Catálogo de publicaciones - libros

We present a simple, two-steps supervised strategy for the identification and classification of thematic roles in natural language texts. We employ no external source of information but automatic parse trees of the input sentences. We use a few attribute-value features and tree kernel functions applied to specialized structured features. Different configurations of our thematic role labeling system took part in 2 tasks of the SemEval 2007 evaluation campaign, namely the closed tasks on semantic role labeling for the English and the Arabic languages. In this paper we present and discuss the system configuration that participated in the English semantic role labeling task and present new results obtained after the end of the evaluation campaign.

We present a probabilistic framework for inferring coreference relations among person names in a news collection. The approach does not assume any prior knowledge about persons (e.g. an ontology) mentioned in the collection and requires basic linguistic processing (named entity recognition) and resources (a dictionary of person names). The system parameters have been estimated on a 5K corpus of Italian news documents. Evaluation, over a sample of four days news documents, shows that the error rate of the system (1.4%) is above a baseline (5.4%) for the task. Finally, we discuss alternative approaches for evaluation.

Dependency Tree Semantics (DTS) is a formalism that allows to underspecify quantifier scope ambiguities. This paper provides an introduction of DTS and highlights its linguistic and computational advantages. From a linguistics point of view, DTS is able to represent the so-called Branching Quantifier readings, i.e. those readings in which two or more quantifiers have to be evaluated in parallel. From a computational point of view, DTS features an easy syntax–semantics interface wrt a Dependency Grammar and allows for incremental disambiguations.

In this paper, we address the problem of personalization in question answering (QA). We describe the personalization component of YourQA, our web-based QA system, which creates individual models of users based on their reading level and interests.

First, we explain how user models are dynamically created, saved and updated to filter and re-rank the answers. Then, we focus on how the user’s interests are used in YourQA. Finally, we introduce a methodology for user-centered evaluation of personalized QA. Our results show a significant improvement in the user’s satisfaction when their profiles are used to personalize answers.

In this paper we compare four selection strategies in evolutionary optimization of information retrieval (IR) in a question answering setting. The IR index has been augmented by linguistic features to improve the retrieval performance of potential answer passages using queries generated from natural language questions. We use a genetic algorithm to optimize the selection of features and their weights when querying the IR database. With our experiments, we can show that the genetic algorithm applied is robust to strategy changes used for selecting individuals. All experiments yield query settings with improved retrieval performance when applied to unseen data. However, we can observe significant runtime differences when applying the various selection approaches which should be considered when choosing one of these approaches.

Rapid classification of documents is of high-importance in many multilingual settings (such as international institutions or Internet search engines). This has been, for years, a well-known problem, addressed by different techniques, with excellent results. We address this problem by a simple n-grams based technique, a variation of techniques of this family. Our n-grams-based classification is very robust and successful, even for 20-fold classification, and even for short text strings. We give a detailed study for different lengths of strings and size of n-grams and we explore what classification parameters give the best performance. There is no requirement for vocabularies, but only for a few training documents. As a main corpus, we used a EU set of documents in 20 languages. Experimental comparison shows that our approach gives better results than four other popular approaches.

Planning as Satisfiability (SAT) is the best approach for optimally solving classical planning problems. The SAT-based planner has been the winner in the deterministic track for optimal planners in the 4th International Planning Competition (IPC-4) and the co-winner in the last 5th IPC (together with another SAT-based planner). Given a planning problem , works by () generating a SAT formula with a fixed “makespan” , and () checking for satisfiability. The algorithm stops if is satisfiable, and thus a plan has been found, otherwise is increased.

Despite its efficiency, and the optimality of the makespan, has significant deficiency related in particular to “plan quality”, e.g., the number of actions in the returned plan, and the possibility to express and reason on “soft” goals.

In this paper, we present , a system, modification of , which makes a significant step towards the elimination of ’s limitations. Given the optimal makespan, returns plans with minimal number of actions and maximal number of satisfied “soft” goals, with respect to both cardinality and subset inclusions. We selected several benchmarks from different domains from all the IPCs: on these benchmarks we show that the plan quality returned by is often significantly higher than the one returned by .

Quite surprisingly, this is often achieved without sacrificing efficiency while obtaining results that are competitive with the winning system of the ”SimplePreferences” domain in the satisfying track of the last IPC.

The paper discusses a distributed approach for monitoring and diagnosing the execution of a plan where concurrent actions are performed by a team of cooperating agents.

The paper extends the notion of plan diagnosis(introduced by Roos et al. for the execution of a multi-agent plan) with the notion of agent diagnosis. While plan diagnosis is able to capture the distinction between primary and secondary failures, the agent diagnosis makes apparent the actual health status of the agents.

The paper presents a mechanism of failure propagation which captures the interplay between agent diagnosis and plan diagnosis; this mechanism plays a critical role in the understanding at what extent a fault affecting the functionalities of an agent affects the global plan too. A relational formalism is adopted for modeling both the nominal and the abnormal execution of the actions.

Iterative Flattening search is a local search schema introduced for solving scheduling problems with a makespan minimization objective. It is an iterative two-step procedure, where on each cycle of the search a subset of ordering decisions on the critical path in the current solution are randomly retracted and then recomputed to produce a new solution. Since its introduction, other variations have been explored and shown to yield substantial performance improvement over the original formulation. In this spirit, we propose and experimentally evaluate further improvements to this basic local search schema. Specifically, we examine the utility of operating with a more flexible solution representation, and of integrating iterative-flattening search with a complementary tabu search procedure. We evaluate these extensions on large benchmark instances of the Multi-Capacity Job-Shop Scheduling Problem () which have been used in previous studies of iterative flattening search procedures.

This paper presents a computationally effective trajectory generation algorithm for omni-directional mobile robots. This method uses the Voronoi diagram to find a sketchy path that keeps away from obstacles and then smooths this path with a novel use of Bezier curves. This method determines velocity magnitude of a robot along the curved path to meet optimality conditions and dynamic constrains using Newton method. The proposed algorithm has been implemented on real robots, and experimental results in different environments are presented.