Notes:
Dimensionality reduction is a technique used to reduce the number of features or dimensions in a dataset, while retaining as much information as possible. This is often used in dialog systems in order to simplify the data and make it easier to analyze and understand. There are several different approaches to dimensionality reduction, including principal component analysis (PCA), independent component analysis (ICA), and singular value decomposition (SVD). These techniques can be used to reduce the complexity of a dataset by identifying patterns and relationships within the data and representing them in a lower-dimensional space.
In dialog systems, dimensionality reduction can be used to extract important features from large amounts of data and represent them in a more manageable form. This can help improve the performance of the system by reducing the complexity of the data and making it easier to analyze and process. Dimensionality reduction can also be used to identify relationships and patterns within the data that may be relevant to the task at hand, such as identifying common themes or topics of conversation.
See also:
Hierarchical Two-level Modelling of Emotional States in Spoken Dialog Systems
O Verkholyak, D Fedotov, H Kaya… – ICASSP 2019-2019 …, 2019 – ieeexplore.ieee.org
… Index Terms— Emotion recognition, cross-corpus, con- text modelling, dialog systems, LSTM … The number of components in PCA-CCA based domain adaptation, which also has an effect of dimensionality reduction, significantly influences the performance of the system; the …
Automatic evaluation of end-to-end dialog systems with adequacy-fluency metrics
LF D’Haro, RE Banchs, C Hori, H Li – Computer Speech & Language, 2019 – Elsevier
… A dialog system is expected to understand user’s requests, ask follow-up questions in … into the evaluation of generative answers in the context of dialog systems, we borrow … here, the continuous space embedding is obtained by applying a dimensionality reduction technique to a …
End-to-End Question Answering Models for Goal-Oriented Dialog Learning
J Shin, A Madotto, M Seo, P Fung – 2019 – workshop.colips.org
… models for the goal-oriented dialog sys- tem task in Dialog System Technology Challenges … response candidates pass through a Multi-layer Perceptron (MLP) for dimensionality reduction that matches … Partially observable markov decision processes for spoken dialog systems …
Unsupervised dialogue intent detection via hierarchical topic model
A Popov, V Bulatov, D Polyudova… – Proceedings of the …, 2019 – aclweb.org
… More universal and robust dialogue systems should work without any supervision or defined rules … Dimensionality reduction methods could be used to improve clustering qual- ity: PCA or Uniform Manifold Approximation and Projection (UMAP, McInnes et al. (2018)) …
A Survey on Transfer Learning
S Panigrahi, A Nanda, T Swarnkar – Intelligent and Cloud Computing, 2019 – Springer
… broad applicability in image classification [1], sentiment classification [2], dialog systems [3], and … Unsupervised transfer learning Un available Un available Clustering, Dimensionality reduction Last case is … Yu, Z., Jiajun, L., Qiang, Y.: Personalizing a dialogue system with transfer …
Reinforcement learning based text style transfer without parallel training corpus
H Gong, S Bhat, L Wu, J Xiong, W Hwu – arXiv preprint arXiv:1903.10671, 2019 – arxiv.org
… natural language generation and is at the heart of many recent NLP applications, such as personalized responses in dialogue system (Zhou et … VAEs are commonly used to learn the hidden rep- resentation of inputs for dimensionality reduction, and have been found to be useful …
A Bandit Approach to Posterior Dialog Orchestration Under a Budget
S Upadhyay, M Agarwal, D Bounneffouf… – arXiv preprint arXiv …, 2019 – arxiv.org
… For multi-purpose dialog systems, like personal home assistants, executing and retrieving features or responses from every skill can be computationally expensive or intractable, with the potential to cause a poor … Motivated by dimensionality reduction tasks, Abbasi-Yadkori et. al …
A multilingual and multidomain study on dialog act recognition using character-level tokenization
E Ribeiro, R Ribeiro, DM de Matos – Information, 2019 – mdpi.com
… Automatic dialog act recognition is an important step for dialog systems since it … Dialog act recognition is an important task in the context of a dialog system, since dialog acts are the minimal units of linguistic communication that reveal the intention behind the uttered words [1 …
Speech Technology for Swedish: Current Impact Areas for Applications and Edyson, an Innovative Tool for Accessing Speech Data
D House, P Fallgren, J Edlund – lt4all.elra.info
… It pushes Swedish spoken dialogue system research by providing a new clear and useful dialogue type … Third, the feature vectors are then run through a dimensionality reduction algorithm, eg t-SNE (Maaten and Hinton, 2008) self-organizing maps (Kohonen, 1982) that maps …
Speech emotion recognition based on PCA and CHMM
X Ke, B Cao, J Bai, Q Yu, D Yang – 2019 IEEE 8th Joint …, 2019 – ieeexplore.ieee.org
… Table 2 shows that the dimensionality reduction of PCA improves the model recognition ability by 22.61 … Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems[C]?Conference on Empirical Methods in Natural Language Processing …
Use of non-verbal vocalizations for continuous emotion recognition from speech and head motion
SN Fatima, E Erzin – 2019 14th IEEE Conference on Industrial …, 2019 – ieeexplore.ieee.org
