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Automatic Text Categorization in Terms of Genre and Author
TLDR
This paper proposes a set of style markers including analysis-level measures that represent the way in which the input text has been analyzed and capture useful stylistic information without additional cost to take full advantage of existing natural language processing (NLP) tools.
Speech enhancement based on audible noise suppression
A novel speech enhancement technique is presented based on the definition of the psychoacoustically derived quantity of audible noise spectrum and its subsequent suppression using optimal nonlinear
Comparative Evaluation of Various MFCC Implementations on the Speaker Verification Task
TLDR
A comparative evaluation of the presented MFCC implementations is performed on the task of text-independent speaker verification, by means of the well-known 2001 NIST SRE (speaker recognition evaluation) one-speaker detection database.
Computer-Based Authorship Attribution Without Lexical Measures
TLDR
This paper presents a fully-automated approach to the identification of the authorship of unrestricted text that excludes any lexical measure and adapts aset of style markers to the analysis of the text performed by an already existing natural language processing tool using three stylometric levels.
Text Genre Detection Using Common Word Frequencies
TLDR
It is shown that the most frequent words of the British National Corpus, representing the most Frequence of the written English language, are more reliable discriminators of text genre in comparison to the most frequently spoken words in a training corpus.
Automatic Stochastic Tagging of Natural Language Texts
TLDR
Five language and tagset independent stochastic taggers, handling morphological and contextual information, are presented and tested in corpora of seven European languages, using two sets of grammatical tags, and it is shown that the taggers' performance is satisfactory, even though a small training text is available.
Automatic Authorship Attribution
TLDR
The proposed set of style markers is able to distinguish texts of various authors of a weekly newspaper using multiple regression and is easily trainable and fully-automated requiring no manual text preprocessing nor sampling.
Neural classification of abnormal tissue in digital mammography using statistical features of the texture
TLDR
The authors investigated the efficiency of neural classifiers in recognizing cancer regions of suspicion (ROS) on mammograms by processing two types of texture features: statistical descriptors based on high-order statistics and the spatial gray-level dependence (SGLD) matrix.
Computer aided diagnosis of breast cancer in digitized mammograms.
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