There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47 on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56 on the 374 analogy questions, statistically equivalent to the average human score of 57 . On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM.
Metaphorical language is very common in our daily life; so common that we are usually unaware of it @cite_46 . gentner01 argue that novel metaphors are understood using analogy, but conventional metaphors are simply recalled from memory. A conventional metaphor is a metaphor that has become entrenched in our language @cite_46 . dolan95 describes an algorithm that can recognize conventional metaphors, but is not suited to novel metaphors. This suggests that it may be fruitful to combine Dolan's algorithm for handling conventional metaphorical language with LRA and SME for handling novel metaphors.
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CorMet is a corpus-based system for discovering metaphorical mappings between concepts. It does this by finding systematic variations in domain-specific selectional preferences, which are inferred from large, dynamically mined Internet corpora.Metaphors transfer structure from a source domain to a target domain, making some concepts in the target domain metaphorically equivalent to concepts in the source domain. The verbs that select for a concept in the source domain tend to select for its metaphorical equivalent in the target domain. This regularity, detectable with a shallow linguistic analysis, is used to find the metaphorical interconcept mappings, which can then be used to infer the existence of higher-level conventional metaphors.Most other computational metaphor systems use small, hand-coded semantic knowledge bases and work on a few examples. Although CorMet's only knowledge base is WordNet (Fellbaum 1998) it can find the mappings constituting many conventional metaphors and in some cases recognize sentences instantiating those mappings. CorMet is tested on its ability to find a subset of the Master Metaphor List (Lakoff, Espenson, and Schwartz 1991). This article explores the application of metaphors in news headlines with a view to interrogating their potential for coercion. Coercion in news discourse is understood as a strategic deployment of pragma-linguistic devices, including metaphors, to foreground the representations of socio-political reality that are compatible with the interests of the news outlet rather than those that inform public debate. It is argued that coercion can be exposed through systematic discourse analysis. Methodologically, the study aims to integrate the cognitive and pragmatic approaches to metaphor in regarding it as both a conceptual building block of news representations and a strategic framing device in news discourse. A quantitative and qualitative analysis of a sample of metaphors excerpted from a corpus of 400 most-read headlines from one of the most visited English-language newspaper sites The Daily Mail is conducted to illustrate such coercive applications of metaphor as simplification, imaging, animalization, confronta tion, (de)legitimization, emotionalization, and dramatization. In the course of the analysis it is demonstrated how certain ideologically-biased representations can be coerced through figurative language. People use metaphors every time they speak. Some of those metaphors are literary - devices for making thoughts more vivid or entertaining. But most are much more basic than that - they're "metaphors we live by", metaphors we use without even realizing we're using them. In this book, George Lakoff and Mark Johnson suggest that these basic metaphors not only affect the way we communicate ideas, but actually structure our perceptions and understandings from the beginning. Bringing together the perspectives of linguistics and philosophy, Lakoff and Johnson offer an intriguing and surprising guide to some of the most common metaphors and what they can tell us about the human mind. And for this new edition, they supply an afterword both extending their arguments and offering a fascinating overview of the current state of thinking on the subject of the metaphor. Conventional software systems, such as those based on the “desktop metaphor,” are ill-equipped to manage the electronic information and events of the typical computer user. We introduce a new metaphor, Lifestreams, for dynamically organizing a user's personal workspace. Lifestreams uses a simple organizational metaphor, a time-ordered stream of documents, as an underlying storage system. Stream filters are used to organize, monitor and summarize information for the user. Combined, they provide a system that subsumes many separate desktop applications. This paper describes the Lifestreams model and our prototype system. The reliable automated identification of metaphors still remains a challenge in metaphor research due to ambiguity between semantic and contextual interpretation of individual lexical items. In this article, we describe a novel approach to metaphor identification which is based on three intersecting methods: imageability, topic chaining, and semantic clustering. Our hypothesis is that metaphors are likely to use highly imageable words that do not generally have a topical or semantic association with the surrounding context. Our method is thus the following: (1) identify the highly imageable portions of a paragraph, using psycholinguistic measures of imageability, (2) exclude imageability peaks that are part of a topic chain, and (3) exclude imageability peaks that show a semantic relationship to the main topics. We are currently working towards fully automating this method for a number of languages. Humans have an uncanny ability to learn new concepts with very few examples. Cognitive theories have suggested that this is done by utilizing prior experience of related tasks. We propose to emulate this process in machines, by transforming new problems into old ones. These transformations are called metaphors. Obviously, the learner is not given a metaphor, but must acquire one through a learning process. We show that learning metaphors yield better results than existing transfer learning methods. Moreover, we argue that metaphors give a qualitative assessment of task relatedness. We show that it is