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Semantics-Space-Time Cube. A Conceptual Framework for Systematic Analysis of Texts in Space and Time
We propose an approach to analyzing data in which texts are associated with spatial and temporal references with the aim to understand how the text semantics vary over space and time. To represent the semantics, we apply probabilistic topic modeling. After extracting a set of topics and representing the texts by vectors of topic weights, we aggregate the data into a data cube with the dimensions corresponding to the set of topics, the set of spatial locations (e.g., regions), and the time divided into suitable intervals according to the scale of the planned analysis. Each cube cell corresponds to a combination (topic, location, time interval) and contains aggregate measures characterizing the subset of the texts concerning this topic and having the spatial and temporal references within these location and interval. Based on this structure, we systematically describe the space of analysis tasks on exploring the interrelationships among the three heterogeneous information facets, semantics, space, and time. We introduce the operations of projecting and slicing the cube, which are used to decompose complex tasks into simpler subtasks. We then present a design of a visual analytics system intended to support these subtasks. To reduce the complexity of the user interface, we apply the principles of structural, visual, and operational uniformity while respecting the specific properties of each facet. The aggregated data are represented in three parallel views corresponding to the three facets and providing different complementary perspectives on the data. The views have similar look-and-feel to the extent allowed by the facet specifics. Uniform interactive operations applicable to any view support establishing links between the facets. The uniformity principle is also applied in supporting the projecting and slicing operations on the data cube. We evaluate the feasibility and utility of the approach by applying it in two analysis scenarios using geolocated social media data for studying people's reactions to social and natural events of different spatial and temporal scales
A processing framework for temporal analysis and its application to instructional texts
Temporal analysis is the task of determining the temporal structure of a given text.
Such a structure represents the order of the events and states mentioned in the text
on a time line. The main contribution of this thesis is in presenting a new processing
framework for temporal analysis.The framework is a computational one and has been implemented in a system called taste for the temporal analysis of instructional texts. In particular, taste has been successfully tested on nine cookery recipes. Amongst the more important ideas explored in this thesis are the following:
• We integrate qualitative information (as expressed by temporal connectives like
before and after) and quantitative information (as expressed in phrases like for
20 minutes and 20 minutes before) in a text into the temporal analysis framework. Previous work has only considered qualitative information but ignored the quantitative kind.
• We propose a new approach to the problem of integrating the current event or
state into the preceding discourse. This problem has been identified as important
for solving the temporal analysis task.
• We show how information from the environment surrounding a text can affect
the temporal analysis of instructional texts. In particular, we show that different
temporal structures for the same text can be derived in different environments.
Note that the environment information is in addition to the usual information
considered in temporal analysis such as information from tense and aspect, temporal connectives and real-world knowledge. An example of information from the environment for the domain of cookery recipes is the availability of resources for carrying out an action.
• We incorporate techniques developed in the field of temporal reasoning into the
temporal analysis task. In addition, we analyse the complexity of temporal reasoning algorithm needed in the temporal analysis of instructional texts.
• We propose a novel ontology for representing the composite and repetitive events that are mentioned in cookery recipes.Finally, the thesis ends with some suggestions for extending the work reported here
Systemic Strategies to Improve the Readability of the English Version of Indonesian Children Stories
The paper discusses languge exploitation for the children story books and offers several systemic strategies to improve the quality of language exploitation so that the books have a better quality for their readabality. Thirty children story books which are classified as narratives according to the publishers were randomly selected for the analysis. The books are targeted for children from five to twelve years old. The analysis on the text structure shows that all the stories have the three obligatory discourse units, namely orientation, complication, and resolution. Meanwhile, seen from the lexicogrammatical exploitation, most of the books have various grammatical mistakes and difficult words
A Web-Based Tool for Analysing Normative Documents in English
Our goal is to use formal methods to analyse normative documents written in
English, such as privacy policies and service-level agreements. This requires
the combination of a number of different elements, including information
extraction from natural language, formal languages for model representation,
and an interface for property specification and verification. We have worked on
a collection of components for this task: a natural language extraction tool, a
suitable formalism for representing such documents, an interface for building
models in this formalism, and methods for answering queries asked of a given
model. In this work, each of these concerns is brought together in a web-based
tool, providing a single interface for analysing normative texts in English.