… HCIs that are based on continuous emotion recognition (CER) include human-robot communication, call center dialog systems, smart gaming … We use principal component analysis (PCA) for dimensionality reduction by adjusting the number of principal components to retain …
Emoji prediction for Hebrew political domain
C Liebeskind, S Liebeskind – … Proceedings of The 2019 World Wide Web …, 2019 – dl.acm.org
… [47] investigated the task of emoji recommendation in multi-turn dialogue systems … For dimensionality reduction, GenSim4 python library with the default settings of 300 dimensions for the LSA, RP ,and Word Embedding (doc2vec) and 100 dimensions for LSA was used (due …
I wanna talk like you: Speaker adaptation to dialogue style in l2 practice conversation
AJ Sinclair, R Ferreira, D Gaševi?, CG Lucas… – … Conference on Artificial …, 2019 – Springer
… Dimensionality reduction is performed using Singular Value Decomposition (SVD) [10] representing the projection graph in a two dimensional space [svd1, svd2] … This better understanding of tutor adaptation can inform the design of tutoring dialogue systems …
Multi-Lingual Dialogue Act Recognition with Deep Learning Methods
J Martínek, P Kral, L Lenc, C Cerisara – arXiv preprint arXiv:1904.05606, 2019 – arxiv.org
… pivotal role in dialogue management [4]. Any improvement in this task may increase the performance of the whole dialogue system … It is a technique for multivariate data analysis and dimensionality reduction, which quantifies the linear associa- tions between a … Dialog Systems …
Exploration of complementary features for speech emotion recognition based on kernel extreme learning machine
L Guo, L Wang, J Dang, Z Liu, H Guan – IEEE Access, 2019 – ieeexplore.ieee.org
… Human-computer interaction has become popular in various fields, especially for intelligent dialogue systems and voice assistants, such as Siri, Cortana, and Google Assistant. In these applications, intention understanding is one of the key parts of the whole dialog system …
Precision Health and Medicine: A Digital Revolution in Healthcare
A Shaban-Nejad, M Michalowski – 2019 – books.google.com
… Yoko Eguchi and Yoshinobu Kano Spoken Dialogue Systems for Medication … based ExplaNAtions Mindfulness-Based Stress Reduction Multifactor Dimensionality Reduction Multiple-Input … Forests rifampicin Recurrent Neural Network Spoken Dialogue System Single-Nucleotide …
Deep dialog act recognition using multiple token, segment, and context information representations
E Ribeiro, R Ribeiro, DM de Matos – Journal of Artificial Intelligence …, 2019 – jair.org
… when the discourse model is based on a CNN or on a bidirectional LSTM unit, it considers information from future segments, which is not available to a dialog system … Dimensionality Reduction Dimensionality Reduction Dimensionality Reduction Dimensionality Reduction …
Hierarchical Multi-Label Dialog Act Recognition on Spanish Data
E Ribeiro, R Ribeiro, DM de Matos – arXiv preprint arXiv:1907.12316, 2019 – arxiv.org
… Thus, their automatic recognition is important for a dialog system trying to understand its conver- sational partner … The values for the number of segments are rounded. The interaction column states whether the dialogs are between humans or if there is a dialog system involved …
TS-RNN: text steganalysis based on recurrent neural networks
Z Yang, K Wang, J Li, Y Huang… – IEEE Signal Processing …, 2019 – ieeexplore.ieee.org
… lated to high-quality readable text generation have appeared, in- cluding neural machine translation [15], dialogue systems [16], and … Estimation: We can use t-Distributed Stochastic Neighbor Embedding (t-SNE) [33] technique for the dimensionality reduction and visualization …
String-based Multinomial Naïve Bayes for Emotion Detection among Facebook Diabetes Community
V Balakrishnan, W Kaur – Procedia Computer Science, 2019 – Elsevier
… and thus showing that converting numerical vectors to string vectors improves dimensionality reduction [7]. Nevertheless, the implementation of string vectors instead of numerical vectors as an effort for dimensionality reduction seems to be … “Health dialog systems for patients …
Precision Health and Medicine
A Shaban-Nejad, M Michalowski – Springer
… Takinami, Yoko Eguchi and Yoshinobu Kano Spoken Dialogue Systems for Medication … MBSR Mindfulness-Based Stress Reduction MDR Multifactor Dimensionality Reduction MIMO Multiple … rifampicin RNN Recurrent Neural Network SDS Spoken Dialogue System SNP Single …
Sisua: Semi-supervised generative autoencoder for single cell data
TN Trong, R Kramer, J Mehtonen, G González… – bioRxiv, 2019 – biorxiv.org
Page 1. SISUA: SEMI-SUPERVISED GENERATIVE AUTOENCODER FOR SINGLE CELL DATA APREPRINT Trung Ngo Trong School of Computing University of Eastern Finland Joensuu, Finland trung@uef.fi Roger Kramer …
Emotional Human-Computer Interface
M Iza – 2019 – pdfs.semanticscholar.org
… Likewise, dialogue systems, mixed-initiative planning systems, or systems that learn from observation could also benefit from such an approach … To reduce the length of this audio-visual vector, dimensionality reduction techniques are applied …
Internet of Things Anomaly Detection using Multivariate Analysis
S Ezekiel, AA Alshehri, L Pearlstein, XW Wu… – The 3rd ICICPE 2019 … – icicpe.org
… a multivariate analysis technique commonly used for feature extraction and dimensionality reduction of large … system for elementary school students by combining scenario-based dialog system and English … For the tasks such as dialogue system or Speech-To- Text (STT), using …
Emoty: an emotionally sensitive conversational agent for people with neurodevelopmental disorders
F Catania, N Di Nardo, F Garzotto… – Proceedings of the 52nd …, 2019 – 128.171.57.22
