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

This paper studies the polynomial-time learnability of the classes of ordered gapped tree patterns (OGT) and ordered gapped forests (OGF) under the into-matching semantics in the query learning model of Angluin. The class OGT is a model of semi-structured database query languages, and a generalization of both the class of ordered/unordered tree pattern languages and the class of non-erasing regular pattern languages. First, we present a polynomial time learning algorithm for µ-OGT, the subclass of OGT without repeated tree variables, using equivalence queries and membership queries. By extending this algorithm, we present polynomial time learning algorithms for the classes µ-OGF of forests without repeated variables and OGT of trees with repeated variables using equivalence queries and subset queries. We also give representation-independent hardness results which indicate that both of equivalence and membership queries are necessary to learn µ-OGT.

Efficient Learning of Semi-structured Data from Queries

We develop new algorithms for learning monadic node selection queries in unranked trees from annotated examples, and apply them to visually interactive Web information extraction. We propose to represent monadic queries by bottom-up deterministic Node Selecting Tree Transducers (NSTTs), a particular class of tree automata that we introduce. We prove that deterministic NSTTs capture the class of queries definable in monadic second order logic (MSO) in trees, which Gottlob and Koch (2002) argue to have the right expressiveness for Web information extraction, and prove that monadic queries defined by NSTTs can be answered efficiently. We present a new polynomial time algorithm in RPNI-style that learns monadic queries defined by deterministic NSTTs from completely annotated examples, where all selected nodes are distinguished. In practice, users prefer to provide partial annotations. We propose to account for partial annotations by intelligent tree pruning heuristics. We introduce pruning NSTT-a formalism that shares many advantages of NSTTs. This leads us to an interactive learning algorithm for monadic queries defined by pruning NSTTs, which satisfies a new formal active learning model in the style of Angluin (1987). We have implemented our interactive learning algorithm integrated it into a visually interactive Web information extraction system-called SQUIRREL-by plugging it into the Mozilla Web browser. Experiments on realistic Web documents confirm excellent quality with very few user interactions during wrapper induction.

Interactive learning of node selecting tree transducer

We propose a Cooperative Question Answering System that takes as input natural language queries and is able to return a cooperative answer based on semantic web resources, more specifically DBpedia represented in OWL/RDF as knowledge base and WordNet to build similar questions. Our system resorts to ontologies not only for reasoning but also to find answers and is independent of prior knowledge of the semantic resources by the user. The natural language question is translated into its semantic representation and then answered by consulting the semantics sources of information. The system is able to clarify the problems of ambiguity and helps finding the path to the correct answer. If there are multiple answers to the question posed (or to the similar questions for which DBpedia contains answers), they will be grouped according to their semantic meaning, providing a more cooperative and clarified answer to the user.

/pdf/work-out-the-semantic-web-search-the-cooperative-way-36er7jeil0.pdf

Work out the semantic web search: the cooperative way

The Web of data with meaning in the sense that a computer program can learn enough about what the data means to process it. During the last decade, the World Wide Web has evolved into the most important public data store on world. An important challenge for computer science today is to develop accurate information extraction and question answering mechanisms for the Web. Berners-Lee points out the difficulty of that task, and that it might even require more adequate formats of Web data representation. Information must be structured, and structure should reflect semantic information, so that machines can learn enough about what the data means. The standard document formats of the Web today, HTML and XML, rely on tree structures that encompass textual information. In this project we want to incorporate novel approaches for modeling tree structure and emerging techniques for machine learning into adaptive information extraction systems for the Web. In the future, we might also have to account for semantic information. The research team is a joint project team with the lifl (CNRS and Lille 1 University) and the Grappa Group (Lille 3 University). The project will be located in Lille 3 University.

Modeling Tree Structures, Machine Learning, and Information Extraction

This paper studies from a machine learning viewpoint the problem of extracting tuples of a target n-ary relation from tree structured data like XML or XHTML documents. Our system can extract, without any post-processing, tuples for all data structures including nested, rotated and cross tables. The wrapper induction algorithm we propose is based on two main ideas. It is incremental: partial tuples are extracted by increasing length. It is based on a representation-enrichment procedure: partial tuples of length i are encoded with the knowledge of extracted tuples of length i - 1. The algorithm is then set in a friendly interactive wrapper induction system for Web documents. We evaluate our system on several information extraction tasks over corporate Web sites. It achieves state-of-the-art results on simple data structures and succeeds on complex data structures where previous approaches fail. Experiments also show that our interactive framework significantly reduces the number of user interactions needed to build a wrapper.

/pdf/interactive-tuples-extraction-from-semi-structured-data-3l15qgxm7x.pdf

Interactive Tuples Extraction from Semi-Structured Data

Résumé. Nous présentons un système pour l’inférence de programmes d ’extraction de relations dans les documents Web. Il utilise les vu s textuelle et structurelle sur les documents. L’extraction des relations est i ncrémentale et utilise des méthodes de composition et d’enrichissement. Nous mont rons que notre système est capable d’extraire des relations pour les organisa tion existantes dans les documents Web (listes, tables, tables tournées, tables croisées).

Extraction de relations dans les documents Web.

Question Answering (QA) systems are complex programs able to answer a question in natural language. Their source of information is a given corpus or, as assumed here, the Web. To achieve their goal, these systems perform various subtasks among which the last one, called answer extraction, is very similar to an Information Extraction task. The main objective of this study it to adapt machine learning techniques defined for Information Extraction tasks to the slightly different task of answer extraction in QA systems. The specificities of QA systems are identified and exploited in this adaptation. Three algorithms, assuming an increasing abstraction of natural language texts, are tested and compared. RÉSUMÉ. Les systèmes Question/Réponse sont des programmes complexes capables de répondre à une question en langage naturel, en utilisant comme source d’information soit un corpus donné, soit, comme c’est le cas ici, le Web. Pour cela, ces systèmes réalisent différentes soustâches parmi lesquelles la dernière, appelée extraction de la réponse, est très similaire à une tâche d’Extraction d’Information. L’objectif de cet article est d’adapter les techniques d’apprentissage automatique utilisées en Extraction d’Information à l’extraction de la réponse. Les spécificités des systèmes Question/Réponse sont identifiées et utilisées dans cette adaptation. Trois algorithmes utilisant une abstraction croissante du texte sont testés et comparés.

Learning to Extract Answers in Question Answering: Experimental Studies.

In the work presented here, we try to automatically extract some product properties from descriptive texts provided by a merchant website. The constitution of an annotated ref- erence corpus reveals some problems, not only due to the texts but also to the specificities of the task. To handle it, two distinct approaches have been tested : an extraction method based on dictionaries and a machine learning approach making use of CRFs (Conditional Random Fields), for which a large number of models have been tried. The results of our experiments outline the advantages and drawbacks of these two methods. MOTS-CLES : descriptions de produits, extraction d'information, apprentissage automatique,

https://www.lattice.cnrs.fr/sites/itellier/articles/Tian_Tellier_Marty.pdf

Extraction de propriétés de produits

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