Through the use of a running example, we describe each component and
demonstrate the workflow established by our tool
EliXR-TIME: A Temporal Knowledge Representation for Clinical Research Eligibility Criteria.
Effective clinical text processing requires accurate extraction and representation of temporal expressions. Multiple temporal information extraction models were developed but a similar need for extracting temporal expressions in eligibility criteria (e.g., for eligibility determination) remains. We identified the temporal knowledge representation requirements of eligibility criteria by reviewing 100 temporal criteria. We developed EliXR-TIME, a frame-based representation designed to support semantic annotation for temporal expressions in eligibility criteria by reusing applicable classes from well-known clinical temporal knowledge representations. We used EliXR-TIME to analyze a training set of 50 new temporal eligibility criteria. We evaluated EliXR-TIME using an additional random sample of 20 eligibility criteria with temporal expressions that have no overlap with the training data, yielding 92.7% (76 / 82) inter-coder agreement on sentence chunking and 72% (72 / 100) agreement on semantic annotation. We conclude that this knowledge representation can facilitate semantic annotation of the temporal expressions in eligibility criteria
Quantifying origin and character of long-range correlations in narrative texts
In natural language using short sentences is considered efficient for
communication. However, a text composed exclusively of such sentences looks
technical and reads boring. A text composed of long ones, on the other hand,
demands significantly more effort for comprehension. Studying characteristics
of the sentence length variability (SLV) in a large corpus of world-famous
literary texts shows that an appealing and aesthetic optimum appears somewhere
in between and involves selfsimilar, cascade-like alternation of various
lengths sentences. A related quantitative observation is that the power spectra
S(f) of thus characterized SLV universally develop a convincing `1/f^beta'
scaling with the average exponent beta =~ 1/2, close to what has been
identified before in musical compositions or in the brain waves. An
overwhelming majority of the studied texts simply obeys such fractal attributes
but especially spectacular in this respect are hypertext-like, "stream of
consciousness" novels. In addition, they appear to develop structures
characteristic of irreducibly interwoven sets of fractals called multifractals.
Scaling of S(f) in the present context implies existence of the long-range
correlations in texts and appearance of multifractality indicates that they
carry even a nonlinear component. A distinct role of the full stops in inducing
the long-range correlations in texts is evidenced by the fact that the above
quantitative characteristics on the long-range correlations manifest themselves
in variation of the full stops recurrence times along texts, thus in SLV, but
to a much lesser degree in the recurrence times of the most frequent words. In
this latter case the nonlinear correlations, thus multifractality, disappear
even completely for all the texts considered. Treated as one extra word, the
full stops at the same time appear to obey the Zipfian rank-frequency
distribution, however.Comment: 28 pages, 8 figures, accepted for publication in Information Science
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
Automatic case acquisition from texts for process-oriented case-based reasoning
This paper introduces a method for the automatic acquisition of a rich case
representation from free text for process-oriented case-based reasoning. Case
engineering is among the most complicated and costly tasks in implementing a
case-based reasoning system. This is especially so for process-oriented
case-based reasoning, where more expressive case representations are generally
used and, in our opinion, actually required for satisfactory case adaptation.
In this context, the ability to acquire cases automatically from procedural
texts is a major step forward in order to reason on processes. We therefore
detail a methodology that makes case acquisition from processes described as
free text possible, with special attention given to assembly instruction texts.
This methodology extends the techniques we used to extract actions from cooking
recipes. We argue that techniques taken from natural language processing are
required for this task, and that they give satisfactory results. An evaluation
based on our implemented prototype extracting workflows from recipe texts is
provided.Comment: Sous presse, publication pr\'evue en 201
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