… Still, we could not find any published paper that explores dialog systems as a tool … The Dialog System has been implemented with the IBM Watson Assistant service, which combines … This step simply represents a dimensionality reduction phase working at row-level based on the …
Community Detection in Knowledge Graph Network with Matrix Factorization Learning
X Shi, Y Qian, H Lu – Asia-Pacific Web (APWeb) and Web-Age Information …, 2019 – Springer
… of knowledge graph will include knowledge fusion, semantic network, semantic search and recommendation, Q&A and dialogue system, and big … SC) make use of the spectrum (eigenvalues) of the similarity matrix of the data to perform dimensionality reduction before clustering …
Towards a Portable SLU System Applied to MSA and Low-resourced Algerian Dialects
M Lichouri, R Djeradi, A Djeradi, M Abbas – International Conference on …, 2019 – Springer
… implementation of a multimodal, multilingual and multi-domain human-machine dialogue system is necessary … present paper, we are interested in the multilingual aspect in human-machine dialog system … Leskovec, J.: Dimensionality reduction PCA, SVD, MDS, ICA, and friends …
Early integration for movement modeling in latent spaces
R Hornung, N Chen, P van der Smagt – The Handbook of Multimodal …, 2019 – dl.acm.org
… Moreover, in most cases the intrinsic dimensionality of the movement is much lower (cf. Section 8.2.2). Dimensionality reduction techniques can reduce the high- dimensional state space to a lower-dimensional latent space, which may not have Page 14 …
Active Annotation: bootstrapping annotation lexicon and guidelines for supervised NLU learning
F Marinelli, A Cervone, G Tortoreto… – arXiv preprint arXiv …, 2019 – arxiv.org
… To perform dimensionality reduction we apply the Principal Component Analysis (PCA) [1], on the re- sulting data we perform unsupervised clustering using K-Means clustering, in particular … Mi- crosoft dialogue challenge: Building end-to-end task-completion dialogue systems …
An analysis of the role of artificial intelligence in education and teaching
G Malik, DK Tayal, S Vij – Recent Findings in Intelligent Computing …, 2019 – Springer
… Collaborative filtering of data also uses advanced AI principles such as dimensionality reduction and prediction elicitation [7]. Assessments of real-time data obtained from these systems are very important to give user a real-time environment where … The dialogue system of Auto …
A comparative study of word embedding methods for early risk prediction on the Internet
E Fano – 2019 – diva-portal.org
… It would be possible to develop chat bots and other dialogue systems that can determine the severity of a person’s mental health risk based on just a few lines of text … This is a technique introduced by Deerwester et al. (1990) where a dimensionality reduction technique is …
Development of a Novel Database in Gujarati Language for Spoken Digits Classification
N Dalsaniya, SH Mankad, S Garg… – … Symposium on Signal …, 2019 – Springer
… Some other applications include airline and train reservation, Robot control and navigational command systems and interactive spoken dialogue systems … points in feature space, indicating that it is not easy to separate the classes even after applying dimensionality reduction …
Systemic-Functional Linguistics and Computation: new directions, new challenges
JA Bateman, D McDonald, T Hiippala… – The Cambridge …, 2019 – helda.helsinki.fi
… The usual components of a computational dialogue system therefore span a con- siderable breadth of linguistic knowledge as well: ranging from … Working on computational dialogue systems is then of considerable value for refining our linguistic theories in each of these areas …
Automatic assessment of interaction quality in human-human conversations
A Spirina – 2019 – oparu.uni-ulm.de
… 2010 engineers/researchers started working with Natural Dialogue Systems (NDS) which can be … et al., 1997] the authors introduced the PARADISE (PARAdigm for Dialogue System Evaluation) framework … helps to reveal “problematic calls and enables the dialog system to react …
Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease
H Wang, Y Shen, S Wang, T Xiao, L Deng, X Wang… – Neurocomputing, 2019 – Elsevier
… For reducing data dimension and optimizing feature expression, Zhang et al. [36] proposed an unsupervised deep-learning data dimensionality reduction method named LDFA, which can learn both local and global features of the sample …
More than just words: Modeling non-textual characteristics of podcasts
L Yang, Y Wang, D Dunne, M Sobolev… – Proceedings of the …, 2019 – dl.acm.org
… in- terview, or monologue speech, which have been widely studied by the speech community in the context of Automatic Speech Recog- nition (ASR) [15], dialog systems [29], and speech … Principle Component Analysis (PCA) was ap- plied for the dimensionality reduction …
Semantic representations for under-resourced languages
J Mazarura, A de Waal, P de Villiers – … of the South African Institute of …, 2019 – dl.acm.org
… In statistical terms these two purposes can be described as clustering and dimensionality reduction. Topic models possess characteris- tics from both … Intent classification, which is used in task-driven dialogue systems and sentiment classification Page 8 …
An Investigation of LSTM-CTC based Joint Acoustic Model for Indian Language Identification
T Mandava, RK Vuddagiri, HK Vydana… – 2019 IEEE Automatic …, 2019 – ieeexplore.ieee.org
… area with notable applica- tions in various fields such as multilingual automatic speech recognizer, multilingual dialogue system, and voice … N. Dehak, PA Torres-Carrasquillo, D. Reynolds, and R. Dehak, “Language recognition via i-vectors and dimensionality reduction,” in Proc …