possible to reliably discriminate whether a syntactic construction is meant literally or metaphorically using lexical semantic features of the words that participate in the construction. Our model is constructed using English resources, and we obtain state-of-the-art performance relative to previous work in this language. Using a model transfer approach by pivoting through a bilingual dictionary, we show our model can identify metaphoric expressions in other languages. We provide results on three new test sets in Spanish, Farsi, and Russian. The results support the hypothesis that metaphors are conceptual, rather than lexical, in nature. Over recent years the topic of technical debt has gained significant attention in the software engineering community. The area of technical debt research is somewhat peculiar within software engineering as it is built on a metaphor. This has certainly benefited the field as it helps to achieve a lot of attention and eases communication about the topic, however, it seems it is to some extent also sidetracking research work, if the metaphor is used beyond its range of applicability. In this paper, we focus on the limits of the metaphor and the problems that arise when over-extending its applicability. We do also aim at providing some additional insights by proposing certain ways of handling these restrictions. Computing metaphors have become an integral part of information systems design, yet they are deeply rooted in cultural practices. This paper presents an investigation of the cross-cultural use and usability of such metaphors by studying the library metaphor of digital libraries in the cultural context of the Maori, the indigenous population of New Zealand. The ethnographic study examines relevant features of the Maori culture, their form of knowledge transfer and their use of physical and digital libraries. On this basis, the paper points out why and when the library metaphor fails Maori and other indigenous users, and indicates how this knowledge can contribute to the improvement of future designs. Many AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for complex hand-coded representations. We introduce the Latent Relation Mapping Engine (LRME), which combines ideas from SME and Latent Relational Analysis (LRA) in order to remove the requirement for hand-coded representations. LRME builds analogical mappings between lists of words, using a large corpus of raw text to automatically discover the semantic relations among the words. We evaluate LRME on a set of twenty analogical mapping problems, ten based on scientific analogies and ten based on common metaphors. LRME achieves human-level performance on the twenty problems. We compare LRME with a variety of alternative approaches and find that they are not able to reach the same level of performance. We present a study exploring upper body 3D spatial interaction metaphors for control and communication with Unmanned Aerial Vehicles (UAV) such as the Parrot AR Drone. We discuss the design and implementation of five interaction techniques using the Microsoft Kinect, based on metaphors inspired by UAVs, to support a variety of flying operations a UAV can perform. Techniques include a first-person interaction metaphor where a user takes a pose like a winged aircraft, a game controller metaphor, where a user's hands mimic the control movements of console joysticks, "proxy" manipulation, where the user imagines manipulating the UAV as if it were in their grasp, and a pointing metaphor in which the user assumes the identity of a monarch and commands the UAV as such. We examine qualitative metrics such as perceived intuition, usability and satisfaction, among others. Our results indicate that novice users appreciate certain 3D spatial techniques over the smartphone application bundled with the AR Drone. We also discuss the trade-offs in the technique design metrics based on results from our study. Procedural content generation (PCG), the algorithmic creation of game content with limited or indirect user input, has much to offer to game design. In recent years, it has become a mainstay of game AI, with significant research being put towards the investigation of new PCG systems, algorithms, and techniques. But for PCG to be absorbed into the practice of game design, it must be contextualised within design-centric as opposed to AI or engineering perspectives. We therefore provide a set of design metaphors for understanding potential relationships between a designer and PCG. These metaphors are: tool, material, designer, and domain expert. By examining PCG through these metaphors, we gain the ability to articulate qualities, consequences, affordances, and limitations of existing PCG approaches in relation to design. These metaphors are intended both to aid designers in understanding and appropriating PCG for their own contexts, and to advance PCG research by highlighting the assumptions implicit in existing systems and discourse. Visual analytics emphasizes sensemaking of large, complex datasets through interactively exploring visualizations generated by statistical models. For example, dimensionality reduction methods use various similarity metrics to visualize textual document collections in a spatial metaphor, where similarities between documents are approximately represented through their relative spatial distances to each other in a 2D layout. This metaphor is designed to mimic analysts' mental models of the document collection and support their analytic processes, such as clustering similar documents together. However, in current methods, users must interact with such visualizations using controls external to the visual metaphor, such as sliders, menus, or text fields, to directly control underlying model parameters that they do not understand and that do not relate to their analytic process occurring within the visual metaphor. In this paper, we present the opportunity for a new design space for visual analytic interaction, called semantic interaction, which seeks to enable analysts to spatially interact with such models directly within the visual metaphor using interactions that derive from their analytic process, such as searching, highlighting, annotating, and repositioning documents. Further, we demonstrate how semantic interactions can be implemented