Contextual Units Research Articles

How GIS can help address the uncertain geographic context problem in social science research

The uncertain geographic context problem (UGCoP), first articulated by Kwan (2012; The uncertain geographic context problem. Annals of the Association of American Geographers, 102 (5), 958–968), refers to the problem that findings about the effects of area-based contextual variables on individual behaviors or outcomes may be affected by how contextual units (e.g., neighborhoods) are geographically delineated and the extent to which these areal units deviate from the true geographic context. It is a significant methodological problem because it means that analytical results can differ for different delineations of contextual units even if everything else is the same. Drawing upon Kwan (2012) and recent social science studies (especially environmental health and neighborhood effects research), this article further elaborates on the nature of the UGCoP and explores how recent advances in geographical information system (GIS) and geospatial technologies can help address the problem. It discusses possible means for mitigating the UGCoP, especially with regard to the collection of detailed individual space–time data with global positioning systems, construction of individual activity spaces, and the use of qualitative and web-based GIS to capture people's activity locations and everyday experiences. It also discusses the challenges for future research that seeks to address the UGCoP.

The Uncertain Geographic Context Problem

Any study that examines the effects of area-based attributes on individual behaviors or outcomes faces another fundamental methodological problem besides the modifiable areal unit problem (MAUP). It is the problem that results about these effects can be affected by how contextual units or neighborhoods are geographically delineated and the extent to which these areal units deviate from the true geographic context. The problem arises because of the spatial uncertainty in the actual areas that exert the contextual influences under study and the temporal uncertainty in the timing and duration in which individuals experienced these contextual influences. Using neighborhood effects and environmental health research as a point of departure, this article clarifies the nature and sources of this problem, which is referred to as the uncertain geographic context problem (UGCoP). It highlights some of the inferential errors that the UGCoP might cause and discusses some means for mitigating the problem. It reviews recent studies to show that both contextual variables and research findings are sensitive to different delineations of contextual units. The article argues that the UGCoP is a problem as fundamental as the MAUP but is a different kind of problem. Future research needs to pay explicit attention to its potential confounding effects on research results and to methods for mitigating the problem.

Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition

This study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions, yielding larger clusters, and precise questions, yielding smaller ones. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.

Refining of segmental boundaries in speech waveforms using contextual-dependent models

A method and apparatus are provided for refining segmental boundaries in speech waveforms. Contextual acoustic feature similarities are used as a basis for clustering adjacent phoneme speech units, where each adjacent pair phoneme speech units include a segmental boundary. A refining model is trained for each cluster and used to refine boundaries of contextual phoneme speech units forming the clusters.

An Improved Greedy Search Algorithm for the Development of a Phonetically Rich Speech Corpus

An efficient way to develop large scale speech corpora is to collect phonetically rich ones that have high coverage of phonetic contextual units. The sentence set, usually called as the minimum set, should have small text size in order to reduce the collection cost. It can be selected by a greedy search algorithm from a large mother text corpus. With the inclusion of more and more phonetic contextual effects, the number of different phonetic contextual units increased dramatically, making the search not a trivial issue. In order to improve the search efficiency, we previously proposed a so-called least-to-most-ordered greedy search based on the conventional algorithms. This paper evaluated these algorithms in order to show their different characteristics. The experimental results showed that the least-to-most-ordered methods successfully achieved smaller objective sets at significantly less computation time, when compared with the conventional ones. This algorithm has already been applied to the development a number of speech corpora, including a large scale phonetically rich Chinese speech corpus ATRPTH which played an important role in developing our multi-language translation system.

Competing definitions of contextual environments

BackgroundThe growing interest in the effects of contextual environments on health outcomes has focused attention on the strengths and weaknesses of alternate contextual unit definitions for use in multilevel analysis. The present research examined three methods to define contextual units for a sample of children already enrolled in a respiratory health study. The Inclusive Equal Weights Method (M1) and Inclusive Sample Weighted Method (M2) defined communities using the boundaries of the census blocks that incorporated the residences of the CHS participants, except that the former estimated socio-demographic variables by averaging the census block data within each community, while the latter used weighted proportion of CHS participants per block. The Minimum Bounding Rectangle Method (M3) generated minimum bounding rectangles that included 95% of the CHS participants and produced estimates of census variables using the weighted proportion of each block within these rectangles. GIS was used to map the locations of study participants, define the boundaries of the communities where study participants reside, and compute estimates of socio-demographic variables. The sensitivity of census variable estimates to the choice of community boundaries and weights was assessed using standard tests of significance.ResultsThe estimates of contextual variables vary significantly depending on the choice of neighborhood boundaries and weights. The choice of boundaries therefore shapes the community profile and the relationships between its components (variables).ConclusionMultilevel analysis concerned with the effects of contextual environments on health requires careful consideration of what constitutes a contextual unit for a given study sample, because the alternate definitions may have differential impact on the results. The three alternative methods used in this research all carry some subjectivity, which is embedded in the decision as to what constitutes the boundaries of the communities. The Minimum Bounding Rectangle was preferred because it focused attention on the most frequently used spaces and it controlled potential aggregation problems. There is a need to further examine the validity of different methods proposed here. Given that no method is likely to capture the full complexity of human-environment interactions, we would need baseline data describing people's daily activity patterns along with expert knowledge of the area to evaluate our neighborhood units.

