This paper describes the resource- and system-building efforts of an eight-week Johns Hopkins University Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation (SIMT). We describe a new modality/negation (MN) annotation scheme, the creation of a (publicly available) MN lexicon, and two automated MN taggers that we built using the annotation scheme and lexicon. Our annotation scheme isolates three components of modality and negation: a trigger (a word that conveys modality or negation), a target (an action associated with modality or negation) and a holder (an experiencer of modality). We describe how our MN lexicon was semi-automatically produced and we demonstrate that a structure-based MN tagger results in precision around 86% (depending on genre) for tagging of a standard LDC data set. We apply our MN annotation scheme to statistical machine translation using a syntactic framework that supports the inclusion of semantic annotations. Syntactic tags enriched with semantic annotations are assigned to parse trees in the target-language training texts through a process of tree grafting. While the focus of our work is modality and negation, the tree grafting procedure is general and supports other types of semantic information. We exploit this capability by including named entities, produced by a pre-existing tagger, in addition to the MN elements produced by the taggers described in this paper. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English test set. This finding supports the hypothesis that both syntactic and semantic information can improve translation quality.

OXFORD 1994 Oxford University Press, Walton Street, Oxford 0X2 6DP Oxford New York Athens Auckland Bangkok Bombay Calcutta Cape Town Dar es Salaam Delhi Florence Hong Kong Istanbul Karachi Kuala Lumpur Madras Madrid Melbourne Mexico City Nairobi Paris Singapore Taipei Tokyo Toronto and associated companies in Berlin lbadan Published in the United States by Oxford University Press Inc., New York 0 E. K. Furukawa, D. Michie, and S. Muggleton, 1994 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press. Enquiries concerning reproduction outside those terms and in other countries should be sent to the Rights Department, Oxford University Press, at the address above. This book is sold subject to the condition that it shall not, by way of trade or otherwise, be lent, re-sold, hired out, or otherwise circulated without the publisher's prior consent in any form of binding or cover other than that in which it is published and without a similar condition including this condition being imposed on the subsequent purchaser. The founder of modern computational logic, J.A. Robinson, opens this volume with a chapter on the field's great forefathers John von Neumann and Alan Turing.

Machine Intelligence 1 (1967) (eds N. Collins and D. Michie) Oliver & Boyd, Edinburgh Machine Intelligence 2 (1968) (eds E. Dale and D. Michie) Oliver & Boyd, Edinburgh (1 and 2 published as one volume in 1971 by Edinburgh University Press) (eds N. Collins, E. Dale, and D. Michie) Machine Intelligence 3 (1968) (ed. CLARENDON PRESS - OXFORD 1991 Oxford University Press, Walton Street, Oxford 0X2 6DP Oxford New York Toronto Delhi Bombay Calcutta Madras Karachi Petaling Jaya Singapore Hong Kong Tokyo Nairobi Dar es Salaam Cape Town Melbourne Auckland and associated companies in Berlin lbadan Oxford is a trade mark of Oxford University Press Published in the United States by Oxford University Press, New York C J. E. Hayes, D. Michie, and E. Tyugu, 1991 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Oxford University Press British Library Cataloguing in Publication Data Machine intelligence. ISBN 0-19-853823-5 Library of Congress Cataloging in Publication Data Machine intelligence 12: towards an automated logic of human thought /edited by J. E. Hayes, D. Michie, and It is a pleasure to contribute an introduction to this twelfth volume of the international Machine Intelligence series. My own work has, at times, cast me in the scientific roles of experimenter, instrumentation designer, and administrator.

Machine Intelligence 1 (1967) (eds N. Collins and D. Michie) Oliver & Boyd, Edinburgh Machine Intelligence 2 (1968) (eds E. Dale and D. Michie) Oliver & Boyd, Edinburgh (1 and 2 published as one volume in 1971 by Edinburgh University Press) (eds N. Collins, E. Dale, and D. Michie). CLARENDON PRESS OXFORD 1988 Oxford University Press, Walton Street, Oxford 0X2 6DP Oxford New York Toronto Delhi Bombay Calcutta Madras Karachi Petaling Jaya Singapore Hong Kong Tokyo Nairobi Dar es Salaam Cape Town Melbourne Auckland and associated companies in Berlin lbadan Oxford is a trade mark of Oxford University Press Published in the United States by Oxford University Press, New York J. E. Hayes, D. Michie, and J. Richards 1988 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Oxford University Press British Library Cataloguing in Publication Data Machine Intelligence. Richard J. 006.3 ISBN 0-19-853718-2 Library of Congress Cataloging in Publication Data Data available Typeset and printed in Northern Ireland at The Universities Press (Belfast) Ltd. Held at intervals in Scotland, the first seven International Machine Intelligence Workshops spanning the period of 1965-71 were involved in developing the new subject internationally--in those early days mainly as a mid-Atlantic phenomenon.

