Previous research uses negative word counts to measure the tone of a text. We show that word lists developed for other disciplines misclassify common words in financial text. In a large sample of 10-Ks during 1994 to 2008, almost three-fourths of the words identified as negative by the widely used Harvard Dictionary are words typically not considered negative in financial contexts. We develop an alternative negative word list, along with five other word lists, that better reflect tone in financial text. We link the word lists to 10-K filing returns, trading volume, return volatility, fraud, material weakness, and unexpected earnings.

/pdf/when-is-a-liability-not-a-liability-textual-analysis-j5sx1r0swv.pdf

When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks

This tutorial is concerned with a comprehensive introduction to the research area of learning to rank for information retrieval. In the first part of the tutorial, we will introduce three major approaches to learning to rank, i.e., the pointwise, pairwise, and listwise approaches, analyze the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures, evaluate the performance of these approaches on the LETOR benchmark datasets, and demonstrate how to use these approaches to solve real ranking applications. In the second part of the tutorial, we will discuss some advanced topics regarding learning to rank, such as relational ranking, diverse ranking, semi-supervised ranking, transfer ranking, query-dependent ranking, and training data preprocessing. In the third part, we will briefly mention the recent advances on statistical learning theory for ranking, which explain the generalization ability and statistical consistency of different ranking methods. In the last part, we will conclude the tutorial and show several future research directions.

/pdf/learning-to-rank-for-information-retrieval-2q6t3viyug.pdf

Learning to Rank for Information Retrieval

Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to-rank techniques. The objective of this tutorial is to give an introduction to this research direction. Specifically, the existing learning-to-rank algorithms are reviewed and categorized into three approaches: the pointwise, pairwise, and listwise approaches. The advantages and disadvantages with each approach are analyzed, and the relationships between the loss functions used in these approaches and IR evaluation measures are discussed. Then the empirical evaluations on typical learning-to-rank methods are shown, with the LETOR collection as a benchmark dataset, which seems to suggest that the listwise approach be the most effective one among all the approaches. After that, a statistical ranking theory is introduced, which can describe different learning-to-rank algorithms, and be used to analyze their query-level generalization abilities. At the end of the tutorial, we provide a summary and discuss potential future work on learning to rank.

/pdf/learning-to-rank-for-information-retrieval-vczd4h43nv.pdf

With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data have to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing (MRSE). We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi-keyword semantics, we choose the efficient similarity measure of "coordinate matching," i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use "inner product similarity" to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. To improve search experience of the data search service, we further extend these two schemes to support more search semantics. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world data set further show proposed schemes indeed introduce low overhead on computation and communication.

/pdf/privacy-preserving-multi-keyword-ranked-search-over-2p8jrwx11a.pdf

Privacy-Preserving Multi-Keyword Ranked Search over Encrypted Cloud Data

In this research we investigate the effect of search engine brand on the evaluation of searching performance. Our research is motivated by the large amount of search traffic directed to a handful of Web search engines, even though many have similar interfaces and performance. We conducted a laboratory experiment with 32 participants using a 42 factorial design confounded in four blocks to measure the effect of four search engine brands (Google, MSN, Yahooe, and a locally developed search engine) while controlling for the quality and presentation of search engine results. We found brand indeed played a role in the searching process. Brand effect varied in different domains. Users seemed to place a high degree of trust in major search engine brands; however, they were more engaged in the searching process when using lesser-known search engines. It appears that branding affects overall Web search at four stages: (a) search engine selection, (b) search engine results page evaluation, (c) individual link evaluation, and (d) evaluation of the landing page. We discuss the implications for search engine marketing and the design of empirical studies measuring search engine performance. © 2009 Wiley Periodicals, Inc.

/pdf/brand-and-its-effect-on-user-perception-of-search-engine-2kjtxyhq7g.pdf

Brand and its effect on user perception of search engine performance

For thousands of years people have realized the importance of archiving and finding information. With the advent of computers, it became possible to store large amounts of information; and finding useful information from such collections became a necessity. The field of Information Retrieval (IR) was born in the 1950s out of this necessity. Over the last forty years, the field has matured considerably. Several IR systems are used on an everyday basis by a wide variety of users. This article is a brief overview of the key advances in the field of Information Retrieval, and a description of where the state-of-the-art is at in the field.

/pdf/modern-information-retrieval-a-brief-overview-198d6ufjo8.pdf

Modern Information Retrieval : A Brief Overview

A search query for a search engine may be improved by incorporating alternate terms into the search query that are semantically similar to terms of the search query, taking into account information derived from the search query. An initial set of alternate terms that may be semantically similar to the original terms in the search query is generated. The initial set of alternate terms may be compared to information derived from the original search query. One example of such information is a set of documents retrieved in response to a search performed using the initial search query. One or more of the alternate terms may be added to the original search query based on their relationship to the information derived from the original search query.

Search queries improved based on query semantic information

A stopword detection component detects stopwords (also stop-phrases) in search queries input to keyword-based information retrieval systems. Potential stopwords are initially identified by comparing the terms in the search query to a list of known stopwords. Context data is then retrieved based on the search query and the identified stopwords. In one implementation, the context data includes documents retrieved from a document index. In another implementation, the context data includes categories relevant to the search query. Sets of retrieved context data are compared to one another to determine if they are substantially similar. If the sets of context data are substantially similar, this fact may be used to infer that the removal of the potential stopword(s) is not material to the search. If the sets of context data are not substantially similar, the potential stopword can be considered material to the search and should not be removed from the query.

Locating meaningful stopwords or stop-phrases in keyword-based retrieval systems

These are exciting times for Information Retrieval. Web search engines have brought IR to the masses. It now affects the lives of hundreds of millions of people, and growing, as Internet search companies launch ever more products based on techniques developed in These are exciting times for Information Retrieval. Web search engines have brought IR to the masses. It now affects the lives of hundreds of millions of people, and growing, as Internet search companies launch ever more products based on techniques developed in IR research.The real world poses unique challenges for search algorithms. They operate at unprecedented scales, and over a wide diversity of information. In addition, we have entered an unprecedented world of "Adversarial Information Retrieval". The lure of billions of dollars of commerce, guided by search engines, motivates all kinds of people to try all kinds of tricks to get their sites to the top of the search results.What techniques do people use to defeat IR algorithms? What are the evaluation challenges for a web search engine? How much impact has IR had on search engines? How does Google serve over 250 Million queries a day, often with sub-second response times? This talk will show that the world of algorithm and system design for commercial search engines can be described by two of Murphy's Laws: a) If anything can go wrong, it will, and b) If anything cannot go wrong, it will anyway.

Challenges in running a commercial search engine

Methods, systems, and computer program products are provided for interleaving search results. A method includes presenting multiple first search results received from a first search engine. The first search results satisfy a search query directed to the first search engine and are presented in an order. A second search result from a second search engine is inserted at a position between two otherwise adjacent first search results. The second search result is received from a second search engine in response to the search query.

Interleaving Search Results

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