Writing intended to inform frequently contains references to document entities (DEs), a mixed class that includes orthographically structured items (e.g., illustrations, sections, lists) and discourse entities (arguments, suggestions, points).Such references are vital to the interpretation of documents, but they often eschew identifiers such as "Figure 1" for inexplicit phrases like "in this figure " or "from these premises".We examine inexplicit references to DEs, termed DE references, and recast the problem of their automatic detection into the determination of relevant word senses.We then show the feasibility of machine learning for the detection of DErelevant word senses, using a corpus of human-labeled synsets from WordNet.We test cross-domain performance by gathering lemmas and synsets from three corpora: website privacy policies, Wikipedia articles, and Wikibooks textbooks.Identifying DE references will enable language technologies to use the information encoded by them, permitting the automatic generation of finely-tuned descriptions of DEs and the presentation of richly-structured information to readers.

Robots have a long history as a tangible platform through which designers and artists can explore human and social experiences. From Pierre Jaquet-Doz's Automatons from the Eighteenth Century, to Dunne & Raby's technological dreams of non-anthropomorphic robots that assist our lives [1], robots have been a rich form of technology that artists and designers have used to explore the human condition and how we relate to technology. This studio will give an introduction to small programmable robots and participants will learn how to use them to develop collaborative performances. Following an introduction, participants will be given the choice to work with bespoke robots (arduino based) or an e-puck [2] robot, participants will work in small groups to develop behaviours that, infer a form of robot dance. For a dance based upon a twitter feed or perform a collective task that assists a human. The studio will culminate in a series of performances by the bots that explore the potential for robots as materials for designing with data.

In this article we discuss several metrics of coherence defined using centering theory and investigate the usefulness of such metrics for information ordering in automatic text generation. We estimate empirically which is the most promising metric and how useful this metric is using a general methodology applied on several corpora. Our main result is that the simplest metric (which relies exclusively on NOCB transitions) sets a robust baseline that cannot be outperformed by other metrics which make use of additional centering-based features. This baseline can be used for the development of both text-to-text and concept-to-text generation systems.

Being able to automatically perceive a variety of emotions from text alone has potentially important applications in CMC and HCI that range from identifying mood from online posts to enabling dynamically adaptive interfaces. However, such ability has not been proven in human raters or computational systems. Here we examine the ability of naive raters of emotion to detect one of eight emotional categories from 50 and 200 word samples of real blog text. Using expert raters as a 'gold standard', naive-expert rater agreement increased with longer texts, and was high for ratings of joy, disgust, anger and anticipation, but low for acceptance and 'neutral' texts. We discuss these findings in light of theories of CMC and potential applications in HCI.

Emotion is central to human interactions, and automatic detection could enhance our experience with technologies. We investigate the linguistic expression of fine-grained emotion in 50 and 200 word samples of real blog texts previously coded by expert and naive raters. Content analysis (LIWC) reveals angry authors use more affective language and negative affect words, and that joyful authors use more positive affect words. Additionally, a co-occurrence semantic space approach (LSA) was able to identify fear (which naive human emotion raters could not do). We relate our findings to human emotion perception and note potential computational applications.

Embodied Conversational Agents (ECAs) are widely used to assist users in carrying out tasks. There are various reasons for including them in interfaces; they may help guide a user's attention to important information; they may improve overall task performance; or they may simply make the interface more attractive, thereby increasing the user's motivation to engage with a task.

Generating referring expressions is a task that has received a great deal of attention in the natural-language generation community, with an increasing amount of recent effort targeted at the generation of multimodal referring expressions. However, most implemented systems tend to assume very little shared knowledge between the speaker and the hearer, and therefore must generate fully-elaborated linguistic references. Some systems do include a representation of the physical context or the dialogue context; however, other sources of contextual information are not normally used. Also, the generated references normally consist only of language and, possibly, deictic pointing gestures.

