Intersecting formatives and inflectional predictability: How do speakers and learners predict the correct form of Murrinhpatha verbs?

2016 ◽  
Vol 9 (2) ◽  
pp. 183-214 ◽  
Author(s):  
John Mansfield
Keyword(s):  
Conditional Entropy ◽  
Northern Australia ◽  
Relevant Model ◽  
Information Theoretic ◽  
Recent Information ◽  
Incomplete Knowledge ◽  
Closed Class ◽  
Verb Inflection ◽  
Correct Form ◽  
Inflectional Paradigms

This article investigates the phenomenon of inflection by intersecting formatives, that is to say, where an exponence is encoded by a combination of independently distributed phonological increments. Formative independence is defined in terms of conditional entropy. The verb inflection system of Murrinhpatha, an Aboriginal language of northern Australia, is analysed as a particularly complex example of intersecting formatives, and in general we can say that inflectional exponence in this language is highly irregular or unpredictable. Recent information-theoretic approaches to morphology provide us with methods for formalising and measuring the unpredictability of Murrinhpatha verb inflection. We add a distinct formalism that models the probability of correct inflectional prediction given incomplete knowledge of the inflectional paradigms in the language. We argue that this is a particularly relevant model for Murrinhpatha speaker/learners, because the language has a small, closed class of finite verb lexemes, most of which have their own idiosyncratic inflectional paradigm. There are not productively applied inflectional classes. In this model of inflectional predictability, intersecting formatives are in some cases the only chance a learner/speaker has of predicting the correct form.

Patterns of interpredictability and principal parts in Latin verb paradigms: an entropy-based approach

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Matteo Pellegrini
Keyword(s):  
Principal Part ◽  
Quantitative Approach ◽  
Information Theoretic ◽  
Verb Inflection ◽  
Type Frequency ◽  
Principal Parts ◽  
The Impact

AbstractThis paper provides a fully word-based, abstractive analysis of predictability in Latin verb paradigms. After reviewing previous traditional and theoretically grounded accounts of Latin verb inflection, a procedure is outlined where the uncertainty in guessing the content of paradigm cells given knowledge of one or more inflected wordforms is measured by means of the information-theoretic notions of unary and n-ary implicative entropy, respectively, in a quantitative approach that uses the type frequency of alternation patterns between wordforms as an estimate of their probability of application. Entropy computations are performed by using the Qumin toolkit on data taken from the inflected lexicon LatInfLexi. Unary entropy values are used to draw a mapping of the verbal paradigm in zones of full interpredictability, composed of cells that can be inferred from one another with no uncertainty. N-ary entropy values are used to extract categorical and near principal part sets, that allow to fill the rest of the paradigm with little or no uncertainty. Lastly, the issue of the impact of information on the derivational relatedness of lexemes on uncertainty in inflectional predictions is tackled, showing that adding a classification of verbs in derivational families allows for a relevant reduction of entropy, not only for derived verbs, but also for simple ones.

scholarly journals Information Guided Exploration of Scalar Values and Isocontours in Ensemble Datasets

Entropy ◽  
2018 ◽  
Vol 20 (7) ◽  
pp. 540 ◽  
Author(s):  
Subhashis Hazarika ◽  
Ayan Biswas ◽  
Soumya Dutta ◽  
Han-Wei Shen
Keyword(s):  
Weather Forecasting ◽  
Conditional Entropy ◽  
Specific Information ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Information Measures ◽  
New Information ◽  
Real World Datasets ◽  
The Individual ◽  
Information Theoretic Approach

Uncertainty of scalar values in an ensemble dataset is often represented by the collection of their corresponding isocontours. Various techniques such as contour-boxplot, contour variability plot, glyphs and probabilistic marching-cubes have been proposed to analyze and visualize ensemble isocontours. All these techniques assume that a scalar value of interest is already known to the user. Not much work has been done in guiding users to select the scalar values for such uncertainty analysis. Moreover, analyzing and visualizing a large collection of ensemble isocontours for a selected scalar value has its own challenges. Interpreting the visualizations of such large collections of isocontours is also a difficult task. In this work, we propose a new information-theoretic approach towards addressing these issues. Using specific information measures that estimate the predictability and surprise of specific scalar values, we evaluate the overall uncertainty associated with all the scalar values in an ensemble system. This helps the scientist to understand the effects of uncertainty on different data features. To understand in finer details the contribution of individual members towards the uncertainty of the ensemble isocontours of a selected scalar value, we propose a conditional entropy based algorithm to quantify the individual contributions. This can help simplify analysis and visualization for systems with more members by identifying the members contributing the most towards overall uncertainty. We demonstrate the efficacy of our method by applying it on real-world datasets from material sciences, weather forecasting and ocean simulation experiments.

scholarly journals On the Complexity and Typology of Inflectional Morphological Systems

