Information is an important concept that is studied extensively across a range of disciplines, from the physical sciences to genetics to psychology to epistemology. Information continues to increase in importance, and the present age has been referred to as the "Information Age."

One may understand information in a variety of ways. For some, information is found in facts that were previously unknown. For others, a fact must have some economic value to be considered information. Other people emphasize the movement through a communication channel from one location to another when describing information. In all of these instances, information is the set of characteristics of the output of a process. Yet Information has seldom been studied in a consistent way across different disciplines.

"Information from Processes" provides a discipline-independent and precise presentation of both information and computing processes. Information concepts and phenomena are examined in an effort to understand them, given a hierarchy of information processes, where one process uses others. Research about processes and computing is applied to answer the question of what information can and cannot be produced, and to determine the nature of this information (theoretical information science). The book also presents some of the basic processes that are used in specific domains (applied information science), such as those that generate information in areas like reasoning, the evolution of informative systems, cryptography, knowledge, natural language, and the economic value of information.Written for researchers and graduate students in information science and related fields, "Information from Processes "details a unique information model independent from other concepts in computer or archival science, which is thus applicable to a wide range of domains. Combining theoretical and empirical methods as well as psychological, mathematical, philosophical, and economic techniques, Losee's book delivers a solid basis and starting point for future discussions and research about the creation and use of information."

Text Retrieval and Filtering: Analytical Models of Performance is the first book that addresses the problem of analytically computing the performance of retrieval and filtering systems. The book describes means by which retrieval may be studied analytically, allowing one to describe current performance, predict future performance, and to understand why systems perform as they do. The focus is on retrieving and filtering natural language text, with material addressing retrieval performance for the simple case of queries with a single term, the more complex case with multiple terms, both with term independence and term dependence, and for the use of grammatical information to improve performance. Unambiguous statements of the conditions under which one method or system will be more effective than another are developed. Text Retrieval and Filtering: Analytical Models of Performance focuses on the performance of systems that retrieve natural language text, considering full sentences as well as phrases and individual words. The last chapter explicitly addresses how grammatical constructs and methods may be studied in the context of retrieval or filtering system performance. The book builds toward solving this problem, although the material in earlier chapters is as useful to those addressing non-linguistic, statistical concerns as it is to linguists. Those interested in grammatical information should be cautioned to carefully examine earlier chapters, especially Chapters 7 and 8, which discuss purely statistical relationships between terms, before moving on to Chapter 10, which explicitly addresses linguistic issues. Text Retrieval and Filtering: Analytical Models of Performance is suitable as a secondary text for a graduate level course on Information Retrieval or Linguistics, and as a reference for researchers and practitioners in industry.

Information is an important concept that is studied extensively across a range of disciplines, from the physical sciences to genetics to psychology to epistemology. Information continues to increase in importance, and the present age has been referred to as the "Information Age." One may understand information in a variety of ways. For some, information is found in facts that were previously unknown. For others, a fact must have some economic value to be considered information. Other people emphasize the movement through a communication channel from one location to another when describing information. In all of these instances, information is the set of characteristics of the output of a process. Yet Information has seldom been studied in a consistent way across different disciplines. Information from Processes provides a discipline-independent and precise presentation of both information and computing processes. Information concepts and phenomena are examined in an effort to understand them, given a hierarchy of information processes, where one process uses others. Research about processes and computing is applied to answer the question of what information can and cannot be produced, and to determine the nature of this information (theoretical information science). The book also presents some of the basic processes that are used in specific domains (applied information science), such as those that generate information in areas like reasoning, the evolution of informative systems, cryptography, knowledge, natural language, and the economic value of information. Written for researchers and graduate students in information science and related fields, Information from Processes details a unique information model independent from other concepts in computer or archival science, which is thus applicable to a wide range of domains. Combining theoretical and empirical methods as well as psychological, mathematical, philosophical, and economic techniques, Losee's book delivers a solid basis and starting point for future discussions and research about the creation and use of information.

Text Retrieval and Filtering: Analytical Models of Performance is the first book that addresses the problem of analytically computing the performance of retrieval and filtering systems. The book describes means by which retrieval may be studied analytically, allowing one to describe current performance, predict future performance, and to understand why systems perform as they do. The focus is on retrieving and filtering natural language text, with material addressing retrieval performance for the simple case of queries with a single term, the more complex case with multiple terms, both with term independence and term dependence, and for the use of grammatical information to improve performance. Unambiguous statements of the conditions under which one method or system will be more effective than another are developed. Text Retrieval and Filtering: Analytical Models of Performance focuses on the performance of systems that retrieve natural language text, considering full sentences as well as phrases and individual words. The last chapter explicitly addresses how grammatical constructs and methods may be studied in the context of retrieval or filtering system performance. The book builds toward solving this problem, although the material in earlier chapters is as useful to those addressing non-linguistic, statistical concerns as it is to linguists. Those interested in grammatical information should be cautioned to carefully examine earlier chapters, especially Chapters 7 and 8, which discuss purely statistical relationships between terms, before moving on to Chapter 10, which explicitly addresses linguistic issues. Text Retrieval and Filtering: Analytical Models of Performance is suitable as a secondary text for a graduate level course on Information Retrieval or Linguistics, and as a reference for researchers and practitioners in industry.

Information Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process. However, in the sciences, phenomena may be predicted, given parameter values of the system. After developing a measure that can be applied retrospectively or can be predicted, performance of a system using a single term can be predicted given several different types of probabilistic distributions. Information Retrieval performance can be predicted with multiple terms, where statistical dependence between terms exists and is understood. These predictive models may be applied to realistic problems, and then the results may be used to validate the accuracy of the methods used. The application of metadata or index labels can be used to determine whether or not these features should be used in particular cases. Linguistic information, such as part-of-speech tag information, can increase the discrimination value of existing terminology and can be studied predictively. This work provides methods for measuring performance that may be used predictively. Means of predicting these performance measures are provided, both for the simple case of a single term in the query and for multiple terms. Methods of applying these formulae are also suggested.