Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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Regularization Of Inverse Problems

Cross-language information retrieval (CLIR) is an active sub-domain of information retrieval (IR). Like IR, CLIR is centered on the search for documents and for information contained within those documents. Unlike IR, CLIR must reconcile queries and documents that are written in different languages. The usual solution to this mismatch involves translating the query and/or the documents before performing the search. Translation is therefore a pivotal activity for CLIR engines. Over the last 15 years, the CLIR community has developed a wide range of techniques and models supporting free text translation. This article presents an overview of those techniques, with a special emphasis on recent developments.

Translation techniques in cross-language information retrieval

Evaluation conferences such as TREC, CLEF, and NTCIR are modern examples of the Cranfield evaluation paradigm. In Cranfield, researchers perform experiments on test collections to compare the relative effectiveness of different retrieval approaches. The test collections allow the researchers to control the effects of different system parameters, increasing the power and decreasing the cost of retrieval experiments as compared to user-based evaluations. This paper reviews the fundamental assumptions and appropriate uses of the Cranfield paradigm, especially as they apply in the context of the evaluation conferences.

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The Philosophy of Information Retrieval Evaluation

In this paper, we describe an image collection created for the CLEF cross-language image retrieval track (ImageCLEF). This image retrieval benchmark (referred to as the IAPR TC-12 Benchmark) has developed from an initiative started by the Technical Committee 12 (TC-12) of the International Association of Pattern Recognition (IAPR). The collection consists of 20,000 images from a private photographic image collection. The construction and composition of the IAPR TC-12 Benchmark is described, including its associated text captions which are expressed in multiple languages, making the collection well-suited for evaluating the effectiveness of both textbased and visual retrieval methods. We also discuss the current and expected uses of the collection, including its use to benchmark and compare different image retrieval systems in ImageCLEF 2006.

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The IAPR Benchmark : A New Evaluation Resource for Visual Information Systems

We describe the overall organization of the CLEF 2003 evaluation campaign, with a particular focus on the cross-language ad hoc and domainspecific retrieval tracks. The paper discusses the evaluation approach adopted, describes the tracks and tasks offered and the test collections used, and provides an outline of the guidelines given to the participants. It concludes with an overview of the techniques employed for results calculation and analysis for the monolingual, bilingual and multilingual and GIRT tasks.

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Comparative Evaluation of Multilingual Information Access Systems

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Evaluation of cross-language information retrieval systems

The Cross-Language Evaluation Forum (CLEF) is now in its fourth year of activity. We summarize the main lessons learned during this period, outline the state-of-the-art of the research reported in the CLEF experiments and discuss the contribution that this initiative has made to research and development in the multilingual information access domain. We also make proposals for future directions in system evaluation aimed at meeting emerging needs.

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Cross-Language Evaluation Forum: Objectives, Results, Achievements

Advances in cross-language information retrieval

Information retrieval systems operating on free text face difficulties when word forms used in the query and documents do not match. The usual solution is the use of a “stemming component” that reduces related word forms to a common stem. Extensive studies of such components exist for English, but considerably less is known for other languages. Previously, it has been claimed that stemming is essential for highly declensional languages. We report on our experiments on stemming for German, where an additional issue is the handling of compounds, which are formed by concatenating several words. The major contribution of our work that goes beyond its focus on German lies in the investigation of a complete spectrum of approaches, ranging from language-independent to elaborate linguistic methods. The main findings are that stemming is beneficial even when using a simple approach, and that carefully designed decompounding, the splitting of compound words, remarkably boosts performance. All findings are based on a thorough analysis using a large reliable test collection.

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Martin Braschler

Papers

Comparative Evaluation of Multilingual Information Access Systems

Evaluation of cross-language information retrieval systems

Cross-Language Evaluation Forum: Objectives, Results, Achievements

Advances in cross-language information retrieval

How Effective is Stemming and Decompounding for German Text Retrieval