2019 ◽  
Vol 7 ◽  
pp. 327-342 ◽  
Author(s):  
Ryan Cotterell ◽  
Christo Kirov ◽  
Mans Hulden ◽  
Jason Eisner
Keyword(s):  
Predictive Model ◽  
Conditional Entropy ◽  
Linguistic Complexity ◽  
Trade Off ◽  
Empirical Trade ◽  
Word Forms ◽  
Inflectional Paradigms ◽  
Surface Realization

We quantify the linguistic complexity of different languages’ morphological systems. We verify that there is a statistically significant empirical trade-off between paradigm size and irregularity: A language’s inflectional paradigms may be either large in size or highly irregular, but never both. We define a new measure of paradigm irregularity based on the conditional entropy of the surface realization of a paradigm— how hard it is to jointly predict all the word forms in a paradigm from the lemma. We estimate irregularity by training a predictive model. Our measurements are taken on large morphological paradigms from 36 typologically diverse languages.

scholarly journals Demorphologization and deepening complexity in Murrinhpatha

2020 ◽  
pp. 52-80
Author(s):  
John Mansfield ◽  
Rachel Nordlinger
Keyword(s):  
Northern Australia ◽  
Morphological Complexity ◽  
Verb Inflection ◽  
System P ◽  
High Level

Inflectional allomorphy is a prototypical form of morphological complexity, introducing unpredictability into the mapping of form to meaning. In this chapter, we examine a system of verb inflection allomorphy in the Murrinhpatha language of northern Australia, which shows a high level of complexity as measured by unpredictability of analogical relations in inflectional exponence. We argue that in this case the unpredictability is associated with incremental demorphologization, the process whereby morphology gradually dissolves into unanalysable lexical form. We present observations of analogical change in Murrinhpatha, comparing contemporary fieldwork documentation with data from forty years earlier, showing that a long-term process of demorphologization is still underway in recent generations, resulting in increasing complexity of the system.

scholarly journals The Information Loss of a Stochastic Map

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1021
Author(s):  
James Fullwood ◽  
Arthur J. Parzygnat
Keyword(s):  
Conditional Entropy ◽  
Information Loss ◽  
Shannon Information ◽  
Bayes Rule ◽  
Information Theoretic ◽  
Information Measures ◽  
Related Information ◽  
Conditional Information ◽  
Stochastic Map

We provide a stochastic extension of the Baez–Fritz–Leinster characterization of the Shannon information loss associated with a measure-preserving function. This recovers the conditional entropy and a closely related information-theoretic measure that we call conditional information loss. Although not functorial, these information measures are semi-functorial, a concept we introduce that is definable in any Markov category. We also introduce the notion of an entropic Bayes’ rule for information measures, and we provide a characterization of conditional entropy in terms of this rule.

A Nonparametric Clustering Algorithm with a Quantile-Based Likelihood Estimator

2014 ◽  
Vol 26 (9) ◽  
pp. 2074-2101 ◽  
Author(s):  
Hideitsu Hino ◽  
Noboru Murata
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Conditional Entropy ◽  
Tuning Parameter ◽  
Clustering Methods ◽  
Sampling Weights ◽  
Data Set ◽  
Information Theoretic ◽  
Nonparametric Clustering ◽  
Conditional Information

Clustering is a representative of unsupervised learning and one of the important approaches in exploratory data analysis. By its very nature, clustering without strong assumption on data distribution is desirable. Information-theoretic clustering is a class of clustering methods that optimize information-theoretic quantities such as entropy and mutual information. These quantities can be estimated in a nonparametric manner, and information-theoretic clustering algorithms are capable of capturing various intrinsic data structures. It is also possible to estimate information-theoretic quantities using a data set with sampling weight for each datum. Assuming the data set is sampled from a certain cluster and assigning different sampling weights depending on the clusters, the cluster-conditional information-theoretic quantities are estimated. In this letter, a simple iterative clustering algorithm is proposed based on a nonparametric estimator of the log likelihood for weighted data sets. The clustering algorithm is also derived from the principle of conditional entropy minimization with maximum entropy regularization. The proposed algorithm does not contain a tuning parameter. The algorithm is experimentally shown to be comparable to or outperform conventional nonparametric clustering methods.

scholarly journals Kardiometabolikus betegségek megelőzése és gyógyítása jógával

2016 ◽  
Vol 157 (9) ◽  
pp. 323-327 ◽  
Author(s):  
Péter Apor
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Side Effects ◽  
Metabolic Risk Factors ◽  
Metabolic Risk ◽  
Adverse Side Effects ◽  
Recent Information ◽  
Incomplete Knowledge ◽  
Low Costs