We present the source authoring facilities of a natural language generation system that produces personalised descriptions of objects in multiple natural languages starting from language-independent symbolic information in ontologies and databases as well as pieces of canned text. The system has been tested in applications ranging from museum exhibitions to presentations of computer equipment for sale. We discuss the architecture of the overall system, the resources that the authors manipulate, the functionality of the authoring facilities, the system's personalisation mechanisms, and how they relate to source authoring. A usability evaluation of the authoring facilities is also presented, followed by more recent work on reusing information extracted from existing databases and documents, and supporting the OWL ontology specification language.

We use a reliably annotated corpus to compare metrics of coherence based on Centering Theory with respect to their potential usefulness for text structuring in natural language generation. Previous corpus-based evaluations of the coherence of text according to Centering did not compare the coherence of the chosen text structure with that of the possible alternatives. A corpus-based methodology is presented which distinguishes between Centering-based metrics taking these alternatives into account, and represents therefore a more appropriate way to evaluate Centering from a text structuring perspective.

Generating text in a hypermedia environment places different demands on a text generation system than occurs in non-interactive environments. This paper describes some of these demands, then shows how the architecture of one text generation system, ILEX, has been shaped by them. The architecture is described in terms of the levels of linguistic representation used, and the processes which map between them. Particular attention is paid to the processes of content selection and text structuring.

This paper outlines a text generation system suited to a large class of information sources, relational databases. We focus on one aspect of the problem: the additional information which needs to be specified to produce reasonable text quality when generating from relational databases. We outline how databases need to be prepared, and then describe various types of domain semantics which can be used to improve text quality.

We will demonstrate the ILEX system, a system which dynamically generates descriptions of database objects for the web, adapting the description to the discourse context and user type. Among other improvements in version 3, the system now generates from relational databases, and this demonstration will focus on this ability. We will also show how incremental extensions to the domain semantics improve the quality of the text produced.

This paper describes the latest version of the SOLE concept-tospeech system, which uses linguistic information provided by a natural language generation system to improve the prosody of synthetic speech. We discuss the types of linguistic information that prove most useful and the implications for text-to-speech systems. 1. INTRODUCTION The purpose of the SOLE project is to make use of automaticallygenerated, high-level linguistic information to improve the quality of the intonation of synthetic speech. After choosing an initial set of linguistic constructs thought to have some influence on prosody, we developed an SGML-based mark-up language to serve as a general interface between NLG and speech synthesis systems, and trained our synthesis system to recognise correlations between the mark-up and intonational contours so that it can make use of this mark-up when synthesising. As a result, many of the errors that the synthesiser makes with regard to knowing when to accent or deaccent ...

We discuss a task requiring the coherent presentation of heterogeneous information about objects recorded in electronic catalogues. We consider the advantages of combining hypermedia delivery with natural language generation technology, so as to allow us to view a session with such a system as a coherent conversation or dialogue. We describe two prototype systems we have built which make use of these combined techniques, and focus on those aspects of the systems which attempt to provide coherence. Although the techniques themselves are not novel, their combination is relatively recent, and promises to help forge useful tools for accomplishing our specific information retrieval task.

Dynamic hypertext' is hypertext which is automatically generated at the point of need.A number of NLG systems have now been developed to operate within a hypertext environment; and now that these systems are becoming widely available on the World Wide Web, it is useful to take stock of how well-equipped NLG technology is to work in this new domain.A generation system in a hypertext environment faces a specific set of requirements; here, we discuss those requirements, and the resources that can be provided to help meet them.Examples are drawn from a number of systems, including our own prototype, ILEX-0.We conclude by indicating that the major benefit of such systems could be in the way that they combine flexibility with the illusion of user control.

We investigate various contextual effects on text interpretation, and account for them by providing contextual constraints in a logical theory of text interpretation. On the basis of the way these constraints interact with the other knowledge sources, we draw some general conclusions about the role of domain-specific information, top-down and bottom-up discourse information flow, and the usefulness of formalisation in discourse theory.