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Collaborative robots can improve ergonomics on factory floors while allowing a higher level of flexibility in production. The evolution of robotics and cyber-physical systems in size and functionality has enabled new applications which were never foreseen in traditional industrial robots. However, the current human-robot collaboration (HRC) technologies are limited in reliability and safety, which are vital in risk-critical scenarios. Certainly, confusion about European safety regulations has led to situations where collaborative robots operate behind security barriers, thus negating their advantages while reducing overall application productivity. Despite recent advances, developing a safe collaborative robotic system for performing complex industrial or daily tasks remains a challenge. Multiple influential factors in HRC make it difficult to define a clear classification to understand the depth of collaboration between humans and robots. In this article, we review the state of the art in reliable collaborative robotic work cells and propose a reference model to combine influential factors such as robot autonomy, collaboration, and safety modes to redefine HRC categorization.

Background The aetiology of ANCA-associated vasculitis (AAV) and triggers of relapse are poorly understood. Vitamin D (vitD) is an important immunomodulator, potentially responsible for the observed latitudinal differences between granulomatous and non-granulomatous AAV phenotypes. A narrow ultraviolet B spectrum induces vitD synthesis (vitD-UVB) via the skin. We hypothesised that prolonged periods of low ambient UVB (and by extension vitD deficiency) are associated with the granulomatous form of the disease and an increased risk of AAV relapse. Methods Patients with AAV recruited to the Irish Rare Kidney Disease (RKD) (n = 439) and UKIVAS (n = 1961) registries were studied. Exposure variables comprised latitude and measures of ambient vitD-UVB, including cumulative weighted UVB dose (CW-D-UVB), a well-validated vitD proxy. An n-of-1 study design was used to examine the relapse risk using only the RKD dataset. Multi-level models and logistic regression were used to examine the effect of predictors on AAV relapse risk, phenotype and serotype. Results Residential latitude was positively correlated (OR 1.41, 95% CI 1.14–1.74, p = 0.002) and average vitD-UVB negatively correlated (0.82, 0.70–0.99, p = 0.04) with relapse risk, with a stronger effect when restricting to winter measurements (0.71, 0.57–0.89, p = 0.002). However, these associations were not restricted to granulomatous phenotypes. We observed no clear relationship between latitude, vitD-UVB or CW-D-UVB and AAV phenotype or serotype. Conclusion Our findings suggest that low winter ambient UVB and prolonged vitD status contribute to AAV relapse risk across all phenotypes. However, the development of a granulomatous phenotype does not appear to be directly vitD-mediated. Further research is needed to determine whether sufficient vitD status would reduce relapse propensity in AAV.

Improving our understanding of how information is encoded in vector space can yield valuable interpretability insights. Alongside vector dimensions, we argue that it is possible for the vector norm to also carry linguistic information. We develop a method to test this: an extension of the probing framework which allows for relative intrinsic interpretations of probing results. It relies on introducing noise that ablates information encoded in embeddings, grounded in random baselines and confidence intervals. We apply the method to well-established probing tasks and find evidence that confirms the existence of separate information containers in English GloVe and BERT embeddings. Our correlation analysis aligns with the experimental findings that different encoders use the norm to encode different kinds of information: GloVe stores syntactic and sentence length information in the vector norm, while BERT uses it to encode contextual incongruity., Comment: 10 pages, 3 tables, Workshop on analyzing and interpreting neural networks for NLP

An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes combining the advantages of inputoutput hidden Markov models and reinforcement learning towards interpretable maintenance decisions. We propose a novel hierarchical-modeling methodology that, at a high level, detects and interprets the root cause of a failure as well as the health degradation of the turbofan engine, while, at a low level, it provides the optimal replacement policy. It outperforms the baseline performance of deep reinforcement learning methods applied directly to the raw data or when using a hidden Markov model without such a specialized hierarchy. It also provides comparable performance to prior work, however, with the additional benefit of interpretability.

