This primer presents parameter estimation methods common in Bayesian statistics and apply them to discrete probability distributions, which commonly occur in text modeling. Presentation starts with maximum likelihood and a posteriori estimation approaches and the full Bayesian approach. This presentation is completed by an overview of Bayesian networks, a graphical language to express probabili...

Understanding a text, whether by a human being or by a computer, implies that units of meanings be identified in the text and that rules composing these units and the corresponding meaning units provide the complete meaning of the text. Such a statement raises many fundamental questions we shall not be concerned with (e.g. What is meaning?). We will limit ourselves to lexical and grammatical pr...

In recent years, there has been rising interest to using evidence derived from automatic syntactic analysis in large-scale corpus studies. Ideally, of course, corpus linguists would prefer to have access to the wealth of structural and featural information provided by a full parser based on a complex grammar formalism. However, to date such parsers achieve neither the speed nor the robustness n...

This paper reports on a project to digitise and analyse sample reading books recently used within British Primary schools. Through analysis of the textual content of example texts from this corpus, we aimed to illustrate statistical characteristics of these texts and consider implications for the expected rate of progression across texts intended for different school levels. Our project also in...

Externalizable.compileTo(classifier,file); @SuppressWarnings("unchecked") ConditionalClassifier compiledClassifier = (ConditionalClassifier) AbstractExternalizable.readObject(file); file.delete(); The static utility method compileTo() from Java’s class AbstractExternalizable class (in the util package) is used to do the writing. This could also be done through LingPi...

We describe a graphical logical form as a semantic representation for text understanding. This representation was designed to bridge the gap between highly expressive “deep” representations of logical forms and more shallow semantic encodings such as word senses and semantic relations. We also present an evaluation metric for the representation and report on the current performance on the TRIPS...

This paper presents a biologically inspired texture-based algorithm using local energy analysis for (1) segmenting text embedded in clutter and (2) classifying text scripts without any explicit knowledge of the type of text present. The local energy model has been shown to work well in texture analysis, where texture segmentation and discrimination are preattentive tasks in human vision. The al...

In which the author revisits the question of what text analysis could be. He traces the tools from their origin in the concordance. He argues that text analysis tools produce new texts generated from queries through processes implemented on the computer. These new texts come from the decomposition of original texts and recomposition into hybrid new works for interpretation. The author ends the ...

i. Introduction The study of text understanding and knowlegde extraction has been actively done by many researchers. The authors also studied a method of structured information extraction from texts without a global text analysis. The method is available for a comparatively sbort text such as a patent claim clause and an abstract of a technical paper. This paper describes tile outline of a meth...

We developed a system to participate in shared tasks on the analyzing clinical text. Our system approaches are both machine learning-based and rule-based. We applied the machine learning-based approach for Task 1: disorder identification, and the rule-based approach for Task 2: template slot filling for the disorder. In Task 1, we developed a supervised conditional random fields model that was ...