René de Saussure and the theory of word formation

This volume presents two works elaborating a general theory of words and their structure written by René de Saussure, younger brother of Ferdinand de Saussure. Although originating in René de Saussure's concerns for the structure of Esperanto, these essays are clearly intended to articulate a general account of word formation in natural language. They appear here in the French original with facing English translations, accompanied by some remarks on René de Saussure's life and followed by essays on the Esperantist background of his analysis (by Marc van Oostendorp), the contemporary relevance of his morphological theory (by Stephen Anderson), and the semantic theory of words underlying his analysis (by Louis de Saussure). These two works have remained essentially unknown to the community of scholars in general linguistics since their publication in 1911 and 1919, respectively, although Esperantists have been aware of them. They develop in quite explicit form a theory of what would later be called morphemic analysis, based primarily on data from French (with some material from German and English, as well as occasional examples from other Indo-European languages). In its fundamental aspect, René's view of word formation differed significantly from that of his brother, who saw the structure of complex words as revealed not through their decomposition into smaller "atomic" units but rather in the relations between words, relations which could be presented in analogical form and which anticipate rule-based theories of morphological structure. The contrast between the two brothers' views thus anticipates basic issues in current theorizing about word structure

René de Saussure and the theory of word formation

This volume presents two works elaborating a general theory of words and their structure written by René de Saussure, younger brother of Ferdinand de Saussure. Although originating in René de Saussure's concerns for the structure of Esperanto, these essays are clearly intended to articulate a general account of word formation in natural language. They appear here in the French original with facing English translations, accompanied by some remarks on René de Saussure's life and followed by essays on the Esperantist background of his analysis (by Marc van Oostendorp), the contemporary relevance of his morphological theory (by Stephen Anderson), and the semantic theory of words underlying his analysis (by Louis de Saussure). These two works have remained essentially unknown to the community of scholars in general linguistics since their publication in 1911 and 1919, respectively, although Esperantists have been aware of them. They develop in quite explicit form a theory of what would later be called morphemic analysis, based primarily on data from French (with some material from German and English, as well as occasional examples from other Indo-European languages). In its fundamental aspect, René's view of word formation differed significantly from that of his brother, who saw the structure of complex words as revealed not through their decomposition into smaller "atomic" units but rather in the relations between words, relations which could be presented in analogical form and which anticipate rule-based theories of morphological structure. The contrast between the two brothers' views thus anticipates basic issues in current theorizing about word structure

René de Saussure and the theory of word formation

This volume presents two works elaborating a general theory of words and their structure written by René de Saussure, younger brother of Ferdinand de Saussure. Although originating in René de Saussure's concerns for the structure of Esperanto, these essays are clearly intended to articulate a general account of word formation in natural language. They appear here in the French original with facing English translations, accompanied by some remarks on René de Saussure's life and followed by essays on the Esperantist background of his analysis (by Marc van Oostendorp), the contemporary relevance of his morphological theory (by Stephen Anderson), and the semantic theory of words underlying his analysis (by Louis de Saussure). These two works have remained essentially unknown to the community of scholars in general linguistics since their publication in 1911 and 1919, respectively, although Esperantists have been aware of them. They develop in quite explicit form a theory of what would later be called morphemic analysis, based primarily on data from French (with some material from German and English, as well as occasional examples from other Indo-European languages). In its fundamental aspect, René's view of word formation differed significantly from that of his brother, who saw the structure of complex words as revealed not through their decomposition into smaller "atomic" units but rather in the relations between words, relations which could be presented in analogical form and which anticipate rule-based theories of morphological structure. The contrast between the two brothers' views thus anticipates basic issues in current theorizing about word structure

René de Saussure and the theory of word formation

This volume presents two works elaborating a general theory of words and their structure written by René de Saussure, younger brother of Ferdinand de Saussure. Although originating in René de Saussure's concerns for the structure of Esperanto, these essays are clearly intended to articulate a general account of word formation in natural language. They appear here in the French original with facing English translations, accompanied by some remarks on René de Saussure's life and followed by essays on the Esperantist background of his analysis (by Marc van Oostendorp), the contemporary relevance of his morphological theory (by Stephen Anderson), and the semantic theory of words underlying his analysis (by Louis de Saussure). These two works have remained essentially unknown to the community of scholars in general linguistics since their publication in 1911 and 1919, respectively, although Esperantists have been aware of them. They develop in quite explicit form a theory of what would later be called morphemic analysis, based primarily on data from French (with some material from German and English, as well as occasional examples from other Indo-European languages). In its fundamental aspect, René's view of word formation differed significantly from that of his brother, who saw the structure of complex words as revealed not through their decomposition into smaller "atomic" units but rather in the relations between words, relations which could be presented in analogical form and which anticipate rule-based theories of morphological structure. The contrast between the two brothers' views thus anticipates basic issues in current theorizing about word structure