This paper describes some work on automatically generating finite counterexamples in topology, and the use of counterexamples to speed up proof discovery in intermediate analysis, and gives some examples theorems where human provers are aided in proof discovery by the use of examples.

Seven years or so after it was first proposed (Kowalski 1974), the technique of'logic programming' today has an enthusiastic band of users and an increasingly impressive record of applications. For most of these people, logic progamming means PROLOG, the system defined and originally implemented by the Marseille group (Roussel 1975). PROLOG has since been implemented in several other places, most notably at Edinburgh (Warren et al. 1977). Much of the rapid success of logic progamming is due to these implementations of PROLOG (as well as to the inspired missionary work of Kowalski, van Emden, Clark and others). The Edinburgh PROLOG system is in particular a superb piece of software engineering which allows the logic progammer to compile assertions into DEC-10 machine code and thus run logic programs with an efficiency which compares favourably with that of compiled LISP. All other implementations of logic programming (including our own, which we describe in this paper) are based on interpreters.

Web services allow communication between heterogeneous systems in a distributed environment. Their enormous success and their increased use led to the fact that thousands of Web services are present on the Internet. This significant number of Web services which not cease to increase has led to problems of the difficulty in locating and classifying web services, these problems are encountered mainly during the operations of web services discovery and substitution. Traditional ways of search based on keywords are not successful in this context, their results do not support the structure of Web services and they consider in their search only the identifiers of the web service description language (WSDL) interface elements. The methods based on semantics (WSDLS, OWLS, SAWSDL...) which increase the WSDL description of a Web service with a semantic description allow raising partially this problem, but their complexity and difficulty delays their adoption in real cases. Measuring the similarity between the web services interfaces is the most suitable solution for this kind of problems, it will classify available web services so as to know those that best match the searched profile and those that do not match. Thus, the main goal of this work is to study the degree of similarity between any two web services by offering a new method that is more effective than existing works.

We consider the Principal Component Analysis problem for large tensors of arbitrary orderk under a single-spike (or rank-one plus noise) model. On the one hand, we use information theory, and recent results in probability theory, to establish necessaryand sufficient conditions under which the principal component can be estimated using unbounded computational resources. It turns out that this is possible as soon as the signal-to-noise ratio β becomes larger than C k log k (and in particular β can remain bounded as the problem dimensions increase). On the other hand, we analyze several polynomial-time estimation algorithms, based on tensor unfolding, power iteration and message passing ideas from graphical models.We show that, unless the signal-to-noise ratio diverges in the system dimensions, none of these approaches succeeds. This is possibly related to a fundamental limitationof computationally tractable estimators for this problem. We discuss various initializations for tensor power iteration, and show that a tractable initialization based on the spectrum of the unfolded tensor outperforms significantly baseline methods, statistically and computationally. Finally, we consider thecase in which additional side information is available about the unknown signal. We characterize the amount of side information that allows the iterative algorithms to converge to a good estimate.

Electroencephalography (EEG) recordings of rhythm perception might contain enough information to distinguish different rhythm types/genres or even identify the rhythms themselves. We apply convolutional neural networks (CNNs) to analyze and classify EEG data recorded within a rhythm perception study in Kigali, Rwanda which comprises 12 East African and 12 Western rhythmic stimuli - each presented in a loop for 32 seconds to 13 participants. We investigate the impact of the data representation and the pre-processing steps for this classification tasks and compare different network structures. Using CNNs, we are able to recognize individual rhythms from the EEG with a mean classification accuracy of 24.4% (chance level 4.17%) over all subjects by looking at less than three seconds from a single channel. Aggregating predictions for multiple channels, a mean accuracy of up to 50% can be achieved for individual subjects.

While topic models can discover patterns of word usage in large corpora, it is difficult to meld this unsupervised structure with noisy, human-provided labels, especially when the label space is large. In this paper, we present a model-Label to Hierarchy (L2H)-that can induce a hierarchy of user-generated labels and the topics associated with those labels from a set of multi-labeled documents. The model is robust enough to account for missing labels from untrained, disparate annotators and provide an interpretable summary of an otherwise unwieldy label set. We show empirically the effectiveness of L2H in predicting held-out words and labels for unseen documents.