Native Language Identification from Raw Waveforms Using Deep Convolutional Neural Networks with Attentive Pooling
R Ubale, V Ramanarayanan, Y Qian… – 2019 IEEE Automatic …, 2019 – ieeexplore.ieee.org
… After dimensionality reduction, the represen- tations are length-normalized and modeled by PLDA to compute the LLR for each test utterance … Ex- ploring ASR-free end-to-end modeling to improve spoken language understanding in a cloud-based dialog system,” in Automatic …
A model based on siamese neural network for Online transaction fraud detection
X Zhou, Z Zhang, L Wang… – 2019 International Joint …, 2019 – ieeexplore.ieee.org
… [19] TH Wen, M. Gasic, et al, “Semantically conditioned lstm-based natural language generation for spoken dialogue systems,” arXiv preprint arXiv:1508.01745, 2015 … [22] R. Hadsell, S. Chopra, Y.Lecun, “Dimensionality reduction by learning an invariant mapping,” IEEE …
Addressing noise and pitch sensitivity of speech recognition system through variational mode decomposition based spectral smoothing
IC Yadav, S Shahnawazuddin, G Pradhan – Digital Signal Processing, 2019 – Elsevier
JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …
Multi-Head Self-Attention Networks for Language Identification
RK Vuddagiri, T Mandava, HK Vydana… – 2019 Twelfth …, 2019 – ieeexplore.ieee.org
… between multiple languages [1]. Some of them are multilingual automatic speech recognizers (ASR), multilingual dialogue systems, and voice … PA Torres-Carrasquillo, D. Reynolds, and R. Dehak, “Language recognition via i-vectors and dimensionality reduction,” in Twelfth …
Translator2Vec: Understanding and Representing Human Post-Editors
A Góis, AFT Martins – arXiv preprint arXiv:1907.10362, 2019 – arxiv.org
… In the last two works, an auxiliary task also helps to pro- vide a latent representation of an object of interest. Visualization of translation sessions. To visu- alize the vectors h produced during our auxiliary task, we use Parametric t-SNE (Maaten, 2009) for dimensionality reduction …
Research on Retrieval Ranking Based on Deep Reinforcement Learning
K Zhang, M Lin, Y Li – 2019 6th International Conference on …, 2019 – ieeexplore.ieee.org
… only used deep neural networks to reduce the dimensionality of high-dimensional input data, and then used traditional reinforcement learning to process the dimensionality reduction data … ranking method will provide a more interesting platform for intelligent dialogue systems …
Finding Interpretable Concept Spaces in Node Embeddings using Knowledge Bases
M Idahl, M Khosla, A Anand – Joint European Conference on Machine …, 2019 – Springer
… graph or node embeddings have proven useful in many applications such as question answering [1], dialog systems [14], recommender … distill the high-dimensional discrete representation of nodes into a dense vector embedding using dimensionality reduction methods, which …
Follow Alice into the Rabbit Hole: Giving Dialogue Agents Understanding of Human Level Attributes
AW Li, V Jiang, SY Feng, J Sprague, W Zhou… – arXiv preprint arXiv …, 2019 – arxiv.org
… 5.3 Baselines We compare against four dialogue system baselines: Kvmemnn, Feed Yourself, Poly-encoder, and a BERT bi- ranker baseline trained on the Persona-Chat dataset using the same training hyperparameters (including learning rate Page 6 …
Enriching Scientific Paper Embeddings with Citation Context
K Henner – 2019 – digital.lib.washington.edu
… representations, which are useful in a wide variety NLP tasks. As Landauer and Dumais (1997) note, the dimensionality reduction also has the effect of identifying latent dimensions in the data. This allows the model to capture the similarity of …
Analysing research on Information Systems success and failure: A Machine Learning Technique
A Akin-Adetoro, L Seymour – Proceedings of the South African Institute …, 2019 – dl.acm.org
… natural language text or speech to accomplish beneficial tasks [8] . Examples of NLP tasks include conversational agent, dialogue system, text classification … The SVD, as illustrated with equation (1), is a dimensionality reduction technique that takes in the TF-IDF matrix (M) and …
Comparing the Performance of Feature Representations for the Categorization of the Easy-to-Read Variety vs Standard Language
M Santini, B Danielsson, A Jönsson – … of the 22nd Nordic Conference on …, 2019 – aclweb.org
… ar- eas that could benefit from it include informa- tion retrieval (eg for the retrieval of easy-to-read or patient-friendly medical information) and deep learning-based dialogue systems (eg customized … Similar to PCA, the basic idea behind autoencoders is dimensionality reduction …
DRCoVe: An Augmented Word Representation Approach using Distributional and Relational Context
MA Parwez, M Abulaish, M Fazil – abulaish.com
… One measure is to convert such sparse vectors into low dimensional dense vectors to improve computational efficiency and generalization. In this regard, dimensionality reduction is a way to find low dimensional dense vectors using matrix factorization techniques such as SVD …
Native Language Identification in Very Short Utterances Using Bidirectional Long Short-Term Memory Network
F Adeeba, S Hussain – IEEE Access, 2019 – ieeexplore.ieee.org
… NLI can also facilitate a spoken dialog system by suggesting a user’s cultural background. Majority of the research in the area of NLI is focused on identifying the native language of speakers learning English as a second language …
Extended Reality Experiences Prediction using Collaborative Filtering
I M. Gironacci – SIGGRAPH Asia 2019 Doctoral Consortium, 2019 – dl.acm.org