GLOBAL RULE INDUCTION FOR INFORMATION EXTRACTION

The ability to extract desired pieces of information from natural language texts is an important task with a growing number of potential applications. This paper presents a novel pattern rule induction learning system, GRID, which emphasizes the use of global feature distribution in all of the training instances in order to make better decision on rule induction. GRID uses chunks as contextual units instead of tokens, and incorporates features at lexical, syntactical and semantic levels simultaneously. The features chosen in GRID are general and they were applied successfully to both semi-structured text and free text. Our experimental results on some publicly available webpage corpora and MUC-4 test set indicate that our approach is effective.

Bayesian Inference for Heterogeneous Event Counts

This article presents an integrated set of Bayesian tools one can use to model heterogeneous event counts. While models for event count cross sections are now widely used, little has been written about how to model counts when contextual factors introduce heterogeneity. The author begins with a discussion of Bayesian cross-sectional count models and discusses an alternative model for counts with overdispersion. To illustrate the Bayesian framework, the author fits the model to the number of women’s rights cosponsorships for each member of the 83rd to 102nd House of Representatives. The model is generalized to allow for contextual heterogeneity. The hierarchical model allows one to explicitly model contextual factors and test alternative contextual explanations, even with a small number of contextual units. The author compares the estimates from this model with traditional approaches and discusses software one can use to easily implement these Bayesian models with little start-up cost.

Extraction and representation of contextual information for knowledge discovery in texts

This paper studies the role of lexical contextual relations for the problem of unsupervised knowledge discovery in full texts. Narrative texts have inherent structure dictated by language usage in generating them. We suggest that the relative distance of terms within a text gives sufficient information about its structure and its relevant content. Furthermore, this structure can be used to discover implicit knowledge embedded in the text, therefore serving as a good candidate to represent effectively the text content for knowledge elicitation tasks. We qualitatively demonstrate that a useful text structure and content can be systematically extracted by collocational lexical analysis without the need to encode any supplemental sources of knowledge. We present an algorithm that systematically extracts the most relevant facts in the texts and labels them by their overall theme, dictated by local contextual information. It exploits domain independent lexical frequencies and mutual information measures to find the relevant contextual units in the texts. We report results from experiments in a real-world textual database of psychiatric evaluation reports.

How the brain encodes the order of letters in a printed word: the SERIOL model and selective literature review.

This paper describes a novel theoretical framework of how the position of a letter within a string is encoded, the SERIOL model (sequential encoding regulated by inputs to oscillations within letter units). Letter order is represented by a temporal activation pattern across letter units, as is consistent with current theories of information coding based on the precise timing of neural spikes. The framework specifies how this pattern is invoked via an activation gradient that interacts with subthreshold oscillations and how it is decoded via contextual units that activate word units. Using mathematical modeling, this theoretical framework is shown to account for the experimental data from a wide variety of string-processing studies, including hemispheric asymmetries, the optimal viewing position, and positional priming effects.

An information-theoretic based model for large-scale contextual text processing

We propose a supervised systematic approach for knowledge extraction from verylarge sets of full texts, such as those retrieved by search engines over the world wide web. The proposed model is entirely data-driven and relies only on the actual content of the texts in the selected corpus. We suggest that the relative distance of terms within a text gives sufficient information about its structure and its relevant content, therefore serving as a good candidate to represent effectively the text content for knowledge elicitation tasks. We qualitatively demonstrate that a useful text structure and content can be systematically extracted by collocational lexical analysis without the need to encode any supplemental sources of knowledge. We present an algorithm that systematically recognizes the most relevant facts in the texts and labels them by their overall theme, dictated by local contextual information. It exploits domain independent lexical frequencies and mutual information measures to find the relevant contextual units in the texts. We report results from experiments in a real-world textual database of psychiatric evaluation reports.

Training set design for connected speech recognition

Two methods for generating training sets for a speech recognition system are studied. The first uses a nondeterministic statistical method to generate a uniform distribution of sentences from a finite state machine (FSM) represented in digraph form. The second method, a deterministic heuristic approach, takes into consideration the importance of word ordering to address the problem of coarticulation effects. The two methods are critically compared. The first algorithm, referred to as MARKOV, converts the FSM into a first-order Markov model. The digraphs are determined, transitive closure computed, transition probabilities are assigned, and stopping criteria established. An efficient algorithm for computing these parameters is described. Statistical tests are conducted to verify performance and demonstrate its utility. A second algorithm for generating training sentences, referred to as BIGRAM, uses heuristics to satisfy three requirements: adequate coverage of basic speech (subword) units; adequate coverage of words in the recognition vocabulary (intraword contextual units); and adequate coverage of word pairs bigrams (interword contextual units).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Academic stratification and functional theory

This paper examines empirically a set of propositions on organizational stratification drawn from functional theory. The data consist of 38 departments of a large university which are treated as contextual units, as well as the responses of individual faculty to a survey. The findings present an interesting theoretical paradox: (1) functional arguments do not appear adequate to account for differences in income stratification among departments, but (2) they do explain participants' beliefs and judgments about the legitimacy of organizational reward systems. The implications are discussed, and a line of inquiry is suggested that would focus on the problem of establishing legitimacy with the environment(s) as a major source of organizational structure. It is suggested that organizational features act as symbolic devices that inform others that a given organization is a competent member of its set. In short, it is conforming to agreed upon definitions of the appropriate—most “rational”—structure for thattype of organization.

Permanence and change in memory II. Reproduction of words in sentences

Abstract.— A retroactive interference paradigm was employed for studying qualitative memory changes. Sentences, containing an ambiguous word that was defined differently by succeeding words, were used as stimulus material. Such retroactive definitions of meaning were found to lead to qualitative changes in reproduction. It is suggested that the issue of permanence and change in memory may be related to the choice of lists of independent items or contextual units as stimulus material. A contextual theory of memory, implying the conception of memory in a stream, is advocated.