7016782
Adaptive Linking between Text and Photos Using Common Sense Reasoning
In a hypermedia authoring task, an author often wants to set up meaningful connections between different media, such as text and photographs. To facilitate this task, it is helpful to have a software agent dynamically adapt the presentation of a media database to the user's authoring activities, and look for opportunities for annotation and retrieval. However, potential connections are often missed because of differences in vocabulary or semantic connections that are "obvious" to people but that might not be explicit.ARIA (Annotation and Retrieval Integration Agent) is a software agent that acts an assistant to a user writing e-mail or Web pages. As the user types a story, it does continuous retrieval and ranking on a photo database. It can use descriptions in the story to semi-automatically annotate pictures. To improve the associations beyond simple keyword matching, we use natural language parsing techniques to extract important roles played by text, such as "who, what, where, when". Since many of the photos depict common everyday situations such as weddings or recitals, we use a common sense knowledge base, Open Mind, to fill in semantic gaps that might otherwise prevent successful associations.
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.
11465537
Visualizing the affective structure of a text document
This paper introduces an approach for graphically visualizing the affective structure of a text document. A document is first affectively analyzed using a unique textual affect sensing engine, which leverages commonsense knowledge to classify text more reliably and comprehensively than can be achieved with keyword spotting methods alone. Using this engine, sentences are annotated using six basic Ekman emotions. Colors used to represent each of these emotions are sequenced into a color bar, which represents the progression of affect through a text document. Smoothing techniques allow the user to vary the granularity of the affective structure being displayed on the color bar. The bar is hyperlinked in a way such that it can be used to easily navigate the document. A user evaluation demonstrates that the proposed method for visualizing and navigating a document's affective structure facilitates a user's within-document information foraging activity.
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.
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.
653966
Fan-Type Theorem for Long Cycles Containing a Specified Edge
In this paper, we prove that if G is 3-connected noncomplete graph of order n satisfying min{max{d(u),d(v)}:d(u,v)=2}=μ, then for each edge e, G has a cycle containing e of length at least min{n,2μ}, unless G is a spanning subgraph of Kμ + Kcn−μ or K3+(lKμ−2∪Ks), where n=l(μ−2)+s+3,1≤s≤μ−2.
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.
2276859
Dynamically Generating a Protein Entity Dictionary Using Online Resources
With the overwhelming amount of biological knowledge stored in free text, natural language processing (NLP) has received much attention recently to make the task of managing information recorded in free text more feasible. One requirement for most NLP systems is the ability to accurately recognize biological entity terms in free text and the ability to map these terms to corresponding records in databases. Such task is called biological named entity tagging. In this paper, we present a system that automatically constructs a protein entity dictionary, which contains gene or protein names associated with UniProt identifiers using online resources. The system can run periodically to always keep up-to-date with these online resources. Using online resources that were available on Dec. 25, 2004, we obtained 4,046,733 terms for 1,640,082 entities. The dictionary can be accessed from the following website: http://biocreative.ifsm.umbc.edu/biothesaurus/.
14433108
Using amino acid patterns to accurately predict translation initiation sites
The translation initiation site (TIS) prediction problem is about how to correctly identify TIS in mRNA, cDNA, or other types of genomic sequences. High prediction accuracy can be helpful in a better understanding of protein coding from nucleotide sequences. This is an important step in genomic analysis to determine protein coding from nucleotide sequences. In this paper, we present an in silico method to predict translation initiation sites in vertebrate cDNA or mRNA sequences. This method consists of three sequential steps as follows. In the first step, candidate features are generated using k-gram amino acid patterns. In the second step, a small number of top-ranked features are selected by an entropy-based algorithm. In the third step, a classification model is built to recognize true TISs by applying support vector machines or ensembles of decision trees to the selected features. We have tested our method on several independent data sets, including two public ones and our own extracted sequences. The experimental results achieved are better than those reported previously using the same data sets. Our high accuracy not only demonstrates the feasibility of our method, but also indicates that there might be "amino acid" patterns around TIS in cDNA and mRNA sequences.
18024196
Error-Sensitive Grading for Model Combination
Ensemble learning is a powerful learning approach that combines multiple classifiers to improve prediction accuracy. An important decision while using an ensemble of classifiers is to decide upon a way of combining the prediction of its base classifiers. In this paper, we introduce a novel grading-based algorithm for model combination, which uses cost-sensitive learning in building a meta-learner. This method distinguishes between the grading error of classifying an incorrect prediction as correct, and the other-way-round, and tries to assign appropriate costs to the two types of error in order to improve performance. We study issues in error-sensitive grading, and then with extensive experiments show the empirically effectiveness of this new method in comparison with representative meta-classification techniques.