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Doelstelling: Het doel van deze masterproef is verder onderzoek te verrichten omtrent de Web People Search taak, meerbepaald naar de resultaten op een Nederlandstalig corpus. De Web People Search taak (WePS) wil het zoeken naar personen via een online zoekmachine vereenvoudigen. De vraag hiernaar is groot omdat heel veel personen dezelfde naam kunnen delen.Het effect van WePS op een online zoekmachine is het volgende: in plaats van een ongesorteerde mix van webpagina's over verschillende mensen die dezelfde naam delen - waarbij de gebruiker dan één voor één de zoekresultaten moet analyseren - is de output van WePS dan een verzameling van clusters, waarbij elke cluster de webpagina's bevat die gaan over één van de verschillende personen (die aan de zoekterm voldoen). De gebruiker kan dan de cluster van de gezochte persoon aanklikken waarna hij meteen alle relevante pagina's over die persoon ziet. Op deze manier kan de zoektocht naar informatie over een bepaalde persoon op het internet sterk vereenvoudigd worden. Middelen of methode: Voor dit onderzoek stelden we een Nederlandstalig corpus op. Dit corpus bestaat uit de eerste 50 zoekresultaten van de bekende online zoekmachine Google voor twee verschillende query's: "Jan De Cock" en "Jan Peeters". Het corpus bestaat dus in totaal uit 100 webpagina's. Deze werden gefilterd van alle "noise": html tags, java,...Daarna werden via TF-IDF frequency de belangrijkste kernwoorden van elke geëxtraheerd, aan de hand daarvan bouwden we een matrix op dat we konden gebruiken voor de automatische clustering. We evalueren per persoon 6 verschillende clusteringmethodes en bespreken daarvan de resultaten, vergeleken met een manueel geclusterde 'gold standard'. Resultaten: De beste resultaten werden voor beide personen behaald met de 'cosine distance'-metriek. Wat betreft het beste clusteringalgoritme, werden de beste resultaten behaald met 'group average' algoritme. We kunnen besluiten dat de resultaten op een Nederlandstalig corpus matig zijn indien we enkel gebruik maken van kernwoorden. Naar de toekomst toe moet men proberen andere middelen hieraan vast te koppelen die deze resultaten verbeteren, zoals bijvoorbeeld bepaling van locatie via het IP-adres of door meer belang te koppelen aan kernwoorden uit de 'snippets' van de zoekresultaten. Ook combinaties met (verwante) onderzoeksgebieden of taken zoals Named Entity Recognition en Coreference Resolution hebben het potentieel om de resultaten te verbeteren.

Clustering. --- Coreference Resolution. --- Information Retrieval. --- Named Entity Recognition. --- Natural Language Processing. --- TF-IDF frequency. --- Taaltechnologische studie. --- Web People Search. --- Word Sense Disambiguation.

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Current approaches to Natural Language Processing (NLP) have shown impressive improvements in many important tasks: machine translation, language modeling, text generation, sentiment/emotion analysis, natural language understanding, and question answering, among others. The advent of new methods and techniques, such as graph-based approaches, reinforcement learning, or deep learning, have boosted many NLP tasks to a human-level performance (and even beyond). This has attracted the interest of many companies, so new products and solutions can benefit from advances in this relevant area within the artificial intelligence domain.This Special Issue reprint, focusing on emerging techniques and trendy applications of NLP methods, reports on some of these achievements, establishing a useful reference for industry and researchers on cutting-edge human language technologies.

