[Respiration. Extract from the first "Cultural Dictionary in the French Language" from Robert's Dictionary].
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History, 15th Century↗
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Results for “DICTIONARIES”
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Dictionary learning algorithms for sparse representation.
Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial "25 words or less"), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations. Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries; that is, images encoded with an overcomplete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error).
Algorithms↗
High-recall protein entity recognition using a dictionary.
SUMMARY: Protein name extraction is an important step in mining biological literature. We describe two new methods for this task: semiCRFs and dictionary HMMs. SemiCRFs are a recently-proposed extension to conditional random fields (CRFs) that enables more effective use of dictionary information as features. Dictionary HMMs are a technique in which a dictionary is converted to a large HMM that recognizes phrases from the dictionary, as well as variations of these phrases. Standard training methods for HMMs can be used to learn which variants should be recognized. We compared the performance of our new approaches with that of Maximum Entropy (MaxEnt) and normal CRFs on three datasets, and improvement was obtained for all four methods over the best published results for two of the datasets. CRFs and semiCRFs achieved the highest overall performance according to the widely-used F-measure, while the dictionary HMMs performed the best at finding entities that actually appear in the dictionary-the measure of most interest in our intended application. AVAILABILITY: Dictionary HMMs were implemented in Java. Algorithms are available through an information extraction package MINORTHIRD on http://minorthird.sourceforge.net
Algorithms↗
Medical and dermatology dictionaries: an examination of unstructured definitions and a proposal for the future.
Medical dictionaries serve to describe and clarify the term set used by medical professionals. In this commentary, we analyze a representative set of skin disease definitions from 2 prominent medical dictionaries, Stedman's Medical Dictionary and Dorland's Illustrated Medical Dictionary. We find that there is an apparent lack of stylistic standards with regard to content and form. We advocate a new standard form for the definition of medical terminology, a standard to complement the easy-to-read yet unstructured style of the traditional dictionary entry. This new form offers a reproducible structure, paving the way for the development of a computer readable "dictionary" of medical terminology. Such a dictionary offers immediate update capability and a fundamental improvement in the ability to search for relationships between terms.
Dermatology↗
[Classification of nucleotide sequences over their frequency dictionaries reveals a relation between the structure of sequences and taxonomy of their bearers].
Classification of 16S RNA sequences over their frequency dictionaries, both real ones, and transformed ones was studied. Two entities were considered to be close each other from the point of view of their structure, if their frequency dictionaries were close, in Eucledian metric. A transformation procedure of a frequency dictionary has been implemented that reveals the peculiarities of information structure of a nucleotide sequence. A comparative study of two classification developed over the real frequency dictionary vs. that one developed over the transformed frequency dictionary was carried out. The strong correlation is revealed between the classification and the taxonomy of 16S RNA bearer. For the classes isolated, the information valuable words were identified. These words are the main factors of a difference between the classes. The frequency dictionaries containing the words of the length 3 exhibit the best correlation between a class and a genus. A genus, as a rule, is included into the same class, and the exclusion are sporadic. A development of hierarchy classification over the transformed frequency dictionaries separated one or two taxonomy groups, as each stage of classification. The unexpectedly frequent, or contrary, unexpectedly rare occurred of words (of the length 3) in entities under consideration make the structure difference between the classes of the nucleotide sequences.
Bacteria↗
Reconstructing ancestral haplotypes with a dictionary model.
We propose a dictionary model for haplotypes. According to the model, a haplotype is constructed by randomly concatenating haplotype segments from a given dictionary of segments. A haplotype block is defined as a set of haplotype segments that begin and end with the same pair of markers. In this framework, haplotype blocks can overlap, and the model provides a setting for testing the accuracy of simpler models invoking only nonoverlapping blocks. Each haplotype segment in a dictionary has an assigned probability and alternate spellings that account for genotyping errors and mutation. The model also allows for missing data, unphased genotypes, and prior distribution of parameters. Likelihood evaluations rely on forward and backward recurrences similar to the ones encountered in hidden Markov models. Parameter estimation is carried out with an EM algorithm. The search for the optimal dictionary is particularly difficult because of the variable dimension of the model space. We define a minimum description length criteria to evaluate each dictionary and use a combination of greedy search and careful initialization to select a best dictionary for a given dataset. Application of the model to simulated data gives encouraging results. In a real dataset, we are able to reconstruct a parsimonious dictionary that captures patterns of linkage disequilibrium well.
Algorithms↗
Automated access to a large medical dictionary: online assistance for research and application in natural language processing.
Online dictionaries can be important tools for research and application in natural language processing. This paper describes work with a machine-readable version of "Dorland's Illustrated Medical Dictionary". First the characteristics of the dictionary are briefly described, and then the complex process of converting the tape to an online interactive dictionary is discussed. The results of several experiments in automatically deriving information from the online dictionary are presented, and the paper ends with a discussion of the use of the online dictionary as a tool in the development of a natural language processing system designed for the biomedical domain.