Image Decomposition and Separation Using Sparse Representations: An Overview

This paper gives essential insights into the use of sparsity and morphological diversity in image decomposition and source separation by reviewing our recent work in this field. The idea to morphologically decompose a signal into its building blocks is an important problem in signal processing and has far-reaching applications in science and technology. Starck , proposed a novel decomposition method—morphological component analysis (MCA)—based on sparse representation of signals. MCA assumes that each (monochannel) signal is the linear mixture of several layers, the so-called morphological components, that are morphologically distinct, e.g., sines and bumps. The success of this method relies on two tenets: sparsity and morphological diversity. That is, each morphological component is sparsely represented in a specific transform domain, and the latter is highly inefficient in representing the other content in the mixture. Once such transforms are identified, MCA is an iterative thresholding algorithm that is capable of decoupling the signal content. Sparsity and morphological diversity have also been used as a novel and effective source of diversity for blind source separation (BSS), hence extending the MCA to multichannel data. Building on these ingredients, we will provide an overview the generalized MCA introduced by the authors in and as a fast and efficient BSS method. We will illustrate the application of these algorithms on several real examples. We conclude our tour by briefly describing our software toolboxes made available for download on the Internet for sparse signal and image decomposition and separation

Compositional Morphology for Word Representations and Language Modelling

This paper presents a scalable method for integrating compositional morphological representations into a vector-based probabilistic language model. Our approach is evaluated in the context of log-bilinear language models, rendered suitably efficient for implementation inside a machine translation decoder by factoring the vocabulary. We perform both intrinsic and extrinsic evaluations, presenting results on a range of languages which demonstrate that our model learns morphological representations that both perform well on word similarity tasks and lead to substantial reductions in perplexity. When used for translation into morphologically rich languages with large vocabularies, our models obtain improvements of up to 1.2 BLEU points relative to a baseline system using back-off n-gram models.Comment: Proceedings of the 31st International Conference on Machine Learning (ICML

Hyperspectral colon tissue cell classification

A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy

Sparsity and adaptivity for the blind separation of partially correlated sources

Blind source separation (BSS) is a very popular technique to analyze multichannel data. In this context, the data are modeled as the linear combination of sources to be retrieved. For that purpose, standard BSS methods all rely on some discrimination principle, whether it is statistical independence or morphological diversity, to distinguish between the sources. However, dealing with real-world data reveals that such assumptions are rarely valid in practice: the signals of interest are more likely partially correlated, which generally hampers the performances of standard BSS methods. In this article, we introduce a novel sparsity-enforcing BSS method coined Adaptive Morphological Component Analysis (AMCA), which is designed to retrieve sparse and partially correlated sources. More precisely, it makes profit of an adaptive re-weighting scheme to favor/penalize samples based on their level of correlation. Extensive numerical experiments have been carried out which show that the proposed method is robust to the partial correlation of sources while standard BSS techniques fail. The AMCA algorithm is evaluated in the field of astrophysics for the separation of physical components from microwave data.Comment: submitted to IEEE Transactions on signal processin

Non-Gaussianity analysis on local morphological measures of WMAP data

The decomposition of a signal on the sphere with the steerable wavelet constructed from the second Gaussian derivative gives access to the orientation, signed-intensity, and elongation of the signal's local features. In the present work, the non-Gaussianity of the WMAP temperature data of the cosmic microwave background (CMB) is analyzed in terms of the first four moments of the statistically isotropic random fields associated with these local morphological measures, at wavelet scales corresponding to angular sizes between 27.5 arcminutes and 30 degrees on the celestial sphere. While no detection is made neither in the orientation analysis nor in the elongation analysis, a strong detection is made in the excess kurtosis of the signed-intensity of the WMAP data. The non-Gaussianity is observed with a significance level below 0.5% at a wavelet scale corresponding to an angular size around 10 degrees, and confirmed at neighbour scales. This supports a previous detection of an excess of kurtosis in the wavelet coefficient of the WMAP data with the axisymmetric Mexican hat wavelet (Vielva et al. 2004). Instrumental noise and foreground emissions are not likely to be at the origin of the excess of kurtosis. Large-scale modulations of the CMB related to some unknown systematics are rejected as possible origins of the detection. The observed non-Gaussianity may therefore probably be imputed to the CMB itself, thereby questioning the basic inflationary scenario upon which the present concordance cosmological model relies. Taking the CMB temperature angular power spectrum of the concordance cosmological model at face value, further analysis also suggests that this non-Gaussianity is not confined to the directions on the celestial sphere with an anomalous signed-intensity.Comment: 10 pages, 3 figures. Version 2 includes minor changes to match version accepted for publication in MNRA