natural language processing --- distributional semantics --- machine learning --- language model --- word embeddings --- machine translation --- sentiment analysis --- quality estimation --- neural machine translation --- pretrained language model --- multilingual pre-trained language model --- WMT --- neural networks --- recurrent neural networks --- named entity recognition --- multi-modal dataset --- Wikimedia Commons --- multi-modal language model --- concreteness --- curriculum learning --- electronic health records --- clinical text --- relationship extraction --- text classification --- linguistic corpus --- deception --- linguistic cues --- statistical analysis --- discriminant function analysis --- fake news detection --- stance detection --- social media --- abstractive summarization --- monolingual models --- multilingual models --- transformer models --- transfer learning --- discourse analysis --- problem–solution pattern --- automatic classification --- machine learning classifiers --- deep neural networks --- question answering --- machine reading comprehension --- query expansion --- information retrieval --- multinomial naive bayes --- relevance feedback --- cause-effect relation --- transitive closure --- word co-occurrence --- automatic hate speech detection --- multisource feature extraction --- Latin American Spanish language models --- fine-grained named entity recognition --- k-stacked feature fusion --- dual-stacked output --- unbalanced data problem --- document representation --- semantic analysis --- conceptual modeling --- universal representation --- trend analysis --- topic modeling --- Bert --- geospatial data technology and application --- attention model --- dual multi-head attention --- inter-information relationship --- question difficult estimation --- named-entity recognition --- BERT model --- conditional random field --- pre-trained model --- fine-tuning --- feature fusion --- attention mechanism --- task-oriented dialogue systems --- Arabic --- multi-lingual transformer model --- mT5 --- language marker --- mental disorder --- deep learning --- LIWC --- spaCy --- RobBERT --- fastText --- LIME --- conversational AI --- intent detection --- slot filling --- retrieval-based question answering --- query generation --- entity linking --- knowledge graph --- entity embedding --- global model --- DISC model --- personality recognition --- predictive model --- text analysis --- data privacy --- federated learning --- transformer --- n/a --- problem-solution pattern

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Current approaches to Natural Language Processing (NLP) have shown impressive improvements in many important tasks: machine translation, language modeling, text generation, sentiment/emotion analysis, natural language understanding, and question answering, among others. The advent of new methods and techniques, such as graph-based approaches, reinforcement learning, or deep learning, have boosted many NLP tasks to a human-level performance (and even beyond). This has attracted the interest of many companies, so new products and solutions can benefit from advances in this relevant area within the artificial intelligence domain.This Special Issue reprint, focusing on emerging techniques and trendy applications of NLP methods, reports on some of these achievements, establishing a useful reference for industry and researchers on cutting-edge human language technologies.

Technology: general issues --- History of engineering & technology --- natural language processing --- distributional semantics --- machine learning --- language model --- word embeddings --- machine translation --- sentiment analysis --- quality estimation --- neural machine translation --- pretrained language model --- multilingual pre-trained language model --- WMT --- neural networks --- recurrent neural networks --- named entity recognition --- multi-modal dataset --- Wikimedia Commons --- multi-modal language model --- concreteness --- curriculum learning --- electronic health records --- clinical text --- relationship extraction --- text classification --- linguistic corpus --- deception --- linguistic cues --- statistical analysis --- discriminant function analysis --- fake news detection --- stance detection --- social media --- abstractive summarization --- monolingual models --- multilingual models --- transformer models --- transfer learning --- discourse analysis --- problem–solution pattern --- automatic classification --- machine learning classifiers --- deep neural networks --- question answering --- machine reading comprehension --- query expansion --- information retrieval --- multinomial naive bayes --- relevance feedback --- cause-effect relation --- transitive closure --- word co-occurrence --- automatic hate speech detection --- multisource feature extraction --- Latin American Spanish language models --- fine-grained named entity recognition --- k-stacked feature fusion --- dual-stacked output --- unbalanced data problem --- document representation --- semantic analysis --- conceptual modeling --- universal representation --- trend analysis --- topic modeling --- Bert --- geospatial data technology and application --- attention model --- dual multi-head attention --- inter-information relationship --- question difficult estimation --- named-entity recognition --- BERT model --- conditional random field --- pre-trained model --- fine-tuning --- feature fusion --- attention mechanism --- task-oriented dialogue systems --- Arabic --- multi-lingual transformer model --- mT5 --- language marker --- mental disorder --- deep learning --- LIWC --- spaCy --- RobBERT --- fastText --- LIME --- conversational AI --- intent detection --- slot filling --- retrieval-based question answering --- query generation --- entity linking --- knowledge graph --- entity embedding --- global model --- DISC model --- personality recognition --- predictive model --- text analysis --- data privacy --- federated learning --- transformer --- n/a --- problem-solution pattern

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This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains.