Additional file 1: Fig. S1. Flowchart of participant selection for the UKIVAS cohort (1). Fig. S2. Flowchart of participant selection for the RKD cohort (2). Fig. S3. Participant locations at diagnosis for i). UKIVAS and ii). RKD cohort. Shading represents the absolute number of participants recruited to the study in that region, at the time of their diagnosis. Tendency to cluster around vasculitis referral centres is observed. Fig. S4. Distribution of vitD-UVB (kJ/m2) (a and b) and CW-D-UVB (kJ/m2) (c and d) by month and season: CW-D-UVB peaks in late summer and nadirs in winter. Fig. S5. The relationship between latitude (degrees) and a). annual daily average vitD-UVB (kJ/m2, Pearson correlation coefficient (corr) -0.98, p

Applying deep learning models to MRI scans of acute stroke patients to extract features that are indicative of short-term outcome could assist a clinician’s treatment decisions. Deep learning models are usually accurate but are not easily interpretable. Here, we trained a convolutional neural network on ADC maps from hyperacute ischaemic stroke patients for prediction of short-term functional outcome and used an interpretability technique to highlight regions in the ADC maps that were most important in the prediction of a bad outcome. Although highly accurate, the model’s predictions were not based on aspects of the ADC maps related to stroke pathophysiology.

Despite the widespread use of Knowledge Graph Embeddings (KGE), little is known about the security vulnerabilities that might disrupt their intended behaviour. We study data poisoning attacks against KGE models for link prediction. These attacks craft adversarial additions or deletions at training time to cause model failure at test time. To select adversarial deletions, we propose to use the model-agnostic instance attribution methods from Interpretable Machine Learning, which identify the training instances that are most influential to a neural model's predictions on test instances. We use these influential triples as adversarial deletions. We further propose a heuristic method to replace one of the two entities in each influential triple to generate adversarial additions. Our experiments show that the proposed strategies outperform the state-of-art data poisoning attacks on KGE models and improve the MRR degradation due to the attacks by up to 62% over the baselines., 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021)

Background Stroke is a worldwide cause of disability; 40% of stroke survivors sustain cognitive impairments, most of them following inpatient rehabilitation at specialized clinical centers. Web-based cognitive rehabilitation tasks are extensively used in clinical settings. The impact of task execution depends on the ratio between the skills of the treated patient and the challenges imposed by the task itself. Thus, treatment personalization requires a trade-off between patients’ skills and task difficulties, which is still an open issue. In this study, we propose Elo ratings to support clinicians in tasks assignations and representing patients’ skills to optimize rehabilitation outcomes. Objective This study aims to stratify patients with ischemic stroke at an early stage of rehabilitation into three levels according to their Elo rating; to show the relationships between the Elo rating levels, task difficulty levels, and rehabilitation outcomes; and to determine if the Elo rating obtained at early stages of rehabilitation is a significant predictor of rehabilitation outcomes. Methods The PlayerRatings R library was used to obtain the Elo rating for each patient. Working memory was assessed using the DIGITS subtest of the Barcelona test, and the Rey Auditory Verbal Memory Test (RAVLT) was used to assess verbal memory. Three subtests of RAVLT were used: RAVLT learning (RAVLT075), free-recall memory (RAVLT015), and recognition (RAVLT015R). Memory predictors were identified using forward stepwise selection to add covariates to the models, which were evaluated by assessing discrimination using the area under the receiver operating characteristic curve (AUC) for logistic regressions and adjusted R2 for linear regressions. Results Three Elo levels (low, middle, and high) with the same number of patients (n=96) in each Elo group were obtained using the 50 initial task executions (from a total of 38,177) for N=288 adult patients consecutively admitted for inpatient rehabilitation in a clinical setting. The mid-Elo level showed the highest proportions of patients that improved in all four memory items: 56% (54/96) of them improved in DIGITS, 67% (64/96) in RAVLT075, 58% (56/96) in RAVLT015, and 53% (51/96) in RAVLT015R (P Conclusions Elo ratings can support clinicians in early rehabilitation stages in identifying cognitive profiles to be used for assigning task difficulty levels.

