SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

In this paper, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, learned and flat analysis. In this paper, we focus on a detailed description of SCREEN's architecture, the flat syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the influence of noisy or incomplete input. The main result of this paper is that flat representations allow more robust processing of spontaneous spoken language than deeply structured representations. In particular, we show how the fault-tolerance and learning capability of connectionist networks can support a flat analysis for providing more robust spoken-language processing within an overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial Intelligence Research 6(1), 199

PARSEC: A Constraint-Based Parser for Spoken Language Processing

PARSEC (1), a text-based and spoken language processing framework based on the Constraint Dependency Grammar (CDG) developed by Maruyama [26,27], is discussed. The scope of CDG is expanded to allow for the analysis of sentences containing lexically ambiguous words, to allow feature analysis in constraints, and to efficiently process multiple sentence candidates that are likely to arise in spoken language processing. The benefits of the CDG parsing approach are summarized. Additionally, the development CDG grammars using PARSEC grammar writing tools and the implementation of the PARSEC parser for word graphs is discussed. (1) Parallel ARchitecture Sentence Constraine

The Use of Abbreviations in English-Medium Astrophysics Research Paper Titles: A Problematic Issue

In this study, we carry out a qualitative and quantitative analysis of abbreviations in 300 randomly collected research paper titles published in the most prestigious European and US-based Astrophysics journals written in English. Our main results show that the process of shortening words and groups of words is one of the most characteristic and recurrent features in Astrophysics research paper titling construction. In spite of the convenience of abbreviations as a mechanism for word-formation, some of them may pose certain difficulties of understanding and/or misinterpretation because of their specificity, ambiguity, or overlapping. To overcome these difficulties, we propose a series of options which with no doubt would lead to a better interaction among the different branches of Astrophysics in particular and of science in general and would definitely improve how research is currently performed and communicated

CLiFF Notes: Research In Natural Language Processing at the University of Pennsylvania

The Computational Linguistics Feedback Forum (CLIFF) is a group of students and faculty who gather once a week to discuss the members\u27 current research. As the word feedback suggests, the group\u27s purpose is the sharing of ideas. The group also promotes interdisciplinary contacts between researchers who share an interest in Cognitive Science. There is no single theme describing the research in Natural Language Processing at Penn. There is work done in CCG, Tree adjoining grammars, intonation, statistical methods, plan inference, instruction understanding, incremental interpretation, language acquisition, syntactic parsing, causal reasoning, free word order languages, ... and many other areas. With this in mind, rather than trying to summarize the varied work currently underway here at Penn, we suggest reading the following abstracts to see how the students and faculty themselves describe their work. Their abstracts illustrate the diversity of interests among the researchers, explain the areas of common interest, and describe some very interesting work in Cognitive Science. This report is a collection of abstracts from both faculty and graduate students in Computer Science, Psychology and Linguistics. We pride ourselves on the close working relations between these groups, as we believe that the communication among the different departments and the ongoing inter-departmental research not only improves the quality of our work, but makes much of that work possible

Roses Are Red, Violets Are Bluehow Poetry In Science Can Help Students Learn Something New

This study was an attempt to examine how poetry integrated with science could assist eighth graders in the memorization of key science vocabulary words. Furthermore, it would investigate if student attitude, interest, and motivation would improve with the use of the poetry. Instruction was adjusted to implement poetry into astronomy lessons. Memorization activities such as poems, chanting, and repetition were used to help students remember the vocabulary and the definitions. Pre/post tests were used to interpret if the poetry did assist in the memorization of the astronomy vocabulary. Science interest surveys and science attitude surveys were used to interpret if the use of the poetry helped to increase student interests in and attitudes toward science. This study was intended to be a first step toward proving how poetry could benefit students in the areas of memorization, attitude, and interest of science; and if successful, perhaps could be used to assist in other subjects as well

音声翻訳における文解析技法について

本文データは平成22年度国立国会図書館の学位論文(博士)のデジタル化実施により作成された画像ファイルを基にpdf変換したものである京都大学0048新制・論文博士博士(工学)乙第8652号論工博第2893号新制||工||968(附属図書館)UT51-94-R411(主査)教授 長尾 真, 教授 堂下 修司, 教授 池田 克夫学位規則第4条第2項該当Doctor of EngineeringKyoto UniversityDFA

The Unstoppable Rise of Computational Linguistics in Deep Learning

In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures. We focus on the importance of variable binding and its instantiation in attention-based models, and argue that Transformer is not a sequence model but an induced-structure model. This perspective leads to predictions of the challenges facing research in deep learning architectures for natural language understanding.Comment: 13 pages. Accepted for publication at ACL 2020, in the theme trac

The Victorious Christian Life: Overcoming Alcohol Dependence through the Phenomenon of Christian Spirituality

Interviews were conducted with graduates of a northeastern evangelical Christian-based treatment program to discover insights regarding the role that the phenomenon of Christian spirituality played in their recovery. Eight themes were extracted from three periods of time in the participants\u27 lives as they related to the phenomenon of Christian spirituality: prior to treatment; during treatment; and after treatment. These themes revealed that the participants lacked resiliencies to overcome alcohol dependence themselves. All participants reported that critical aspects of Christian spirituality, such as regeneration and cultivation of a divine-human relationship through lifestyle exercise of Spiritual Disciplines and diligent application of the fruit of the Holy Spirit and Christian virtues worked to transform them during and after treatment

Legal Agreement

This Article grapples with the question of what it means to agree about what the law is. First, it shows that the question of what it means to “agree about the law” invites us to consider many different kinds of agreement and disagreement we might have about what the law is. Second, it shows that without selecting one of these kinds of agreement, we cannot speak intelligibly about whether we agree or disagree. Third, it explains that this failure to choose is a source of much confusion and apparent disagreement between competing philosophers and philosophies of law. Fourth, it argues that the presence or absence of at least certain kinds of agreement cannot tell us whether we should prefer Legal Positivism or other theories of law. Finally, it concludes that the pervasive reliance among Positivists on a generalized notion that there exists “massive agreement” about the law should be regarded skeptically