Information technology industries --- Computer science --- tourism big data --- text mining --- NLP --- deep learning --- clinical named entity recognition --- information extraction --- multitask model --- long short-term memory --- conditional random field --- relation extraction --- entity recognition --- long short-term memory network --- multi-turn chatbot --- dialogue context encoding --- WGAN-based response generation --- BERT word embedding --- text summary --- reinforce learning --- FAQ classification --- encoder-decoder neural network --- multi-level word embeddings --- BERT --- bidirectional RNN --- cloze test --- Korean dataset --- machine comprehension --- neural language model --- sentence completion --- primary healthcare --- chief complaint --- virtual medical assistant --- spoken natural language --- disease diagnosis --- medical specialist --- protein-protein interactions --- deep learning (DL) --- convolutional neural networks (CNN) --- bidirectional long short-term memory (bidirectional LSTM) --- dialogue management --- user simulation --- reward shaping --- conversation knowledge --- multi-agent reinforcement learning --- language modeling --- classification --- error probability --- error assessment --- logic error --- neural network --- LSTM --- attention mechanism --- programming education --- neural architecture search --- word ordering --- Korean syntax --- adversarial attack --- adversarial example --- sentiment classification --- dual pointer network --- context-to-entity attention --- text classification --- rule-based --- word embedding --- Doc2vec --- paraphrase identification --- encodings --- R-GCNs --- contextual features --- sentence retrieval --- TF−ISF --- BM25 --- partial match --- sequence similarity --- word to vector --- word embeddings --- antonymy detection --- polarity --- text normalization --- natural language processing --- deep neural networks --- causal encoder --- question classification --- multilingual --- convolutional neural networks --- Natural Language Processing (NLP) --- transfer learning --- open information extraction --- recurrent neural networks --- bilingual translation --- speech-to-text --- LaTeX decompilation --- word representation learning --- word2vec --- sememes --- structural information --- sentiment analysis --- zero-shot learning --- news analysis --- cross-lingual classification --- multilingual transformers --- knowledge base --- commonsense --- sememe prediction --- attention model --- ontologies --- fixing ontologies --- quick fix --- quality metrics --- online social networks --- rumor detection --- Cantonese --- XGA model --- delayed combination --- CNN dictionary --- named entity recognition --- deep learning NER --- bidirectional LSTM CRF --- CoNLL --- OntoNotes --- toxic comments --- neural networks

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The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved state-of-the-art applications in many fields, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks has been devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. This book gives significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications.

Information technology industries --- facial image analysis --- facial nerve paralysis --- deep convolutional neural networks --- image classification --- Chinese text classification --- long short-term memory --- convolutional neural network --- Arabic named entity recognition --- bidirectional recurrent neural network --- GRU --- LSTM --- natural language processing --- word embedding --- CNN --- object detection network --- attention mechanism --- feature fusion --- LSTM-CRF model --- elements recognition --- linguistic features --- POS syntactic rules --- action recognition --- fused features --- 3D convolution neural network --- motion map --- long short-term-memory --- tooth-marked tongue --- gradient-weighted class activation maps --- ship identification --- fully convolutional network --- embedded deep learning --- scalability --- gesture recognition --- human computer interaction --- alternative fusion neural network --- deep learning --- sentiment attention mechanism --- bidirectional gated recurrent unit --- Internet of Things --- convolutional neural networks --- graph partitioning --- distributed systems --- resource-efficient inference --- pedestrian attribute recognition --- graph convolutional network --- multi-label learning --- autoencoders --- long-short-term memory networks --- convolution neural Networks --- object recognition --- sentiment analysis --- text recognition --- IoT (Internet of Thing) systems --- medical applications