It is often the case with new technologies that it is very hard to predict their long-term impacts and as a result, although new technology may be beneficial in the short term, it can still cause problems in the longer term. This is what happened with oil by-products in different areas: the use of plastic as a disposable material did not take into account the hundreds of years necessary for its decomposition and its related long-term environmental damage. Data is said to be the new oil. The message to be conveyed is associated with its intrinsic value. But as in the case of real oil, we should take care to ensure that its use does not create harm in the future. We know from recent history that data can be used in harmful ways, but data also has enormous positive potential when applied to the service of communities. In this article, we highlight the opportunities, problems and best practice of using data.

Supplemental material, sj-pdf-1-bds-10.1177_2053951720915939 for Expectations of artificial intelligence and the performativity of ethics: Implications for communication governance by Aphra Kerr, Marguerite Barry and John D Kelleher in Big Data & Society

We present an approach to design the grid searches for hyper-parameter optimization for recurrent neural architectures. The basis for this approach is the use of mutual information to analyze long distance dependencies (LDDs) within a dataset. We also report a set of experiments that demonstrate how using this approach, we obtain state-of-the-art results for DilatedRNNs across a range of benchmark datasets.

This paper connects a series of papers dealing with taxonomic word embeddings. It begins by noting that there are different types of semantic relatedness and that different lexical representations encode different forms of relatedness. A particularly important distinction within semantic relatedness is that of thematic versus taxonomic relatedness. Next, we present a number of experiments that analyse taxonomic embeddings that have been trained on a synthetic corpus that has been generated via a random walk over a taxonomy. These experiments demonstrate how the properties of the synthetic corpus, such as the percentage of rare words, are affected by the shape of the knowledge graph the corpus is generated from. Finally, we explore the interactions between the relative sizes of natural and synthetic corpora on the performance of embeddings when taxonomic and thematic embeddings are combined., Comment: 7 pages 0 figures

Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method—the U-net—is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We performed both quantitative and qualitative analyses. The U-net models yielded high performance for both the full and the reduced architecture: A Dice value of ~0.88, a 95HD of ~47 voxels and an AVD of ~0.4 voxels. The visual analysis revealed excellent performance in large vessels and sufficient performance in small vessels. Pathologies like cortical laminar necrosis and a rete mirabile led to limited segmentation performance in few patients. The U-net outperfomed the traditional graph-cuts method (Dice ~0.76, 95HD ~59, AVD ~1.97). Our work highly encourages the development of clinically applicable segmentation tools based on deep learning. Future works should focus on improved segmentation of small vessels and methodologies to deal with specific pathologies

From theoretical linguistic and cognitive perspectives, situated dialog systems are interesting as they provide ideal test-beds for investigating the interaction between language and perception. At the same time there are a growing number of practical applications, for example robotic systems and driver-less cars, where spoken interfaces, capable of situated dialog, promise many advantages. To date, however much of the work on situated dialog has focused resolving anaphoric or exophoric references. This paper, by contrast, opens up the question of how perceptual memory and linguistic references interact, and the challenges that this poses to computational models of perceptually grounded dialog., Comment: 18 Pages, 4 Figures

Creating word embeddings that reflect semantic relationships encoded in lexical knowledge resources is an open challenge. One approach is to use a random walk over a knowledge graph to generate a pseudo-corpus and use this corpus to train embeddings. However, the effect of the shape of the knowledge graph on the generated pseudo-corpora, and on the resulting word embeddings, has not been studied. To explore this, we use English WordNet, constrained to the taxonomic (tree-like) portion of the graph, as a case study. We investigate the properties of the generated pseudo-corpora, and their impact on the resulting embeddings. We find that the distributions in the psuedo-corpora exhibit properties found in natural corpora, such as Zipf’s and Heaps’ law, and also ob- serve that the proportion of rare words in a pseudo-corpus affects the performance of its embeddings on word similarity.

Dialogue State Tracking is arguably one of the most challenging tasks among dialogue processing problems due to the uncertainties of language and complexity of dialogue contexts. We argue that this problem is made more challenging by variable dependencies in the dialogue states that must be accounted for in processing. In this paper we give details on our motivation for this argument through statistical tests on a number of dialogue datasets. We also propose a machine learning-based approach called energy-based learning that tackles variable dependencies while performing prediction on the dialogue state tracking tasks.