… in order to engage users even more, and let them feel like part of the story rather than just observers; 4) creation of facial animations – to increase the realism of characters; 5) the investigation of a dialogue system … “Application of Dimensionality Reduction in Recommender …
Deep learning for nlp and speech recognition
U Kamath, J Liu, J Whitaker – 2019 – Springer
Page 1. Uday Kamath · John Liu · James Whitaker Deep Learning for NLP and Speech Recognition Page 2. Deep Learning for NLP and Speech Recognition Page 3. Uday Kamath • John Liu • James Whitaker Deep Learning for NLP and Speech Recognition 123 Page 4 …
Integration of Wavelet and Recurrence Quantification Analysis in Emotion Recognition of Bilinguals
A Goshvarpour, A Goshvarpour… – International Clinical …, 2019 – journals.sbmu.ac.ir
Page 1. Introduction Over the past decades, rapid progress within the field of human-computer interface (HCI) has apprehended. By including emotions and affective communications in HCI, a new prospect in human life has developed, which is known as affective computing …
A review of deep learning research
R Mu, X Zeng – KSII Transactions on Internet and Information …, 2019 – koreascience.or.kr
… Chinese microblog sentiment analysis [27], machine translation [28], Question Answering [29], Dialogue System [30], etc … There are two main functions of the down-sampling layer: a) dimensionality reduction of the feature map; b) maintaining the scale invariant characteristics …
A survey on deep learning empowered IoT applications
X Ma, T Yao, M Hu, Y Dong, W Liu, F Wang… – IEEE Access, 2019 – ieeexplore.ieee.org
… FIGURE 1. An RBM with m visible and n hidden variables. in dimensionality reduction and collaborative filtering [20]. A … 68]. Such dialogue system based products would func- tion as the next-generation smart home controller …
Empirical study and improvement on deep transfer learning for human activity recognition
R Ding, X Li, L Nie, J Li, X Si, D Chu, G Liu, D Zhan – Sensors, 2019 – mdpi.com
… Some researchers studied the switching of conversation models in different scenarios within NLP (Natural Language Processing), so that the dialogue system can satisfy the needs of users [15]. We focus on the transfer learning based on features …
Retrieving Relationships from a Knowledge Graph for Question Answering
P Agarwal, M Ramanath, G Shroff – European Conference on Information …, 2019 – Springer
Answering natural language questions posed on a knowledge graph requires traversing an appropriate sequence of relationships starting from the mentioned entities. To answer complex queries, we often…
Evaluation of Feature Learning Methods for Voice Disorder Detection
H Guan, A Lerch – International Journal of Semantic Computing, 2019 – World Scientific
… Electr. Eng. 57 (2017) 257–265. [11] N. Souissi and A. Cherif, Dimensionality reduction for voice disorders identi¯cation system based on mel frequency cepstral coe±cients and support vector machine, in 7th Int. Conf. Modelling, Identi¯cation and Control, 2016, pp …
Dynamic emotion modelling and anomaly detection in conversation based on emotional transition tensor
X Sun, C Zhang, L Li – Information Fusion, 2019 – Elsevier
… comprehensively. Nowadays, emotional transition research and dialogue system are becoming ever more popular, which can be applied to complex tasks, such as healthcare, security and intelligent environments. Applications …
Semantic and Discursive Representation for Natural Language Understanding
D Sileo – 2019 – tel.archives-ouvertes.fr
Page 1. HAL Id: tel-02619733 https://tel.archives-ouvertes.fr/tel-02619733 Submitted on 25 May 2020 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not …
Text Classification With Deep Neural Networks
T Huynh – 2019 – oro.open.ac.uk
… word spans without explicit training labels. In the future I propose the learned representations to be used with the discussed Deep Neural Net- works in different NLP tasks such as Dialog Systems, Machine Translation or Natural Language Inference. Page 4. Contents …
Annotation-efficient approaches towards real-time emotion recognition
IP Lajos – 2019 – ritsumei.repo.nii.ac.jp
… As a novel approach, the author argues that emotion-interdependent dialogue acts can improve emotion/sentiment/polarity recognition even on small sets of labeled data, thus making them applicable for the pre-training of commercial games, dialogue systems, and other …
Learning in the presence of partial observability and concept drifts
R Seraj – 2019 – search.proquest.com
Page 1. Learning in the presence of partial observability and concept drifts. Raihan Seraj Master of Engineering Electrical and Computer Engineering McGill University Montreal, Canada April 2019 A thesis submitted to McGill …
Artificial Intelligence acceptance: morphological elements of the acceptance of Artificial Intelligence
MM Figueiredo – 2019 – repositorio.ucp.pt
… Discrete Classification Clustering Continuous Regression Dimensionality reduction Page 27 … Shawar and Atwell (2010) enumerate the different names people have given to chatbots ranging from virtual agents and dialogue systems to machine conversation language system …
Neural Language Models with Explicit Coreference Decision
J Kunz – 2019 – diva-portal.org
… 0 in many approaches). The next step is a dimensionality reduction. Although in the example above it does not seem useful, it is important to remember that common vocabulary sizes 9 Page 10. are very high. Therefore M is …
Word similarity datasets for Thai: Construction and evaluation
P Netisopakul, G Wohlgenannt, A Pulich – IEEE Access, 2019 – ieeexplore.ieee.org