using machine learning techniques in a visual analytic tool, called ForceSPIRE, for interactive analysis of textual data within a spatial visualization. Analysts can express their expert domain knowledge about the documents by simply moving them, which guides the underlying model to improve the overall layout, taking the user's feedback into account. Presentation and graphics software enables users to experiment with variations of illustrations. They can revisit recent editing operations using the ubiquitous undo command, but they are limited to sequential exploration. We propose a new interaction metaphor and visualization for operation history. While editing, a user can access a history mode in which actions are denoted by graphical depictions appearing on top of the document. Our work is inspired by the visual language of film storyboards and assembly instructions. Our storyboard provides an interactive visual history, summarizing the editing of a document or a selected object. Each view is composed of action depictions representing the user’s editing actions and enables the user to consider the operation history in context rather than in a disconnected list view. This metaphor provides instant access to any past action and we demonstrate that this is an intuitive interface to a selective undo mechanism. An intergenerational design team of children (ages 7-11 years old) along with graduate students and faculty in computer science and information studies developed a programming language for children, Toque. Concrete real-world cooking scenarios were used as programming metaphors to support an accessible programming learning experience. The Wiimote and Nunchuk were used as physical programming input devices. The programs that were created were pictorial recipes which dynamically controlled animations of an on-screen chef preparing virtual dishes in a graphical kitchen environment. Through multiple design sessions, programming strategies were explored, cooking metaphors were developed and, prototypes of the Toque environment were iterated. Results of these design experiences have shown us the importance of pair-programming, programming by storytelling, parallel programming, function-argument relationships, and the role of tangibility in overcoming challenges with constraints imposed by the system design. A key problem in software engineering is changing the code. We present a sequence of visualizations and visual metaphors designed to help engineers understand and manage the software change process. The principal metaphors are matrix views, cityscapes, bar and pie charts, data sheets and networks. Linked by selection mechanisms, multiple views are combined to form perspectives that both enable discovery of high-level structure in software change data and allow effective access to details of those data. Use of the views and perspectives is illustrated in two important contexts: understanding software change by exploration of software change data and management of software development. Our approach complements existing visualizations of software structure and software execution. We introduce a novel concurrent logic programming language, which we call LO, based on an extension of Horn logic. This language enhances the process view of objects implementable in Horn-based concurrent logic programming languages with powerful capabilities for knowledge structuring, leading to a flexible form of variable-structure inheritance. The main novelty about LO is a new kind of OR-concurrency which is dual to the usual AND-concurrency and provides us with the notion of structured process. Such OR-concurrency can be nicely characterized with a sociological metaphor as modelling the internal distribution of tasks inside a complex organization; this complements the external cooperation among different entities accounted for by AND-concurrency . Scientific visualization and illustration tools are designed to help people understand the structure and complexity of scientific data with images that are as informative and intuitive as possible. In this context the use of metaphors plays an important role since they make complex information easily accessible by using commonly known concepts. In this paper we propose a new metaphor, called "topological landscapes," which facilitates understanding the topological structure of scalar functions. The basic idea is to construct a terrain with the same topology as a given dataset and to display the terrain as an easily understood representation of the actual input data. In this projection from an n-dimensional scalar function to a two-dimensional (2D) model we preserve function values of critical points, the persistence (function span) of topological features, and one possible additional metric property (in our examples volume). By displaying this topologically equivalent landscape together with the original data we harness the natural human proficiency in understanding terrain topography and make complex topological information easily accessible. The Go-Go immersive interaction technique uses the metaphor of interactively growing the user’s arm and non-linear mapping for reaching and manipulating distant objects. Unlike others, our technique allows for seamless direct manipulation of both nearby objects and those at a distance. Besides making our thoughts more vivid and filling our communication with richer imagery, metaphor also plays an important structural role in our cognition. Although there is a consensus in the linguistics and NLP research communities that the phenomenon of metaphor is not restricted to similarity-based extensions of meanings of isolated words, but rather involves reconceptualization of a whole area of experience (target domain) in terms of another (source domain), there still has been no proposal for a comprehensive procedure for annotation of cross-domain mappings. However, a corpus annotated for conceptual mappings could provide a new starting point for both linguistic and cognitive experiments. The annotation scheme we present in this paper is a step towards filling this gap. We test our procedure in an experimental setting involving multiple annotators and estimate their agreement on the task. The associated corpus annotated for source target domain mappings will be publicly available.