Incrementality is a fundamental feature of language in real world use. To this point, however, the vast majority of work in automated dialogue processing has focused on language as turn based. In this paper we explore the challenge of incremental dialogue state tracking through the development and analysis of a multi-task approach to incremental dialogue state tracking. We present the design of our incremental dialogue state tracker in detail and provide evaluation against the well known Dialogue State Tracking Challenge 2 (DSTC2) dataset. In addition to a standard evaluation of the tracker, we also provide an analysis of the Incrementality phenomenon in our model’s performance by analyzing how early our models can produce correct predictions and how stable those predictions are. We find that the Multi-Task Learning-based model achieves state-of-the-art results for incremental processing.

An urgent limitation in current Image Captioning models is their tendency to produce generic captions that avoid the interesting detail which makes each image unique. To address this limitation, we propose an approach that enforces a stronger alignment between image regions and specific segments of text. The model architecture is composed of a visual region proposer, a region-order planner and a region-guided caption generator. The region-guided caption generator incorporates a novel information gate which allows visual and textual input of different frequencies and dimensionalities in a Recurrent Neural Network.

We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility. In particular, the assumed Markovian nature of mobility was used to establish an upper bound on the predictability of human mobility, based on the temporal entropy. Since its inception, this bound has been widely used for validating the performance of mobility prediction models. We show that the variants of recurrent neural network architectures can achieve significantly higher prediction accuracy surpassing this upper bound. The central objective of our work is to show that human-mobility dynamics exhibit criticality characteristics which contributes to this discrepancy. In order to explain this anomaly, we shed light on the underlying assumption that human mobility characteristics follow an exponential decay that has resulted in this bias. By evaluating the predictability on real-world datasets, we show that human mobility exhibits scale-invariant long-distance dependencies, bearing resemblance to power-law decay, contrasting with the initial Markovian assumption. We experimentally validate that this assumption inflates the estimated mobility entropy, consequently lowering the upper bound on predictability. We demonstrate that the existing approach of entropy computation tends to overlook the presence of long-distance dependencies and structural correlations in human mobility. We justify why recurrent-neural network architectures that are designed to handle long-distance dependencies surpass the previously computed upper bound on mobility predictability.

This article revisits the question of ‘la bêtise’ or stupidity in the era of Artificial Intelligence driven by Big Data, it extends on the questions posed by Gille Deleuze and more recently by Bernard Stiegler. However, the framework for revisiting the question of la bêtise will be through the lens of contemporary computer science, in particular the development of data science as a mode of analysis, sometimes, misinterpreted as a mode of intelligence. In particular, this article will argue that with the advent of forms of hype (sometimes referred to as the hype cycle) in relation to big data and modalities of data analytics there is a form of computational stupidity or functional stupidity at work. The exaggerated promises of big data to solve everything are overblown expectations which will lead ultimately to a form of disillusionment with data science. This can be seen in a number of domains, for example smart city technologies, the internet of things, and machine translation. In addition to the negative effects of exaggerated claims of Big Data is the possibility that societal norms will facilitate Big Data technological change by incorporating the bêtise of Big Data, thus leading to a change in our relationship to technology, examples of this would be privacy standards and ownership of data. This paper will conclude by setting out the analysis some of the limitations of Artificial intelligence and Big Data in order to allow a re-examination of the claims made.

Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectively. The approaches are frequently considered to stand in opposition to each other. Examining some recent approaches in deep learning we argue that deep neural networks incorporate both perspectives and, furthermore, that leveraging this aspect of deep learning may help in solving complex problems within language technology, such as modelling language and perception in the domain of spatial cognition.

This paper examines to what degree current deep learning architectures for image caption generation capture spatial lan- guage. On the basis of the evaluation of examples of generated captions from the literature we argue that systems capture what objects are in the image data but not where these objects are located: the cap- tions generated by these systems are the output of a language model conditioned on the output of an object detector that cannot capture fine-grained location information. Although language models provide useful knowledge for image captions, we argue that deep learning image captioning architectures should also model geometric rela- tions between objects.