… often used vector-space models of term collocation counts [5], sometimes with post- processing like dimensionality reduction techniques or … However, the performance in NLP downstream tasks such as dialogue systems or document classification depends on many factors such …
Machine Learning Algorithms for Big Data
CSR Prabhu, AS Chivukula, A Mogadala… – Big Data Analytics …, 2019 – Springer
… 6.4.5 Manifold Learning. Goal is to achieve nonlinear dimensionality reduction … Also, it has shown its applicability to other areas such as robotic control (where robots perform several tasks similar to humans), online advertising, dialogue systems, etc …
Arabic Poem Generation with Hierarchical Recurrent Attentional Network
S Talafha, B Rekabdar – 2019 IEEE 13th International …, 2019 – ieeexplore.ieee.org
… Recently, deep learning has played a crucial role in developing the Arabic NLP field. It has opened the door to many applications including question- answering [6], neural machine translation [11], sentiment analyses [12], and dialogue systems [7]. M.AlSmadi et al …
Embedding Humans into Service Systems Analysis: The Evolution of Mathematical Thinking About Services
A Medina-Borja – Handbook of Service Science, Volume II, 2019 – Springer
… adapt mimicking the case of human-human interaction as the naturalness of the dialogue increases in spoken dialogue systems, where the … learning approach has been widely used not only for classification tasks and data mining, but also for dimensionality reduction and image …
AHNG: Representation learning on attributed heterogeneous network
M Liu, J Liu, Y Chen, M Wang, H Chen, Q Zheng – Information Fusion, 2019 – Elsevier
JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …
Prediction of Traffic Congestion on Wired and Wireless Networks Using RNN
S Yamaguchi, T Kamiyama… – Proceedings of the 13th …, 2019 – books.google.com
… many kinds of neural networks such as those used for computer vision, time-series data, clustering and dimensionality reduction … a new model by extending the hierarchical recurrent encoder- decoder neural network for building a conversational dialogue system which provides …
Multimodal Analysis and Estimation of Intimate Self-Disclosure
M Soleymani, K Stefanov, SH Kang, J Ondras… – 2019 International …, 2019 – dl.acm.org
… recog- nition of self-disclosure has been limited to language under- standing in interactions with spoken dialogue system [35], online … We used the 128- dimensional embedding that can be generated by VGGish after dimensionality reduction with Principal Component Analysis …
Reinforcement Learning in Structured and Partially Observable Environments
K Azizzadenesheli – 2019 – escholarship.org
… iteratively find the low rank structure in SLBs. We show that deploying projection meth- ods assures dimensionality reduction and results in a tighter regret upper bound that is in terms of the dimensionality of the subspace and its properties, rather than the dimensional …
Neural-network-based Memory for a Social Robot: Learning a Memory Model of Human Behavior from Data
M Doering, T Kanda, H Ishiguro – ACM Transactions on Human-Robot …, 2019 – dl.acm.org
… Oz setting and was designed for “adding memory to goal-oriented dialog systems” [42 … Janarthanam and Lemon presented a finite-state-based dialog system for setting up home broad … vectorization (n = 1, 2, and 3) with latent semantic analysis (LSA) for dimensionality reduction …
A novel multi-input bidirectional LSTM and HMM based approach for target recognition from multi-domain radar range profiles
F Gao, T Huang, J Wang, J Sun, A Hussain, H Zhou – Electronics, 2019 – mdpi.com
… The shallow CNN is used for learning and dimensionality reduction of two-dimensional features in the time-frequency domain; the multi-input BLSTM is used for feature fusion of different data domains; and HMMs are used for target multi-aspect sequence feature learning and …
Analyzing Prosody with Legendre Polynomial Coefficients
R Rakov – 2019 – academicworks.cuny.edu
… spoken dialog systems) which are able to recognize sarcastic speech will be useful in the future … more and more from virtual assistance that use ASR and spoken dialogue systems … that can easily adapt to non-native speech, so that a spoken dialogue system could respond to its …
Ambient Assisted Living with Deep Learning
E Merdivan – 2019 – tel.archives-ouvertes.fr
… important components: improving activity recognition, addressing privacy concerns and developing intelligent dialogue systems for AAL systems, with an emphasis on a framework which is flexible and scalable for real-world applications. Page 20. Chapter 1. Introduction 3 …
Human interaction with shopping assistant robot in natural language
G Sidorov, I Markov, O Kolesnikova… – Journal of Intelligent …, 2019 – content.iospress.com
… obtain a goldstandard), feature selection and calculation of feature values (vector space construction), dimensionality reduction (eg, applying … Catizone , Machine learning approaches to human dialogue modeling, In Advances in Natural Multimodal Dialogue Systems, 2005, pp …
End-to-end facial and physiological model for\\Affective Computing and applications
J Comas, D Aspandi, X Binefa – arXiv preprint arXiv:1912.04711, 2019 – arxiv.org
… Thus, we may have a less noisy features. This has been shown to improve the model estimates on other computing fields, such as facial recognition [43], object classification [44], music generation [45], data compression [18], dimensionality reduction [19] etc …
Cross-lingual word embeddings
A Søgaard, I Vuli?, S Ruder… – Synthesis Lectures on …, 2019 – morganclaypool.com
… Semantic Role Labeling Martha Palmer, Daniel Gildea, and Nianwen Xue 2010 Spoken Dialogue Systems Kristiina Jokinen and Michael McTear 2009 Introduction to Chinese Natural Language Processing Kam-Fai Wong, Wenjie Li, Ruifeng Xu, and Zheng-sheng Zhang 2009 …
