This paper presents a corpus study that explores the extent to which captions contribute to recognizing the intended message of an information graphic. It then presents an implemented graphic interpretation system that takes into account a variety of communicative signals, and an evaluation study showing that evidence obtained from shallow processing of the graphic’s caption has a signiﬁcant impact on the system’s success. This work is part of a larger project whose goal is to provide sight-impaired users with effective access to information graphics. ...
This paper introduces B IU T EE1 , an opensource system for recognizing textual entailment. Its main advantages are its ability to utilize various types of knowledge resources, and its extensibility by which new knowledge resources and inference components can be easily integrated.
This paper presents a general-purpose open source package for recognizing Textual Entailment. The system implements a collection of algorithms, providing a conﬁgurable framework to quickly set up a working environment to experiment with the RTE task. Fast prototyping of new solutions is also allowed by the possibility to extend its modular architecture. We present the tool as a useful resource to approach the Textual Entailment problem, as an instrument for didactic purposes, and as an opportunity to create a collaborative environment to promote research in the ﬁeld. ...
Understanding language requires both linguistic knowledge and knowledge about how the world works, also known as common-sense knowledge. We attempt to characterize the kinds of common-sense knowledge most often involved in recognizing textual entailments.
This paper presents an unsupervised opinion analysis method for debate-side classiﬁcation, i.e., recognizing which stance a person is taking in an online debate. In order to handle the complexities of this genre, we mine the web to learn associations that are indicative of opinion stances in debates. We combine this knowledge with discourse information, and formulate the debate side classiﬁcation task as an Integer Linear Programming problem. Our results show that our method is substantially better than challenging baseline methods.
This paper proposes a knowledge representation model and a logic proving setting with axioms on demand successfully used for recognizing textual entailments. It also details a lexical inference system which boosts the performance of the deep semantic oriented approach on the RTE data. The linear combination of two slightly different logical systems with the third lexical inference system achieves 73.75% accuracy on the RTE 2006 data.
We present a portable translator that recognizes and translates phrases on signboards and menus as captured by a builtin camera. This system can be used on PDAs or mobile phones and resolves the difﬁculty of inputting some character sets such as Japanese and Chinese if the user doesn’t know their readings. Through the high speed mobile network, small images of signboards can be quickly sent to the recognition and translation server.
Miscommunication in speech recognition systems is unavoidable, but a detailed characterization of user corrections will enable speech systems to identify when a correction is taking place and to more accurately recognize the content of correction utterances. In this paper we investigate the adaptations of users when they encounter recognition errors in interactions with a voice-in/voice-out spoken language system.
Many real-world texts contain tables. In order to process these texts correctly and extract the information contained within the tables, it is important to identify the presence and structure of tables. In this paper, we present a new approach that learns to recognize tables in free text, including the boundary, rows and columns of tables. When tested on Wall Street Journal news documents, our learning approach outperforms a deterministic table recognition algorithm that identifies tables based on a fixed set of conditions. ...
In this paper, we explore ways of improving an inference rule collection and its application to the task of recognizing textual entailment. For this purpose, we start with an automatically acquired collection and we propose methods to reﬁne it and obtain more rules using a hand-crafted lexical resource. Following this, we derive a dependency-based structure representation from texts, which aims to provide a proper base for the inference rule application.
We present an unsupervised approach to recognizing discourse relations of CON TRAST, EXPLANATION - EVIDENCE , CON DITION and ELABORATION that hold between arbitrary spans of texts. We show that discourse relation classiﬁers trained on examples that are automatically extracted from massive amounts of text can be used to distinguish between some of these relations with accuracies as high as 93%, even when the relations are not explicitly marked by cue phrases.
Detection of discourse structure is crucial in many text-based applications. This paper presents an original framework for describing textual parallelism which allows us to generalize various discourse phenomena and to propose a unique method to recognize them. With this prospect, we discuss several methods in order to identify the most appropriate one for the problem, and evaluate them based on a manually annotated corpus.
After reading this chapter, you should be able to: Describe the fundamental pay programs for recognizing employees' contributions to the organization's success, list the advantages and disadvantages of the pay programs, list the major factors to consider in matching the pay strategy to the organization's strategy,...
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Recognizing Uncertainty in Speech
Heather Pon-Barry and Stuart M. Shieber
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Optimizing Statistical Character Recognition Using Evolutionary Strategies to Recognize Aircraft Tail Numbers
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: 6-sulfo sialyl Lewis X is the common receptor determinant recognized by H5, H6, H7 and H9 influenza viruses of terrestrial poultry
A variety of scientific disciplines have set as their task explaining mental activities, recognizing that in some way these activities depend upon our brain. But, until recently, the opportunities to conduct experiments directly on our brains were limited. As a result, research efforts were split between disciplines such as cognitive psychology, linguistics, and artificial intelligence that investigated behavior, while disciplines such as neuroanatomy, neurophysiology, and genetics experimented on the brains of non-human animals.
This paper reports on the recognition component of an intelligent tutoring system that is designed to help foreign language speakers learn standard English. The system models the grammar of the learner, with this instantiation of the system tailored to signers of American Sign Language (ASL). We discuss the theoretical motivations for the system, various difficulties that have been encountered in the implementation, as well as the methods we have used to overcome these problems. Our method of capturing ungrammaticalities involves using malrules (also called 'error productions'). ...