Dialogue State Tracking is an important task in dialogue management as it provides a mechanism to monitor dialogue contributions. In this paper we introduce an Incremental Joint Model as a new approach to the task. Our tracker is capable of incrementally tracking Dialogue States. We base our model and analysis on the datasets provided in the Second Dialogue State Tracking Challenge (DSTC2). Our early stage evaluations are based on comparisons of our tracker with both the baseline model provide by the DSTC2 and also LecTrack: a state-of-the-art incremental LSTM-based tracker. The main finding of our experiments is that moving from an utterance based to incremental word based tracker results in better performance for our RNN based joint task models.

The aim of this paper is to present techniques and results for identifying the key of Irish traditional music melodies, or tunes. Several corpora are used, consisting of both symbolic and audio representations. Monophonic and heterophonic recordings are present in the audio datasets. Some particularities of Irish traditional music are discussed, notably its modal nature. New key-profiles are defined, that are better suited to Irish music.

In this paper we argue that since the beginning of the natural language processing or computational linguistics there has been a strong connection between logic and machine learning. First of all, there is something logical about language or linguistic about logic. Secondly, we argue that rather than distinguishing between logic and machine learning, a more useful distinction is between top-down approaches and data-driven approaches. Examining some recent approaches in deep learning we argue that they incorporate both properties and this is the reason for their very successful adoption to solve several problems within language technology.

This paper presents an interactive visualisation tool that encodes the scope chain, and information related to the scope chain, within source code. The main goal of the tool is to support programmers when dealing with issues related to scope and to provide answers to questions such as to which scope does a specific variable or function belong to and can I access a specific variable from the scope I am currently located in. The design guidelines followed during the implementation of the tool, as well as the design rationale behind the main features of the tool are described. Finally, the results of a pilot user experience evaluation study are presented where an interesting observation was that the tool seemed to support programmers in verifying and correcting their assumptions when asked questions about specific scoping issues within a source code document.

This paper presents a short introduction to neural networks and how they are used for machine translation and concludes with some discussion on the current research challenges being addressed by neural machine translation (NMT) research. The primary goal of this paper is to give a no-tears introduction to NMT to readers that do not have a computer science or mathematical background. The secondary goal is to provide the reader with a deep enough understanding of NMT that they can appreciate the strengths of weaknesses of the technology. The paper starts with a brief introduction to standard feed-forward neural networks (what they are, how they work, and how they are trained), this is followed by an introduction to word-embeddings (vector representations of words) and then we introduce recurrent neural networks. Once these fundamentals have been introduced we then focus in on the components of a standard neural-machine translation architecture, namely: encoder networks, decoder language models, and the encoder-decoder architecture.

An emerging trend in music information retrieval (MIR) is the use of supervised machine learning to train automatic music transcription models. A prerequisite of adopting a machine learning methodology is the availability of annotated corpora. However, different genres of music have different characteristics and modelling these characteristics is an important part of creating state of the art MIR systems. Consequently, although some music corpora are available the use of these corpora is tied to the specific music genre, instrument type and recording context the corpus covers. This paper introduces the first corpus of annotations of audio recordings of Irish traditional dance music that covers multiple instrument types and both solo studio and live session recordings. We first discuss the considerations that motivated our design choices in developing the corpus. We then benchmark a number of automatic music transcription algorithms against the corpus. The underlying dataset for this research is available here at Github or here in Arrow .

The Folk Music Analysis Workshop brings together computational music analysis and ethnomusicology. Both symbolic and audio representations of music are considered, with a broad range of scientific approaches being applied (signal processing, graph theory, deep learning). The workshop features a range of interesting talks from international researchers in areas such as Indian classical music, Iranian singing, Ottoman-Turkish Makam music scores, Flamenco singing, Irish traditional music, Georgian traditional music and Dutch folk songs. Invited guest speakers were Anja Volk, Utrecht University and Peter Browne, Technological University Dublin.

This paper presents a study which evaluates the use of a tree visualisation (icicle tree) to encode the hierarchical structure of source code. The tree visualisation was combined with a source code editor in order to function as a compact overview to facilitate the process of comprehending the global structure of a source code document. Results from our study show that pro- viding an overview visualisation led to an increase in accuracy and a decrease in completion time when participants performed counting tasks. However, in locating tasks, the presence of the visualisation led to a decrease in participants’ performance.

