This paper presents the Excitement Open Platform (EOP), a generic architecture and a comprehensive implementation for textual inference in multiple languages. The platform includes state-of-art algorithms, a large number of knowledge resources, and facilities for experimenting and testing innovative approaches. The EOP is distributed as an open source software.
A key challenge at the core of many Natural Language Processing (NLP) tasks is the ability to determine which conclusions can be inferred from a given natural language text. This problem, called theRecognition of Textual Entailment (RTE), has initiated the development of a range of algorithms, methods, and technologies. Unfortunately, research on Textual Entailment (TE), like semantics research more generally, is fragmented into studies focussing on various aspects of semantics such as world knowledge, lexical and syntactic relations, or more specialized kinds of inference. This fragmentation has problematic practical consequences. Notably, interoperability among the existing RTE systems is poor, and reuse of resources and algorithms is mostly infeasible. This also makes systematic evaluations very difficult to carry out. Finally, textual entailment presents a wide array of approaches to potential end users with little guidance on which to pick. Our contribution to this situation is the novel EXCITEMENT architecture, which was developed to enable and encourage the consolidation of methods and resources in the textual entailment area. It decomposes RTE into components with strongly typed interfaces. We specify (a) a modular linguistic analysis pipeline and (b) a decomposition of the ‘core’ RTE methods into top-level algorithms and subcomponents. We identify four major subcomponent types, including knowledge bases and alignment methods. The architecture was developed with a focus on generality, supporting all major approaches to RTE and encouraging language independence. We illustrate the feasibility of the architecture by constructing mappings of major existing systems onto the architecture. The practical implementation of this architecture forms the EXCITEMENT open platform. It is a suite of textual entailment algorithms and components which contains the three systems named above, including linguistic-analysis pipelines for three languages (English, German, and Italian), and comprises a number of linguistic resources. By addressing the problems outlined above, the platform provides a comprehensive and flexible basis for research and experimentation in textual entailment and is available as open source software under the GNU General Public License.
This paper presents an efficient algorithm for online avoidance of static obstacles that accounts for robot dynamics and actuator constraints. The robot trajectory (path and speed) is generated incrementally by avoiding obstacles optimally one at a time, thus reducing the original problem from one that avoids m obstacles to m simpler problems that avoid one obstacle each. The computational complexity of this planner is therefore linear in the number of obstacles, instead of the typical exponential complexity of traditional geometric planners. This approach is quite general and applicable to any cost function and to any robot dynamics; it is treated here for minimum time motions, a planar point mass robot, and circular obstacles. Numerical experiments demonstrate the algorithm for very cluttered environments (70 obstacles) and narrow passages. Upper and lower bounds on the motion time and on the path length were derived as functions of the Euclidean distance between the end points and the average obstacle size. Comparing a kinematic version of this algorithm to the RRT and RRT* planners showed significant advantages over both planners. The algorithm was demonstrated on an experimental mobile robot moving at high speeds in a known environment.
We investigate recognizing implied predicate-argument relationships which are not explicitly expressed in syntactic structure. While prior works addressed such relationships as an extension to semantic role labeling, our work investigates them in the context of textual inference scenarios. Such scenarios provide prior information, which substantially eases the task. We provide a large and freely available evaluation dataset for our task setting, and propose methods to cope with it, while obtaining promising results in empirical evaluations.
Abstract. We introduce a new semantic annotation scheme for the Recognizing Textual Entailment (RTE) dataset as well as a manually annotated dataset that uses this scheme. The scheme addresses three types of modification that license entailment patterns: restrictive, appositive and conjunctive, with a formal semantic specification of these patterns' contribution for establishing entailment. These inferential constructions were found to occur in 77.68% of the entailments in the RTE 1-3 corpora. They were annotated with cross-annotator agreement of 70.73% on average. A central aim of our annotations is to examine components that address these phenomena in RTE systems. Specifically, the new annotated dataset is used for examining a syntactic rule base within the BIUTEE recognizer, a publicly available entailment system. According to our tests, the rule base is rarely used to process the phenomena annotated in our corpus and most of the recognition work is done by other components in the system. 3
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