10,580 research outputs found
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation
Existing approaches to automatic VerbNet-style verb classification are
heavily dependent on feature engineering and therefore limited to languages
with mature NLP pipelines. In this work, we propose a novel cross-lingual
transfer method for inducing VerbNets for multiple languages. To the best of
our knowledge, this is the first study which demonstrates how the architectures
for learning word embeddings can be applied to this challenging
syntactic-semantic task. Our method uses cross-lingual translation pairs to tie
each of the six target languages into a bilingual vector space with English,
jointly specialising the representations to encode the relational information
from English VerbNet. A standard clustering algorithm is then run on top of the
VerbNet-specialised representations, using vector dimensions as features for
learning verb classes. Our results show that the proposed cross-lingual
transfer approach sets new state-of-the-art verb classification performance
across all six target languages explored in this work.Comment: EMNLP 2017 (long paper
Emotion Recognition from Acted and Spontaneous Speech
DizertaÄnĂ prĂĄce se zabĂœvĂĄ rozpoznĂĄnĂm emoÄnĂho stavu mluvÄĂch z ĆeÄovĂ©ho signĂĄlu. PrĂĄce je rozdÄlena do dvou hlavnĂch ÄastĂ, prvnĂ ÄĂĄst popisuju navrĆŸenĂ© metody pro rozpoznĂĄnĂ emoÄnĂho stavu z hranĂœch databĂĄzĂ. V rĂĄmci tĂ©to ÄĂĄsti jsou pĆedstaveny vĂœsledky rozpoznĂĄnĂ pouĆŸitĂm dvou rĆŻznĂœch databĂĄzĂ s rĆŻznĂœmi jazyky. HlavnĂmi pĆĂnosy tĂ©to ÄĂĄsti je detailnĂ analĂœza rozsĂĄhlĂ© ĆĄkĂĄly rĆŻznĂœch pĆĂznakĆŻ zĂskanĂœch z ĆeÄovĂ©ho signĂĄlu, nĂĄvrh novĂœch klasifikaÄnĂch architektur jako je napĆĂklad âemoÄnĂ pĂĄrovĂĄnĂâ a nĂĄvrh novĂ© metody pro mapovĂĄnĂ diskrĂ©tnĂch emoÄnĂch stavĆŻ do dvou dimenzionĂĄlnĂho prostoru. DruhĂĄ ÄĂĄst se zabĂœvĂĄ rozpoznĂĄnĂm emoÄnĂch stavĆŻ z databĂĄze spontĂĄnnĂ ĆeÄi, kterĂĄ byla zĂskĂĄna ze zĂĄznamĆŻ hovorĆŻ z reĂĄlnĂœch call center. Poznatky z analĂœzy a nĂĄvrhu metod rozpoznĂĄnĂ z hranĂ© ĆeÄi byly vyuĆŸity pro nĂĄvrh novĂ©ho systĂ©mu pro rozpoznĂĄnĂ sedmi spontĂĄnnĂch emoÄnĂch stavĆŻ. JĂĄdrem navrĆŸenĂ©ho pĆĂstupu je komplexnĂ klasifikaÄnĂ architektura zaloĆŸena na fĂșzi rĆŻznĂœch systĂ©mĆŻ. PrĂĄce se dĂĄle zabĂœvĂĄ vlivem emoÄnĂho stavu mluvÄĂho na ĂșspÄĆĄnosti rozpoznĂĄnĂ pohlavĂ a nĂĄvrhem systĂ©mu pro automatickou detekci ĂșspÄĆĄnĂœch hovorĆŻ v call centrech na zĂĄkladÄ analĂœzy parametrĆŻ dialogu mezi ĂșÄastnĂky telefonnĂch hovorĆŻ.Doctoral thesis deals with emotion recognition from speech signals. The thesis is divided into two main parts; the first part describes proposed approaches for emotion recognition using two different multilingual databases of acted emotional speech. The main contributions of this part are detailed analysis of a big set of acoustic features, new classification schemes for vocal emotion recognition such as âemotion couplingâ and new method for mapping discrete emotions into two-dimensional space. The second part of this thesis is devoted to emotion recognition using multilingual databases of spontaneous emotional speech, which is based on telephone records obtained from real call centers. The knowledge gained from experiments with emotion recognition from acted speech was exploited to design a new approach for classifying seven emotional states. The core of the proposed approach is a complex classification architecture based on the fusion of different systems. The thesis also examines the influence of speakerâs emotional state on gender recognition performance and proposes system for automatic identification of successful phone calls in call center by means of dialogue features.
Image-based Text Classification using 2D Convolutional Neural Networks
We propose a new approach to text classification
in which we consider the input text as an image and apply
2D Convolutional Neural Networks to learn the local and
global semantics of the sentences from the variations of the
visual patterns of words. Our approach demonstrates that
it is possible to get semantically meaningful features from
images with text without using optical character recognition
and sequential processing pipelines, techniques that traditional
natural language processing algorithms require. To validate
our approach, we present results for two applications: text
classification and dialog modeling. Using a 2D Convolutional
Neural Network, we were able to outperform the state-ofart
accuracy results for a Chinese text classification task and
achieved promising results for seven English text classification
tasks. Furthermore, our approach outperformed the memory
networks without match types when using out of vocabulary
entities from Task 4 of the bAbI dialog dataset
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
A neural network approach to audio-assisted movie dialogue detection
A novel framework for audio-assisted dialogue detection based on indicator functions and neural networks is investigated. An indicator function defines that an actor is present at a particular time instant. The cross-correlation function of a pair of indicator functions and the magnitude of the corresponding cross-power spectral density are fed as input to neural networks for dialogue detection. Several types of artificial neural networks, including multilayer perceptrons, voted perceptrons, radial basis function networks, support vector machines, and particle swarm optimization-based multilayer perceptrons are tested. Experiments are carried out to validate the feasibility of the aforementioned approach by using ground-truth indicator functions determined by human observers on 6 different movies. A total of 41 dialogue instances and another 20 non-dialogue instances is employed. The average detection accuracy achieved is high, ranging between 84.78%±5.499% and 91.43%±4.239%
Virtual environment trajectory analysis:a basis for navigational assistance and scene adaptivity
This paper describes the analysis and clustering of motion trajectories obtained while users navigate within a virtual environment (VE). It presents a neural network simulation that produces a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organising map algorithm was tested and improved to in excess of 85% by using learning vector quantisation. This paper considers how such user classifications could be utilised in the delivery of intelligent navigational support and the dynamic reconfiguration of scenes within such VEs. We explore how such intelligent assistance and system adaptivity could be delivered within a Multi-Agent Systems (MAS) context
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