We performed an experiment in which human participants interacted through a natural language dialogue interface with a simulated robot to fulfil a series of object manipulation tasks. We introduced errors into the robot’s perception, and observed the resulting problems in the dialogues and their resolutions. We then introduced different methods for the user to request information about the robot’s understanding of the environment. In this work, we describe the effects that the robot’s perceptual errors and the information request options available to the participant had on the reformulation of the referring expressions the participants used when resolving a unsuccessful reference.

In a situated dialogue, misunderstandings may arise if the participants perceive or interpret the environment in different ways. In human-computer dialogue this may be due the sensor errors. We present an experiment system and a series of experiments in which we investigate this problem.

To apply machine learning techniques to the production and interpretation of natural language, we need large amounts of annotated language data. Manual annotation, however, is an expensive and time consuming process since it involves human annotators looking at the data and explicitly adding information that is implicitly contained in the data, based on their judgment. This work presents an approach to automatically annotating referring expressions in situated dialogues by exploiting the interpretation of language by the participants in the dia- logue. We associate instructions concerning objects in the environment with automatically detected events involving these objects and predict the referents of referring expressions in the instructions on the basis of the objects aected by the events. We judge the reliability of these pre- dictions based on the temporal and textual distance between instruction and event. We apply our approach to an annotated corpus and evalu- ate the results against human annotation. The evaluation shows that the approach can be used to accurately annotate a large proportion of the utterances in the corpus dialogues and highlight those utterances for which human annotation is required, thus reducing the amount of human annotation required.

Localisation and navigation are still of the most important issues in mobile robotics. In certain indoor application scenarios Radio frequency identification (RFID) based absolute localisation has been found to be especially successful in supporting navigation. In this paper we examine the feasibility of an RFID and compass based approach to robot localisation and navigation for indoor environments that are dominated by corridors. We present a proof of concept system and show how it can be used to localized within and navigate through an environment.

Localisation and navigation are still two of the most important issues in mobile robotics. In certain indoor application scenarios RFID (radio frequency identification)-based absolute localisation has been found to be especially successful in supporting navigation. In this paper we evaluate the feasibility of an RFID and compass based approach to robot localisation and navigation for indoor environments that are dominated by corridors. We describe our system and evaluate its performance in a small, but full-scale, test environment.

This research outlines an Intelligent Virtual Agent (IVA) interface, where multiple users will be able to interact with 3D avatars. This will take place in a distributed multi-modal environ- ment where the LOK8 Avatar System (AS) will need to locate it’s users from a crowd, using face tracking and novel 3D animation techniques.

Mixed-reality virtual agents are an attractive solution to the problems associated with human-robot interaction, allowing all the expressiveness of virtual characters to be married with the advantages of a physical artifact which exists in a shared environment with the user. However, common approaches to achieving this restrict the virtual characters appearing on top of, or encompassing the robot. This paper describes the Stepping Off the Stage system in which mixed-reality agents are allowed to step off the robot stage and move to other parts of the environment, offering compelling new interaction possibilities.

Engineering education deals primarily with calculating quantitative performance of engineering objects, such as machines, circuits or dams, and with designing variations of these objects. However when engineering graduates enter the workforce they must be able to do a great deal more than solve the technical problems taught in engineering school [1]. More specifically they will need to deal with a great range of problems some of which are not technical engineering problems at all. Examples of such problems include working as part of a larger group, project management, negotiation, component sourcing and an awareness of the multi-disciplinary nature of engineering. Such real-world engineering problems force graduate engineers to draw on their professional and personal resourcefulness, adapt to on-the-job pressure, cope with people problems and broaden their knowledge base. Increased competition and high labour costs put pressure on engineering graduates to contribute to companies as soon as they enter the workplace and reduces the time available to graduates to develop these skills on-the-job. To help students develop real-world engineering skills as part of their engineering education the Mechanical Engineering Department in Technological University Dublin (DIT) three years ago introduced Problem Based Learning (PBL). This paper describes a PBL module for Third Year Mechanical Engineering students at DIT designed to help students refine their soft skills in addition to a deeper understanding of their technical skills. This approach, developed by Kelleher, gives students the opportunity to develop a range of experiences which will prepare them to contribute to their employer on entry to the company.