Deep Neural Networks for Selected Natural Language Processing Tasks
J Martínek – 2019 – dspace5.zcu.cz
Page 1. University of West Bohemia Department of Computer Science And Engineering Univerzitni 22 306 14 Plzen Czech Republic Deep Neural Networks for Selected Natural Language Processing Tasks PhD Study Report Ing. Jirí Martínek Technical Report No …
Efficient estimation of node representations in large graphs using linear contexts
T Pimentel, R Castro, A Veloso… – 2019 International Joint …, 2019 – ieeexplore.ieee.org
Page 1. Efficient Estimation of Node Representations in Large Graphs using Linear Contexts Tiago Pimentel CS Dept., UFMG & Kunumi Brazil tiago.pimentel@kunumi.com Rafael Castro CS Dept., UFMG Brazil rafael.castro@dcc.ufmg.br …
Recognizing induced emotions of movie audiences from multimodal information
M Muszynski, L Tian, C Lai, J Moore… – IEEE Transactions …, 2019 – ieeexplore.ieee.org
Page 1. 1949-3045 (c) 2018 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This …
Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Healthcare Applications: 10th International Conference, DHM …
VG Duffy – 2019 – books.google.com
… 391 Shota Nakatani, Sachio Saiki, Masahide Nakamura, and Kiyoshi Yasuda Design of Coimagination Support Dialogue System with Pluggable Dialogue System – Towards Long-Term Experiment …
Text Summarization for Chatbots
M Lustig – 2019 – support.dce.felk.cvut.cz
… Both approaches can however be combined. For example, the navigation between states of an automaton in the frame based dialogue system can be based on learned probabilities. The area of dialogue systems development resembles more an alchemy than a strict science …
A state-of-the-art survey on deep learning theory and architectures
MZ Alom, TM Taha, C Yakopcic, S Westberg, P Sidike… – Electronics, 2019 – mdpi.com
In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities …
A Lexical Resource-Constrained Topic Model for Word Relatedness
Y Yin, J Zeng, H Wang, K Wu, B Luo, J Su – IEEE Access, 2019 – ieeexplore.ieee.org
… Besides the capability of dimensionality reduction in word meaning representation, our model also utilizes external lexical resource to refine model training. This is achieved by incorporating word pairs that are known to be related as constraints in the learning process …
Adaptive and Personalized Systems Based on Semantics
P Lops, C Musto, F Narducci, G Semeraro – Semantics in Adaptive and …, 2019 – Springer
… As we introduced in Sect. 3.1 approaches for endogenous semantics representation exploit textual content and usually require dimensionality reduction techniques to obtain a more compact and (almost equivalent to the original one) representation of words …
Deep learning for multi-class identification from domestic violence online posts
S Subramani, S Michalska, H Wang, J Du… – IEEE …, 2019 – ieeexplore.ieee.org
… The unique characteristics of words embeddings such as automatic fea- tures extraction, semantic relationships retention and signif- icant dimensionality reduction overcome the drawbacks of the traditional features extraction such as sparsity and non- semantic representation …
Deep multigrained cascade forest for hyperspectral image classification
X Liu, R Wang, Z Cai, Y Cai… – IEEE Transactions on …, 2019 – ieeexplore.ieee.org
… First, principal component analysis (PCA) is used for dimensionality reduction on the whole original image con- taining labeled and unlabeled pixels in order to eliminate the influence of redundant information, as shown in Fig …
Speaker recognition with random digit strings using uncertainty normalized HMM-based i-vectors
N Maghsoodi, H Sameti, H Zeinali… – … /ACM Transactions on …, 2019 – ieeexplore.ieee.org
… of classes. Yet, in RSR2015 the number of training speakers is smaller than the i-vector dimension. To overcome this limitation and avoid dimensionality reduction we add a simple regulariza- tion term to Sb. The regularized …
Reinforcement learning in healthcare: A survey
C Yu, J Liu, S Nemati – arXiv preprint arXiv:1908.08796, 2019 – arxiv.org
… This adaptive closed-loop feature renders RL distinct from traditional supervised learning methods for regression or clas- sification, in which a list of correct labels must be provided, or from unsupervised learning approaches to dimensionality reduction or density estimation …
Homophily preserving community detection
F Ye, C Chen, Z Wen, Z Zheng… – IEEE Transactions on …, 2019 – ieeexplore.ieee.org
Page 1. This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Homophily Preserving Community Detection …
Ai & Quantum Computing For Finance & Insurance: Fortunes And Challenges For China And America
DKC Lee, P Schulte – 2019 – books.google.com
Page 1. Singapºre University ºf Sºcial Sciences – Wºrld Scientific : Future Economy Series : Al & Guantum Computing for Finance & Insurance Fortunes and Challenges for China and America Paul SCHULTE David LEE Kuo Chuen …
Robot Data-Driven Imitation Learning of Human Social Behavior with Time-Persistent Interaction Context
M Robert Doering – 2019 – ir.library.osaka-u.ac.jp
… agent domain collected in a Wizard-of-Oz setting and was designed for “adding mem- ory to goal-oriented dialog systems” [26] … Janarthanam and Lemon presented a finite-state based dialog system for setting up home broadband networks that learned to adapt to users with …
TUM Data Innovation Lab
H Agarwala, R Becker, M Fatima, L Riediger, A Belitski… – 2019 – di-lab.tum.de
Page 1. TECHNICAL UNIVERSITY OF MUNICH TUM Data Innovation Lab Development of an artificial conversation entity for continuous learning and adaption to user’s preferences and behavior Harshita Agarwala Robin Becker Mehnoor Fatima Lucian Riediger …
