Data Mining Practical Machine Learning Tools and Techniques

From the Publisher:
This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.

/pdf/speech-and-language-processing-an-introduction-to-natural-atzaecqfem.pdf

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

Art : Art in which the subject matter has been simplified or distorted to the point that it may or may not be easily descended. Acrylic: Resin that, when mixed with water and pigment, forms an inorganic and quick drying medium. Acrylic paint therefore is a fast drying synthetic paint made from acrylic resin. Action Painting: The technique of dripping and splashing paint onto canvases stretched on the floor Aesthetic: Pertaining to the appreciation of the beautiful as opposed to the functional or utilitarian, and, by extention, to the appreciation of any form of art. Aesthetic value: The impact of a work of art on our senses, intellect, and emotions. Analogous colours:Pairs of colour such as yellow and orange, that are adjudscent to each other on the colour wheel. Acqatint : A print making process that includes etching and that permits broad areas of black and gray tones. Air brush: Atomizer operated by compressed air used for spraying on paint. Art Deco: A popular art and design style of the 1920s and 30s, and characterized by its intergration of organic and geometric forms. Art: The special expression of ideas, feelings and values in visual forms. Art criticism: A special concentrated way of looking at a piece of art with a purpose to recieve maximum enjoyment and meaning from it. Abstract Expressionism: A twentieth-century painting style in which artists applied paint freely to huge canvases in an effort to show feelings and emotions rather than realistic subject matter.Expressionism: A twentieth-century painting style in which artists applied paint freely to huge canvases in an effort to show feelings and emotions rather than realistic subject matter.

The Ministry of Education

In this paper we describe a state-of-the-art language identification system based on a parallel phone recognizer, the same as in PPRLM, but instead of using as phonotactic constraints traditional n-gram language models we use a new language model which is created using a ranking with the most frequent and discriminative n-grams between languages. Then, the distance between the ranking for the input sentence and the ranking for each language is computed, based on the difference in relative positions for each n-gram. The advantage of the proposed ranking is that it is able to model reliably longer span information than in traditional language models and that with less training data it is able to obtain more reliable estimations. In the paper, we describe the modifications that we have made to the original ranking technique, i.e., different discriminative formulas to establish the ranking, variations of the template size and a penalty for out-of-rank n-grams. Results are presented on a new and larger database. The test database has been significantly increased using cross-fold validation for more reliable results. Index Te rms: Language Identification, n-gram frequency ranking, text categorization, PPRLM

http://www-gth.die.upm.es/~lfdharo/Papers/DiscLID_FALA2010.pdf

A Discriminative Text Categorization Technique for Language Identification built into a PPRLM System

This paper describes a categorization module for improving the performance of a Spanish into Spanish Sign Language (LSE) translation system. This categorization module replaces Spanish words with associated tags. When implementing this module, several alternatives for dealing with non-relevant words have been studied. Non-relevant words are Spanish words not relevant in the translation process. The categorization module has been incorporated into a phrase-based system and a Statistical Finite State Transducer (SFST). The evaluation results reveal that the BLEU has increased from 69.11% to 78.79% for the phrase-based system and from 69.84% to 75.59% for the SFST.

/pdf/source-language-categorization-for-improving-a-speech-into-2zhmm49bx1.pdf

Source Language Categorization for improving a Speech into Sign Language Translation System

We propose three architectures for a word vector prediction system (WVPS) built with LSTMs that consider both past and future contexts of a word for predicting a vector in an embedded space where its surrounding area is semantically related to the considered word. We introduce an attention mechanism in one of the architectures so the system is able to assess the specific contribution of each context word to the prediction. All the architectures are trained under the same conditions and the same training material, following a curricular-learning fashion in the presentation of the data. For the inputs, we employ pretrained word embeddings. We evaluate the systems after the same number of training steps, over two different corpora composed of ground-truth speech transcriptions in Spanish language from TCSTAR and TV recordings used in the Search on Speech Challenge of IberSPEECH 2018. The results show that we are able to reach significant differences between the architectures, consistently across both corpora. The attention-based architecture achieves the best results, suggesting its adequacy for the task. Also, we illustrate the usefulness of the systems for resolving out-of-vocabulary (OOV) regions marked by an ASR system capable of detecting OOV occurrences

/pdf/attention-based-word-vector-prediction-with-lstms-and-its-z085wr355i.pdf

Attention-based word vector prediction with LSTMs and its application to the OOV problem in ASR

Traditionally, textual content has been the main source of information extraction and indexing, and other technologies that are capable of extracting information from the audio and video of multimedia documents have joined later. Other major axis of analysis is the emotional and affective aspect intrinsic in human communication. This information of emotions, stances, preferences, figurative language, irony, sarcasm, etc. is fundamental and irreplaceable for a complete understanding of the content in conversations, speeches, debates, discussions, etc. The objective of this project is focused on advancing, developing and improving speech and language technologies as well as image and video technologies in the analysis of multimedia content adding to this analysis the extraction of affective-emotional information. As additional steps forward, we will advance in the methodologies and ways for presenting the information to the user, working on technologies for language simplification, automatic reports and summary generation, emotional speech synthesis and natural and inclusive interaction.

/pdf/amic-affective-multimedia-analytics-with-inclusive-and-5aa1rjszup.pdf

AMIC: Affective multimedia analytics with inclusive and natural communication

This paper proposes a d-vector approach for extracting robust biometrics from inertial signals recorded with wearable sensors. The d-vector approach generates identity representations using a deep learning architecture composed of Convolutional Neural Networks. This architecture includes two convolutional layers for learning features from the inertial signal spectrum. These layers were pretrained using data from 154 subjects. After that, additional fully connected layers were attached to perform user identification and verification, considering 36 new subjects. This paper compares the proposed d-vector approach with previous proposed algorithms using in-the-wild recordings in different scenarios. The results demonstrated the robustness of the proposed d-vector approach for in-the-wild conditions: 97.69% and 94.16% accuracies (for user identification) and 99.89% and 99.67% Areas Under the Curve (for user verification) were obtained using one (walking) or several activities (walking, jogging and stairs) respectively. These results were also verified in laboratory conditions improving the performance reported in previous works. All the analyses were carried out using public datasets recorded at the Wireless Sensor Data Mining laboratory.

Rubén San-Segundo

Papers

A Discriminative Text Categorization Technique for Language Identification built into a PPRLM System

Source Language Categorization for improving a Speech into Sign Language Translation System

Attention-based word vector prediction with LSTMs and its application to the OOV problem in ASR

AMIC: Affective multimedia analytics with inclusive and natural communication

Robust Biometrics from Motion Wearable Sensors Using a D-vector Approach