This papers desibes a set of systems developed at DIT for the Referring Expression Generation challenage at INLG 2008.In Proceedings of the 5th International Natural Language Generation Conference (INLG-08)

Negation is commonly seen in clinical documents [Chapman et al., 2001a] ”In clinical reports the presence of a term does not necessarily indicate the presence of the clinical condition represented by that term. In fact, many of the most frequently described findings and diseases in discharge summaries, radiology reports, history and physical exams, and other transcribed reports are denied in the patient” [Chapman et al., 2001b, page. 301].

This section describes the two systems developed at DIT for the attribute selection track of the REG 2008 challenge. Both of theses systems use an incremental greedy search to generate descriptions, similar to the incremental algorithm described in (Dale and Reiter, 1995). The output of these incremental algorithms are, to a large extent, determined by the order in which the algorithm tests the target object’s attributes for inclusion in the description. Indeed, the major difference between the two systems described in this section is the mechanism used to order the attributes for inclusion.

This paper describes the approach of the DIT AIGroup to the i2b2 Obesity Challenge to build a system to diagnose obesity and related co-morbidities from narrative, unstructured patient records. Based on experimental results a system was developed which used knowledge-light text classification using decision trees, and negation labelling.

The DIT system uses an incremental greedy search to generate descriptions, similar to the incremental algorithm described in (Dale and Reiter, 1995). The selection of the next attribute to be tested for inclusion in the description is ordered by the absolute frequency of each attribute in the training corpus. Attributes are selected in descending order of frequency (i.e. the attribute that occurred most frequently in the training corpus is selected first). Where two or more attributes have the same frequency of occurrence the first attribute found with that frequency is selected. The type attribute is always included in the description. Other attributes are included in the description if they exclude at least 1 distractor from the set of distractors that fulfil the description generated prior that attribute’s selection.The algorithm terminates when a distinguishing description has been generated (i.e., all the distractors have been excluded) or when all the target’s attributes have been tested for inclusion in the description.

This volume contains the papers presented at the Fourth ACL-SIGSEM Workshop on Prepositions. This workshop is endorsed by the ACL Special Interest Group on Semantics (ACL-SIGSEM), and is hosted in conjunction with ACL 2007, taking place on 28th June, 2007 in Prague, the Czech Republic.

The DIT system uses an incremental greedy search to generate descriptions, (similar to the incremental algorithm described in (Dale and Reiter, 1995) ) incremental algorithm). The selection of the next attribute to be tested for inclusion in the description is ordered by the absolute frequency of each attribute in the training corpus. Attributes are selected in descending order of frequency (i.e. the attribute that occurred most frequently in the training corpus is selected first).

DIT’s nascent speech corpus will allow a body of spoken material to be searched for features of informal native speech via a normalised transcription. Once located, the original sound files can be played at normal speed or slowed down in order to better study the recorded speech. The DIT speech corpus treats speed of delivery as a key element in producing the elisions, assimilation, reductions and co-articulations characteristic of native-to-native dialogues. Lack of training in dealing with this spoken register can lead to lack of preparation for the world of real speech and even to a degree of social exclusion. It is also envisaged that non-native speech will be included in the corpus so that comparisons can be drawn between native speech and that of various nativised productions of the same items. The database will therefore be capable of being queried on a multi-factorial basis depending on user needs. The optimal segmentation of the normalised transcript is, however, far from clear, and some of the difficulties will be touched on by this presentation. While the tone unit, as proposed by David Brazil, for example, is attractive as a base unit for displaying the concordanced speech corpus, it nevertheless raises problems when there is a discrepancy between semantic segmentation and actual phonetic delivery. The rationale for the currently adopted minimal unit will be explained and members of the audience will be invited to offer feedback on any requirements their own use of corpora would place on the database.