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.
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.
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.
36964410
A Knowledge Object Oriented System for High Throughput Collection and Analysis of Data
23957657
Terminology model discovery using natural language processing and visualization techniques
Medical terminologies are important for unambiguous encoding and exchange of clinical information. The traditional manual method of developing terminology models is time-consuming and limited in the number of phrases that a human developer can examine. In this paper, we present an automated method for developing medical terminology models based on natural language processing (NLP) and information visualization techniques. Surgical pathology reports were selected as the testing corpus for developing a pathology procedure terminology model. The use of a general NLP processor for the medical domain, MedLEE, provides an automated method for acquiring semantic structures from a free text corpus and sheds light on a new high-throughput method of medical terminology model development. The use of an information visualization technique supports the summarization and visualization of the large quantity of semantic structures generated from medical documents. We believe that a general method based on NLP and information visualization will facilitate the modeling of medical terminologies.
52857898
Schema Discovery for Semistructured Data
To formulate a meaningful query on semistructured data, such as on the Web, that matches some of the source's structure, we need first to discover something about how the information is represented in the source. This is referred to as schema discovery and was considered for a single object recently. In the case of multiple objects, the task of schema discovery is to identify typical structuring information of those objects as a whole. We motivate the schema discovery in this general setting and propose a framework and algorithm for it. We apply the framework to a real Web database, the Internet Movies Database, to discover typical schema of most voted movies.
2045948
Compact Dual Ensembles for Active Learning
Generic ensemble methods can achieve excellent learning performance, but are not good candidates for active learning because of their different design purposes. We investigate how to use diversity of the member classifiers of an ensemble for efficient active learning. We empirically show, using benchmark data sets, that (1) to achieve a good (stable) ensemble, the number of classifiers needed in the ensemble varies for different data sets; (2) feature selection can be applied for classifier selection from ensembles to construct compact ensembles with high performance. Benchmark data sets and a real-world application are used to demonstrate the effectiveness of the proposed approach.
29257622
Use of Built-in Features in the Interpretation of High-dimensional Cancer Diagnosis Data
Cancer diagnosis data, for example microarray gene expression profiling data and proteomic profiling data, are often described by thousands of features. To computationally make a diagnosis for new samples, these data are usually input to a learning algorithm, the algorithm then induces a classifier, the classifier then predicts a class label for any test sample. As the data is so high-dimensional, most of the resulting classifiers are very complicated particularly those based on kernel-functions such as support vector machines---the interpretation of the decision results must need all the features to be involved. In this paper, we discuss built-in features and use them to concisely characterize the data and to easily interpret the decisions. Built-in features are features that are used only in the classifiers, and that are only a small subset of the original features, e.g., the features in a decision tree. So, the notion of built-in features is different from input features and also from original features. As there is a significant reduction from the huge size of original features to a small number of relevant features, the complexity of the interpretation can be much eased. The use of built-in features also provides much potential for elucidating the translation between raw data and clinically useful knowledge. In this paper, we also report that the performance of classifiers using built-in features tends to remain stable even input feature space changes, but other types of classifiers fluctuate their performance. So, once again, we promote the use of classifiers that use built-in features since the algorithms can avoid the existing hard problem of selecting best number of features for learning.