Abstracting and Indexing↗
Can we classify medical data dictionaries?
Medical Data Dictionaries enable a clinical information system to maintain a controlled vocabulary, to store descriptive knowledge about terms, to map between those terms and from those terms to external classifications. They support a variety of functions in the information system, ranging from structured documentation to knowledgebased functions. This paper derives a multi-axial classification for medical data dictionaries. Dictionaries are classified along 4 axes, a vocabulary axis defining vocabulary properties, an application axis which characterises the degree of linkage between dictionary and information system, a semantic axis defining the quality of inter-term relationships and finally a language axis which classifies rules for inter-term relationships in semiotic theory. As an example two existing dictionaries are classified in the model and reference is taken to the design of future dictionaries.
Artificial Intelligence↗
The Portable Dictionary of the Mouse Genome: a personal database for gene mapping and molecular biology.
The Portable Dictionary of the Mouse Genome is a database for personal computers that contains information on approximately 10,000 loci in the mouse, along with data on homologs in several other mammalian species, including human, rat, cat, cow, and pig. Key features of the dictionary are its compact size, its network independence, and the ability to convert the entire dictionary to a wide variety of common application programs. Another significant feature is the integration of DNA sequence accession data. Loci in the dictionary can be rapidly resorted by chromosomal position, by type, by human homology, or by gene effect. The dictionary provides an accessible, easily manipulated set of data that has many uses--from a quick review of loci and gene nomenclature to the design of experiments and analysis of results. The Portable Dictionary is available in several formats suitable for conversion to different programs and computer systems. It can be obtained on disk or from Internet Gopher servers (mickey.utmen.edu or anat4.utmen.edu), an anonymous FTP site (nb.utmem.edu in the directory pub/genedict), and a World Wide Web server (http://mickey.utmem.edu/front.html).
Animals↗
Evaluation of techniques for increasing recall in a dictionary approach to gene and protein name identification.
Gene and protein name identification in text requires a dictionary approach to relate synonyms to the same gene or protein, and to link names to external databases. However, existing dictionaries are incomplete. We investigate two complementary methods for automatic generation of a comprehensive dictionary: combination of information from existing gene and protein databases and rule-based generation of spelling variations. Both methods have been reported in literature before, but have hitherto not been combined and evaluated systematically. We combined gene and protein names from several existing databases of four different organisms. The combined dictionaries showed a substantial increase in recall on three different test sets, as compared to any single database. Application of 23 spelling variation rules to the combined dictionaries further increased recall. However, many rules appeared to have no effect and some appear to have a detrimental effect on precision.
Abstracting and Indexing↗
An object-oriented design for automated navigation of semantic networks inside a medical data dictionary.
In this paper we present a data dictionary server for the automated navigation of information sources. The underlying knowledge is represented within a medical data dictionary. The mapping between medical terms and information sources is based on a semantic network. The key aspect of implementing the dictionary server is how to represent the semantic network in a way that is easier to navigate and to operate, i.e. how to abstract the semantic network and to represent it in memory for various operations. This paper describes an object-oriented design based on Java that represents the semantic network in terms of a group of objects. A node and its relationships to its neighbors are encapsulated in one object. Based on such a representation model, several operations have been implemented. They comprise the extraction of parts of the semantic network which can be reached from a given node as well as finding all paths between a start node and a predefined destination node. This solution is independent of any given layout of the semantic structure. Therefore the module, called Giessen Data Dictionary Server can act independent of a specific clinical information system. The dictionary server will be used to present clinical information, e.g. treatment guidelines or drug information sources to the clinician in an appropriate working context. The server is invoked from clinical documentation applications which contain an infobutton. Automated navigation will guide the user to all the information relevant to her/his topic, which is currently available inside our closed clinical network.
Automation↗
A dictionary-based approach for gene annotation.
This paper describes a fast and fully automated dictionary-based approach to gene annotation and exon prediction. Two dictionaries are constructed, one from the nonredundant protein OWL database and the other from the dbEST database. These dictionaries are used to obtain O (1) time lookups of tuples in the dictionaries (4 tuples for the OWL database and 11 tuples for the dbEST database). These tuples can be used to rapidly find the longest matches at every position in an input sequence to the database sequences. Such matches provide very useful information pertaining to locating common segments between exons, alternative splice sites, and frequency data of long tuples for statistical purposes. These dictionaries also provide the basis for both homology determination, and statistical approaches to exon prediction.
Alternative Splicing↗
Fast search for best representations in multitree dictionaries.
