280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification

We propose a simple, yet effective, approach towards inducing multilingual taxonomies from Wikipedia. Given an English taxonomy, our approach leverages the interlanguage links of Wikipedia followed by character-level classifiers to induce high-precision, high-coverage taxonomies in other languages. Through experiments, we demonstrate that our approach significantly outperforms the state-of-the-art, heuristics-heavy approaches for six languages. As a consequence of our work, we release presumably the largest and the most accurate multilingual taxonomic resource spanning over 280 languages

Limits of Preprocessing

We present a first theoretical analysis of the power of polynomial-time preprocessing for important combinatorial problems from various areas in AI. We consider problems from Constraint Satisfaction, Global Constraints, Satisfiability, Nonmonotonic and Bayesian Reasoning. We show that, subject to a complexity theoretic assumption, none of the considered problems can be reduced by polynomial-time preprocessing to a problem kernel whose size is polynomial in a structural problem parameter of the input, such as induced width or backdoor size. Our results provide a firm theoretical boundary for the performance of polynomial-time preprocessing algorithms for the considered problems.Comment: This is a slightly longer version of a paper that appeared in the proceedings of AAAI 201

Taxonomy Induction using Hypernym Subsequences

We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract hypernym subsequences. Taxonomy induction from extracted subsequences is cast as an instance of the minimumcost flow problem on a carefully designed directed graph. Through experiments, we demonstrate that our approach outperforms stateof- the-art taxonomy induction approaches across four languages. Importantly, we also show that our approach is robust to the presence of noise in the input vocabulary. To the best of our knowledge, no previous approaches have been empirically proven to manifest noise-robustness in the input vocabulary

Guarantees and Limits of Preprocessing in Constraint Satisfaction and Reasoning

We present a first theoretical analysis of the power of polynomial-time preprocessing for important combinatorial problems from various areas in AI. We consider problems from Constraint Satisfaction, Global Constraints, Satisfiability, Nonmonotonic and Bayesian Reasoning under structural restrictions. All these problems involve two tasks: (i) identifying the structure in the input as required by the restriction, and (ii) using the identified structure to solve the reasoning task efficiently. We show that for most of the considered problems, task (i) admits a polynomial-time preprocessing to a problem kernel whose size is polynomial in a structural problem parameter of the input, in contrast to task (ii) which does not admit such a reduction to a problem kernel of polynomial size, subject to a complexity theoretic assumption. As a notable exception we show that the consistency problem for the AtMost-NValue constraint admits a polynomial kernel consisting of a quadratic number of variables and domain values. Our results provide a firm worst-case guarantees and theoretical boundaries for the performance of polynomial-time preprocessing algorithms for the considered problems.Comment: arXiv admin note: substantial text overlap with arXiv:1104.2541, arXiv:1104.556

Improvement and Integration of Counting-Based Search Heuristics in Constraint Programming

Ce mémoire s’intéresse à la programmation par contraintes, un paradigme pour résoudre des problèmes combinatoires. Pour la plupart des problèmes, trouver une solution n’est pas possible si on se limite à des mécanismes d’inférence logique; l’exploration d’un espace des solutions à l’aide d’heuristiques de recherche est nécessaire. Des nombreuses heuristiques existantes, les heuristiques de branchement basées sur le dénombrement seront au centre de ce mémoire. Cette approche repose sur l’utilisation d’algorithmes pour estimer le nombre de solutions des contraintes individuelles d’un problème de satisfaction de contraintes. Notre contribution se résume principalement à l’amélioration de deux algorithmes de dénombrement pour les contraintes alldifferent et spanningTree; ces contraintes peuvent exprimer de nombreux problèmes de satisfaction, et sont par le fait même essentielles à nos heuristiques de branchement. Notre travail fait également l’objet d’une contribution à un solveur de programmation par contraintes open-source. Ainsi, l’ensemble de ce mémoire est motivé par cette considération pratique; nos algorithmes doivent être accessibles et performants. Finalement, nous explorons deux techniques applicables à l’ensemble de nos heuristiques: une technique qui réutilise des calculs précédemment faits dans l’arbre de recherche ainsi qu’une manière d’apprendre de nouvelles heuristiques de branchement pour un problème.=----------ABSTRACT: This thesis concerns constraint programming, a paradigm for solving combinatorial problems. The focus is on the mechanism involved in making hypotheses and exploring the solution space towards satisfying solutions: search heuristics. Of interest to us is a specific family called counting-based search, an approach that uses algorithms to estimate the number of solutions of individual constraints in constraint satisfaction problems to guide search. The improvements of two existing counting algorithms and the integration of counting-based search in a constraint programming solver are the two main contributions of this thesis. The first counting algorithm concerns the alldifferent constraint; the second one, the spanningTree constraint. Both constraints are useful for expressing many constraint satisfaction problems and thus are essential for counting-based search. Practical matters are also central to this work; we integrated counting-based search in an open-source constraint programming solver called Gecode. In doing so, we bring this family of search heuristics to a wider audience; everything in this thesis is built upon this contribution. Lastly, we also look at more general improvements to counting-based search with a method for trading computation time for accuracy, and a method for learning new counting-based search heuristics from past experiments