from an automatic search assistant that provides feedback on their query modification strategies. We present a method to learn from a search log which types of query modifications have and have not been effective in the past. The method analyses query modifications along two dimensions: a traditional term-based dimension and a semantic dimension, for which queries are enriches with linked data entities. Applying the method to the search logs of two search engines, we identify six opportunities for a query modification assistant to improve search: modification strategies that are commonly used, but that often do not lead to satisfactory results.Comment: 1st International Workshop on Usage Analysis and the Web of Data (USEWOD2011) in the 20th International World Wide Web Conference (WWW2011), Hyderabad, India, March 28th, 201