book review learning to classify text using support vector machines methods theory and algorithms by thorsten joachims cornell university dordrecht kluwer academic publishers 2002 xvii 205 pp hardbound isbn 0 7923 7679 x 110 00 121 00 77 00

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Production of a New Drug: A Sequential Investment ProcessUnder Uncertainty

On the basis of a database of more than 80 thousand records on total retails and production costs of the pharmaceutical industry worldwide we consider four classes of drugs. We evaluate the expected profits of an investment in a new drug in the four classes of pharmaceutical products by considering the standard NPV evaluation. We compare these outcomes with the evaluation of the expected profits of the four new drugs obtained by the real option approach. Interestingly enough quite different outcomes are obtained. These results loom on the capacity of standard methods to give a reliable evaluation of real investment projects that are analogous to compound optionscompound option, real option valuation, net present value, drugs

Designing Semantic Kernels as Implicit Superconcept Expansions

Recently, there has been an increased interest in the exploitation of background knowledge in the context of text mining tasks, especially text classification. At the same time, kernel-based learning algorithms like Support Vector Machines have become a dominant paradigm in the text mining community. Amongst other reasons, this is also due to their capability to achieve more accurate learning results by replacing standard linear kernel (bag-of-words) with customized kernel functions which incorporate additional apriori knowledge. In this paper we propose a new approach to the design of ‘semantic smoothing kernels’ by means of an implicit superconcept expansion using well-known measures of term similarity. The experimental evaluation on two different datasets indicates that our approach consistently improves performance in situations where (i) training data is scarce or (ii) the bag-ofwords representation is too sparse to build stable models when using the linear kernel

A context based model for sentiment analysis in twitter for the italian language

Studi recenti per la Sentiment Analysis in Twitter hanno tentato di creare modelli per caratterizzare la polarit´a di un tweet osservando ciascun messaggio in isolamento. In realt`a, i tweet fanno parte di conversazioni, la cui natura pu`o essere sfruttata per migliorare la qualit`a dell’analisi da parte di sistemi automatici. In (Vanzo et al., 2014) `e stato proposto un modello basato sulla classificazione di sequenze per la caratterizzazione della polarit` a dei tweet, che sfrutta il contesto in cui il messaggio `e immerso. In questo lavoro, si vuole verificare l’applicabilit`a di tale metodologia anche per la lingua Italiana.Recent works on Sentiment Analysis over Twitter leverage the idea that the sentiment depends on a single incoming tweet. However, tweets are plunged into streams of posts, thus making available a wider context. The contribution of this information has been recently investigated for the English language by modeling the polarity detection as a sequential classification task over streams of tweets (Vanzo et al., 2014). Here, we want to verify the applicability of this method even for a morphological richer language, i.e. Italian

A discriminative approach to grounded spoken language understanding in interactive robotics

Spoken Language Understanding in Interactive Robotics provides computational models of human-machine communication based on the vocal input. However, robots operate in specific environments and the correct interpretation of the spoken sentences depends on the physical, cognitive and linguistic aspects triggered by the operational environment. Grounded language processing should exploit both the physical constraints of the context as well as knowledge assumptions of the robot. These include the subjective perception of the environment that explicitly affects linguistic reasoning. In this work, a standard linguistic pipeline for semantic parsing is extended toward a form of perceptually informed natural language processing that combines discriminative learning and distributional semantics. Empirical results achieve up to a 40% of relative error reduction

Robust Spoken Language Understanding for House Service Robots

Service robotics has been growing significantly in thelast years, leading to several research results and to a numberof consumer products. One of the essential features of theserobotic platforms is represented by the ability of interactingwith users through natural language. Spoken commands canbe processed by a Spoken Language Understanding chain, inorder to obtain the desired behavior of the robot. The entrypoint of such a process is represented by an Automatic SpeechRecognition (ASR) module, that provides a list of transcriptionsfor a given spoken utterance. Although several well-performingASR engines are available off-the-shelf, they operate in a generalpurpose setting. Hence, they may be not well suited in therecognition of utterances given to robots in specific domains. Inthis work, we propose a practical yet robust strategy to re-ranklists of transcriptions. This approach improves the quality of ASRsystems in situated scenarios, i.e., the transcription of roboticcommands. The proposed method relies upon evidences derivedby a semantic grammar with semantic actions, designed tomodel typical commands expressed in scenarios that are specificto human service robotics. The outcomes obtained throughan experimental evaluation show that the approach is able toeffectively outperform the ASR baseline, obtained by selectingthe first transcription suggested by the AS

Introduction to Machine Learning

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Late Holocene landscape change history related to the Alpine Fault determined from drowned forests in Lake Poerua, Westland, New Zealand

Abstract. Lake Poerua is a small, shallow lake that abuts the scarp of the Alpine Fault on the West Coast of New Zealand's South Island. Radiocarbon dates from drowned podocarp trees on the lake floor, a sediment core from a rangefront alluvial fan, and living tree ring ages have been used to deduce the late Holocene history of the lake. Remnant drowned stumps of kahikatea (Dacrycarpus dacrydioides) at 1.7–1.9 m water depth yield a preferred time-of-death age at 1766–1807 AD, while a dryland podocarp and kahikatea stumps at 2.4–2.6 m yield preferred time-of-death ages of ca. 1459–1626 AD. These age ranges are matched to, but offset from, the timings of Alpine Fault rupture events at ca. 1717 AD, and either ca. 1615 or 1430 AD. Alluvial fan detritus dated from a core into the toe of a rangefront alluvial fan, at an equivalent depth to the maximum depth of the modern lake (6.7 m), yields a calibrated age of AD 1223–1413. This age is similar to the timing of an earlier Alpine Fault rupture event at ca. 1230 AD ± 50 yr. Kahikatea trees growing on rangefront fans give ages of up to 270 yr, which is consistent with alluvial fan aggradation following the 1717 AD earthquake. The elevation levels of the lake and fan imply a causal and chronological link between lake-level rise and Alpine Fault rupture. The results of this study suggest that the growth of large, coalescing alluvial fans (Dry and Evans Creek fans) originating from landslides within the rangefront of the Alpine Fault and the rise in the level of Lake Poerua may occur within a decade or so of large Alpine Fault earthquakes that rupture adjacent to this area. These rises have in turn drowned lowland forests that fringed the lake. Radiocarbon chronologies built using OxCal show that a series of massive landscape changes beginning with fault rupture, followed by landsliding, fan sedimentation and lake expansion. However, drowned Kahikatea trees may be poor candidates for intimately dating these events, as they may be able to tolerate water for several decades after metre-scale lake level rises have occurred