Adversarial learning in statistical classification: A comprehensive review of defenses against attacks
DJ Miller, Z Xiang, G Kesidis – arXiv preprint arXiv:1904.06292, 2019 – arxiv.org
Page 1. 1 Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks David J. Miller, Zhen Xiang, and George Kesidis Abstract With the wide deployment of machine learning (ML …
Rich and Scalable Models for Text
J Boyd-Graber, P Resnik – 2019 – drum.lib.umd.edu
Page 1. ABSTRACT Title of dissertation: RICH AND SCALABLE MODELS FOR TEXT Thang Dai Nguyen, Doctor of Philosophy, 2019 Dissertation directed by: Professor Jordan Boyd-Graber Department of Computer Science and Institute for Advanced Computer Studies …
Rich and Scalable Models for Text
T Dai Nguyen – 2019 – search.proquest.com
Page 1. ABSTRACT Title of dissertation: RICH AND SCALABLE MODELS FOR TEXT Thang Dai Nguyen, Doctor of Philosophy, 2019 Dissertation directed by: Professor Jordan Boyd-Graber Department of Computer Science and Institute for Advanced Computer Studies …
A Systematic Approach for Automatically Answering General-Purpose Objective and Subjective Questions
LP Acharya – 2019 – repository.lib.fit.edu
Page 1. A Systematic Approach for Automatically Answering General-Purpose Objective and Subjective Questions by Lok Prasad Acharya A dissertation submitted to Florida Institute of Technology in partial fulfillment of the requirements for the degree of …
Narrative Text Generation via Latent Embedding from Visual Stories
??? – 2019 – s-space.snu.ac.kr
… distances.1 The properties can be the distances in the lower-dimensional space (dimensionality reduction), the local distances (manifold learning) 2, the weights of links in graphs (graph embedding) (Goyal and Ferrara, 2018), the semantics …
O-ADPI: Online Adaptive Deep-Packet Inspector Using Mahalanobis Distance Map for Web Service Attacks Classification
M Kakavand, A Mustapha, Z Tan, SF Yazdani… – IEEE …, 2019 – ieeexplore.ieee.org
… The proposed O-ADPI model constructs a Unigram-based Weighting Scheme (UWS), a set of Payload Feature Con- struction (PFC), a Payload Weighting Scheme (PWS), and uses a dimensionality reduction technique based on the Prin- ciple Component Analysis (PCA) to …
Source Modeling Techniques for Quality Enhancement in Statistical Parametric Speech Synthesis
KS Rao, NP Narendra – 2019 – Springer
… To accomplish this task, the series will showcase the latest findings in speech technology, ranging from a comparative analysis of contemporary methods of speech parameterization to recent advances in commercial deployment of spoken dialog systems …
A Novel Methodology for Timely Brain Formations of 3D Spatial Information with Application to Visually Impaired Navigation
S Manganas – 2019 – rave.ohiolink.edu
Page 1. A NOVEL METHODOLOGY FOR TIMELY BRAIN FORMATIONS OF 3D SPATIAL INFORMATION WITH APPLICATION TO VISUALLY IMPAIRED NAVIGATION A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy By …
Representation Learning for Information Extraction
E Amjadian – 2019 – curve.carleton.ca
Page 1. Representation Learning for Information Extraction by Ehsan Amjadian A thesis submitted to the Faculty of Graduate and Postdoctoral Affairs in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Cognitive Science Carleton University …
Non-acted multi-view audio-visual dyadic interactions. Project non-verbal emotion recognition in dyadic scenarios and speaker segmentation
P Lázaro Herrasti – 2019 – diposit.ub.edu
Page 1 …
Efficient Algorithm for Answering Fact-based Queries Using Relational Data Enriched by Context-Based Embeddings
AA Altowayan – 2019 – webpage.pace.edu
Page 1. Efficient Algorithm for Answering Fact-based Queries Using Relational Data Enriched by Context-Based Embeddings by A. Aziz Altowayan December 12, 2019 Submitted in partial fulfillment of the requirements for the …
Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I
L Azzopardi, B Stein, N Fuhr, P Mayr, C Hauff… – 2019 – books.google.com
Page 1. Leif Azzopardi· Benno Stein · Norbert Fuhr· Philipp Mayr· Claudia Hauff· Djoerd Hiemstra (Eds.) Advances in Information Retrieval 41st European Conference on IR Research, ECIR 2019 Cologne, Germany, April 14–18, 2019 Proceedings, Part I 123 Page 2 …
Improving Software Defect Assignment Accuracy with the LSTM and Rule Engine Model
R Zhu – 2019 – search.proquest.com
Page 1. Improving Software Defect Assignment Accuracy With the LSTM and Rule Engine Model by Robert Zhu, BE, MS, MAS Submitted in partial fulfillment of the requirements for the degree of Doctor of Professional Studies in Computing at …
Distributed Moving Base Driving Simulators: Technology, Performance, and Requirements
A Andersson – 2019 – books.google.com
Page 1. Linköping Studies in Science and Technology Dissertations, No. 1984 Distributed Moving Base Driving Simulators Technology, Performance, and Requirements Anders Andersson Page 2. Linköping Studies in Science and Technology Dissertaons, No …
Multimodal Data Fusion Using Voice and Electromyography Data for Robotic Control
T Khan Mohd – 2019 – etd.ohiolink.edu
Page 1. A Thesis entitled Multimodal Data Fusion Using Voice and Electromyography Data for Robotic Control by Tauheed Khan Mohd Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Engineering …
Beyond Labels and Captions: Contextualizing Grounded Semantics for Explainable Visual Interpretation
SN Aakur – 2019 – search.proquest.com
… cast as a feed-forward neural network. GloVe, on the other hand, is a count-based model and as such, learn their representations through dimensionality reduction on a co-occurrence matrix. A large matrix is constructed based …