Yoga and other body-mind techniques enjoy an increasing popularity in many fields of health maintaining practices, in prevention of some illnesses and in curative medicine in spite of our incomplete knowledge about its applicability and effects. There are large differences among the various yoga-schools and the heterogeneity of indications etc. In this article a bucket of recent information is offered for the inquirers on the potential advantages of yoga (diet, mind-exercises, asanas, pranayamas) for decreasing cardio-metabolic risk factors, stabilizing mental health, and its addictive use in curative medicine. Few adverse side-effects may occur only in the case of misapplication. Its advantages are low costs, availability for broad population, and very few contraindications. Disadvantages include differences in the ability of yoga instructors and in yoga practices. Orv. Hetil., 2016, 157(9), 323–327.

scholarly journals Simple, direct and efficient multi-way spectral clustering

2018 ◽  
Vol 8 (1) ◽  
pp. 181-203 ◽  
Author(s):  
Anil Damle ◽  
Victor Minden ◽  
Lexing Ying
Keyword(s):  
Real World ◽  
Spectral Clustering ◽  
Randomized Algorithms ◽  
Initial Guess ◽  
Qr Factorization ◽  
Frobenius Norm ◽  
Cluster Assignment ◽  
Information Theoretic ◽  
Stochastic Block Model ◽  
Recent Information

AbstractWe present a new algorithm for spectral clustering based on a column-pivoted QR factorization that may be directly used for cluster assignment or to provide an initial guess for k-means. Our algorithm is simple to implement, direct and requires no initial guess. Furthermore, it scales linearly in the number of nodes of the graph and a randomized variant provides significant computational gains. Provided the subspace spanned by the eigenvectors used for clustering contains a basis that resembles the set of indicator vectors on the clusters, we prove that both our deterministic and randomized algorithms recover a basis close to the indicators in Frobenius norm. We also experimentally demonstrate that the performance of our algorithm tracks recent information theoretic bounds for exact recovery in the stochastic block model. Finally, we explore the performance of our algorithm when applied to a real-world graph.

scholarly journals Biodiversity, functional redundancy and system stability: subtle connections

2018 ◽  
Vol 15 (147) ◽  
pp. 20180367 ◽  
Author(s):  
Robert E. Ulanowicz
Keyword(s):  
Conditional Entropy ◽  
Functional Redundancy ◽  
System Stability ◽  
Biomass Distribution ◽  
Information Theoretic ◽  
Trophic Networks ◽  
System Components ◽  
Hill Numbers ◽  
Ensemble Systems ◽  
The Relationship

The relationship between biodiversity and functional redundancy has remained ambiguous for over a half-century, likely due to an inability to distinguish between positivist and apophatic (that which is missing) properties of ecosystems. Apophases are best addressed by mathematics that is predicated upon absence, such as information theory. More than 40 years ago, the conditional entropy of a flow network was proposed as a formulaic way to quantify trophic functional redundancy, an advance that has remained relatively unappreciated. When applied to a collection of 25 fully quantified trophic networks, this authoritative index correlates only poorly and transitively with conventional Hill numbers used to represent biodiversity. Despite such a weak connection, the underlying biomass distribution remains useful in conjunction with the qualitative diets of system components for providing a quick and satisfactory emulation of a system's functional redundancy. Furthermore, an information-theoretic cognate of the Wigner Semicircle Rule can be formulated using network conditional entropy to provide clues to the relative stability of any ecosystem under study. The necessity for a balance between positivist and apophatic attributes pertains to the functioning of a host of other living ensemble systems.

scholarly journals Dimensional Information-Theoretic Measurement of Facial Emotion Expressions in Schizophrenia

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jihun Hamm ◽  
Amy Pinkham ◽  
Ruben C. Gur ◽  
Ragini Verma ◽  
Christian G. Kohler
Keyword(s):  
Facial Expressions ◽  
Rating Scales ◽  
Ecological Validity ◽  
Conditional Entropy ◽  
Movement Analysis ◽  
Facial Emotion ◽  
Information Theoretic ◽  
Reliable Assessment ◽  
Information Theoretic Measures ◽  
Persons With Schizophrenia

Altered facial expressions of emotions are characteristic impairments in schizophrenia. Ratings of affect have traditionally been limited to clinical rating scales and facial muscle movement analysis, which require extensive training and have limitations based on methodology and ecological validity. To improve reliable assessment of dynamic facial expression changes, we have developed automated measurements of facial emotion expressions based on information-theoretic measures of expressivity ofambiguityanddistinctivenessof facial expressions. These measures were examined in matched groups of persons with schizophrenia (n=28) and healthy controls (n=26) who underwent video acquisition to assess expressivity of basic emotions (happiness, sadness, anger, fear, and disgust) in evoked conditions. Persons with schizophrenia scored higher onambiguity, the measure of conditional entropy within the expression of a single emotion, and they scored lower ondistinctiveness, the measure of mutual information across expressions of different emotions. The automated measures compared favorably with observer-based ratings. This method can be applied for delineating dynamic emotional expressivity in healthy and clinical populations.

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