15034094
Rendering Aesthetic Impressions of Text in Color Space
What is an artwork and how could a machine become artist? This paper addresses the provocative question by theorizing a computational model of aesthetics and implementing the Aesthetiscope|a computer program that portrays aesthetic impressions of text and renders an abstract color grid artwork reminiscent of early twentieth century abstract expressionism. Following Dewey’s psychological interpretation of \aesthetic" and Jung’s ontology of fundamental psychological functions, we theorize that a viewer flnds an artwork moving and satisfying because it seduces her into rich evocations of thoughts, sensations, intuitions, and feelings. The Aesthetiscope embodies this theory and aims to generate color grids paired with inspiration texts (a word, a poem, or song lyrics), which can be received as aesthetic and artistic by a viewer. The paper describes flve Jungian aesthetic readers which are together capable of creative narrative understanding, and three color-logics that employ psycho-semantic principles to render the aesthetic readings in color space. Evaluations of the Aesthetiscope revealed that the program is best at portraying intuition and feeling, and that overall, the Aesthetiscope is capable of creating the aesthetic of art based on an inspiration text in a non-arbitrary way.
6190656
Toward a Programmatic Semantics of Natural Language
Natural language is imbued with a rich semantics but unfortunately its complex elegance is often mistaken for mere imprecision. Because complete parsers of English are not yet achievable, people assume that it is not feasible to use English directly as a means of instructing computers. However, in this paper, we show that English descriptions of procedures often contain programmatic semantics-linguistic features that can be easily mapped into programming language constructs. Some linguistic features can even inspire new ways of thinking about specifying programs. Far from being hopelessly ambiguous, natural languages exhibit important principles of communication that could be used to make human-computer communication more natural
38681572
Programmatic semantics for natural language interfaces
An important way of making interfaces usable by non-expert users is to enable the use of natural language input, as in natural language query interfaces to databases, or MUDs and MOOs. When the subject matter is about procedures, however, we have discovered that interfaces can take advantage of what we call Programmatic Semantics, procedural relations that can be inferred from the linguistic structure. Roughly, nouns can be interpreted as data structures; verbs are functions; adjectives are properties. Some linguistic forms imply conditionals, loops, and recursive structures.We illustrate the principles of Programmatic Semantics with a description of Metafor, a "brainstorming" editor for programs, analogous to an outlining tool for prose writing. Metafor interactively converts English sentences to partially specified program code, to be used as "scaffolding" for a more detailed program. A user study showed that Metafor is capable of capturing enough Programmatic Semantics to facilitate non-programming users and beginners' conceptualization of programming problems.
6157335
Beating Common Sense into Interactive Applications
A long-standing dream of artificial intelligence has been to put commonsense knowledge into computers -- enabling machines to reason about everyday life. Some projects, such as Cyc, have begun to amass large collections of such knowledge. However, it is widely assumed that the use of common sense in interactive applications will remain impractical for years, until these collections can be considered sufficiently complete and commonsense reasoning sufficiently robust. Recently, at the Massachusetts Institute of Technology's Media Laboratory, we have had some success in applying commonsense knowledge in a number of intelligent interface agents, despite the admittedly spotty coverage and unreliable inference of today's commonsense knowledge systems. This article surveys several of these applications and reflects on interface design principles that enable successful use of commonsense knowledge.
2204750
A model of textual affect sensing using real-world knowledge
This paper presents a novel way for assessing the affective qualities of natural language and a scenario for its use. Previous approaches to textual affect sensing have employed keyword spotting, lexical affinity, statistical methods, and hand-crafted models. This paper demonstrates a new approach, using large-scale real-world knowledge about the inherent affective nature of everyday situations (such as "getting into a car accident") to classify sentences into "basic" emotion categories. This commonsense approach has new robustness implications.Open Mind Commonsense was used as a real world corpus of 400,000 facts about the everyday world. Four linguistic models are combined for robustness as a society of commonsense-based affect recognition. These models cooperate and compete to classify the affect of text. Such a system that analyzes affective qualities sentence by sentence is of practical value when people want to evaluate the text they are writing. As such, the system is tested in an email writing application. The results suggest that the approach is robust enough to enable plausible affective text user interfaces.
5882174
Active learning for detecting a spectrally variable subject in color infrared imagery
To classify Egeria densa, Brazilian waterweed, in scan-digitized color infrared aerial photographs, we are developing an interactive computer system based on data-mining techniques with active learning capabilities. Key components of the system are: feature extraction, automatic classification, active learning, and experimental evaluation.
13024009
Building effective defect-prediction models in practice
Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction. Effective defect prediction models can help developers focus quality assurance activities on defect-prone modules and thus improve software quality by using resources more efficiently. These models often use static measures obtained from source code, mainly size, coupling, cohesion, inheritance, and complexity measures, which have been associated with risk factors, such as defects and changes.
9357291
A Balanced Ensemble Approach to Weighting Classifiers for Text Classification
This paper studies the problem of constructing an effective heterogeneous ensemble classifier for text classification. One major challenge of this problem is to formulate a good combination function, which combines the decisions of the individual classifiers in the ensemble. We show that the classification performance is affected by three weight components and they should be included in deriving an effective combination function. They are: (1) Global effectiveness, which measures the effectiveness of a member classifier in classifying a set of unseen documents; (2) Local effectiveness, which measures the effectiveness of a member classifier in classifying the particular domain of an unseen document; and (3) Decision confidence, which describes how confident a classifier is when making a decision when classifying a specific unseen document. We propose a new balanced combination function, called dynamic classifier weighting (DCW), that incorporates the aforementioned three components. The empirical study demonstrates that the new combination function is highly effective for text classification.
17605363
Handling concept drifts in incremental learning with support vector machines
With the increase in the size of real-world databases, there is an ever-increasing need to scale up inductive learning algorithms. Incremental learning techniques are one possible solution to the scalability problem. In this paper, we propose three ctiteria to evaluate the robustness and reliability of incremental learning methods, and use them to study the robustness of an incremental training method for Support Vector Machines. We provide empirical results using benchmark machine learning datasets to show that support vectors form a svccdnct and suficient set for block-by-block incremental learning.
30030780
Headwords and Suffixes in Biomedical Names
Natural Language Processing (NLP) techniques have been used for the task of extracting and mining knowledge from biomedical literature. One of the critical steps of such a task is biomedical named entity tagging (BNER) which usually contains two steps: the first step is the identification of biomedical names in text and the second is the assignment of semantic classes predefined to names identified by the first step. Headwords and suffixes have been used frequently by BNER systems as features for the assignment of semantic classes to names in text. However, there are few studies to evaluate the performance of headwords and suffixes in predicting semantic classes of biomedical terms utilizing knowledge sources in an unsupervised way. We conducted a study to evaluate the performance of headwords and suffixes using names in the Unified Medical Language System (UMLS) where the semantic classes associated with these names were obtained by modifying an existing UMLS semantic group system and incorporating the GENIA ontology. We define headwords and suffixes that are significantly associated with a specific semantic class as semantic suffixes. The performance of semantic assignment using semantic suffixes achieved an F-measure of 86.4% with a precision of 91.6% and a recall of 81.7%. When applying these semantic suffixes obtained using the UMLS to names extracted from the GENIA corpus, the system achieved an F-measure of 73.4% with a precision of 84.2% and a recall of 65.1% where these performance measures could be improved dramatically when limited to names associated with classes that have the corresponding GENIA types.