We address the best basis problem--or, more generally, the best representation problem: Given a signal, a dictionary of representations, and an additive cost function, the aim is to select the representation from the dictionary which minimizes the cost for the given signal. We develop a new framework of multitree dictionaries, which includes some previously proposed dictionaries as special cases. We show how to efficiently find the best representation in a multitree dictionary using a recursive tree-pruning algorithm. We illustrate our framework through several examples, including a novel block image coder, which significantly outperforms both the standard JPEG and quadtree-based methods and is comparable to embedded coders such as JPEG2000 and SPIHT.
Algorithms↗
A dictionary server for supplying context sensitive medical knowledge.
The Giessen Data Dictionary Server (GDDS), developed at Giessen University Hospital, integrates clinical systems with on-line, context sensitive medical knowledge to help with making medical decisions. By "context" we mean the clinical information that is being presented at the moment the information need is occurring. The dictionary server makes use of a semantic network supported by a medical data dictionary to link terms from clinical applications to their proper information sources. It has been designed to analyze the network structure itself instead of knowing the layout of the semantic net in advance. This enables us to map appropriate information sources to various clinical applications, such as nursing documentation, drug prescription and cancer follow up systems. This paper describes the function of the dictionary server and shows how the knowledge stored in the semantic network is used in the dictionary service.
Decision Support Systems, Clinical↗
[The first Croatian medical dictionary of Dr. Ivan Dezman (on the 125th anniversary of publication)].
The year 1868 marked the publication of the first "Dictionary of Medical Terms" in the Croatian language, the work of the physician and writer Ivan Dezman (1841-1873) and one of the first professional dictionaries to appear in Croatia. In composing the dictionary Dezman made use of all the general Croatian language dictionaries which were available at the time, of the vernacular idioms as well as of the new words he coined himself. Although the dictionary was modest by volume, it comprised definitions for 3247 entries, its great value lied in the fact that is strongly influenced both the medical writing and future work on medical nomenclature.
Croatia↗
Data dictionaries at Giessen University Hospital: past--present--future.
The concept of maintaining a medical data dictionary as a HIS core component was fundamental for all HIS development phases since the mid eighties at Giessen University Hospital. Being influenced by an early experimental installation of the HELP hospital information system and its PTXT data dictionary, we kept this approach through a number of development cycles of our own hospital information system. While our first data dictionary implementation (GMDD) was still very close to the PTXT structure (polyhierarchical design with an eight level hierarchy), the second generation dictionary (MDD-GIPHARM) has already been designed using a more flexible semantic network model. GMDD was a mainframe development (realized on Tandem Computers) based on the Tandem Nonstop SQL RDBMS. The major clinical applications established on top of the GMDD were laboratory results review, diagnosis documentation and physician discharge summaries. The MDD-GIPHARM development was initiated on PC-basis as the core of a rheumatology departmental system using MS-Access and then further enhanced within a research project to build knowledge-based functions for drug therapy. A first set of such functions based on MDD-GIPHARM is in routine use since 1996. Our current focus is to enhance MDD-GIPHARM towards an application independent vocabulary server (GDDS), which may be used for a variety of applications with the intranet of Giessen University Hospital. In this paper the evolutionary development of those data dictionary concepts at Giessen University Hospital is illustrated and compared with international activities in the last decade.
Hospital Information Systems↗
A feature dictionary supporting a multi-domain medical knowledge base.
Because different terminology is used by physicians of different specialties in different locations to refer to the same feature (signs, symptoms, test results), it is essential that our knowledge development tools provide a means to access a common pool of terms. This paper discusses the design of an online medical dictionary that provides a solution to this problem for developers of multi-domain knowledge bases for MEDAS (Medical Emergency Decision Assistance System). Our Feature Dictionary supports phrase equivalents for features, feature interactions, feature classifications, and translations to the binary features generated by the expert during knowledge creation. It is also used in the conversion of a domain knowledge to the database used by the MEDAS inference diagnostic sessions. The Feature Dictionary also provides capabilities for complex queries across multiple domains using the supported relations. The Feature Dictionary supports three methods for feature representation: (1) for binary features, (2) for continuous valued features, and (3) for derived features.
Artificial Intelligence↗
Using dictionaries to study the mental lexicon.
The notion of a mental lexicon has its historical roots in practical reference dictionaries. The distributional analysis of dictionaries provides one means of investigating the structure of the mental lexicon. We review our earlier work with dictionaries, based on a three-way horserace model of lexical access and production, and then present the most recent results of our ongoing analysis of the Oxford English Dictionary, Second Edition on CD-ROM, which traces changes in productivity over time of the English suffixes -ment and -ity, both of which originate in French borrowings. Our results lead us to question the validity of automatic analogy from a set of existing words as the driving force behind morphological productivity.
CD-ROM↗