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Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2707 (2022)
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Impact of Sentence Representation Matching in Neural Machine Translation
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1313 (2022)
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Phoneme-to-Audio Alignment with Recurrent Neural Networks for Speaking and Singing Voice
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In: Proceedings of Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03552964 ; Proceedings of Interspeech 2021, International Speech Communication Association, Aug 2021, Brno, Czech Republic. pp.61-65, ⟨10.21437/interspeech.2021-1676⟩ ; https://www.interspeech2021.org/ (2021)
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End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings
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In: ISBN: 978-1-6654-0019-0 ; 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII) ; https://hal.archives-ouvertes.fr/hal-03405970 ; 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII), Sep 2021, Nara, Japan ; https://www.acii-conf.net/2021/ (2021)
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| 5 |
Comparing Encoder-Decoder Architectures for Neural Machine Translation: A Challenge Set Approach ...
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Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic
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In: Applied Sciences ; Volume 11 ; Issue 22 (2021)
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Comparing Encoder-Decoder Architectures for Neural Machine Translation: A Challenge Set Approach
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Neural approach for Arabic sentiment analysis ; Une approche neuronale pour l’analyse d’opinions en arabe
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In: https://tel.archives-ouvertes.fr/tel-03084468 ; Informatique et langage [cs.CL]. Université du Maine; Université de Sfax (Tunisie), 2020. Français. ⟨NNT : 2020LEMA1022⟩ (2020)
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| 11 |
Dynamic Hand Gesture Recognition Based on a Leap Motion Controller and Two-Layer Bidirectional Recurrent Neural Network
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In: Sensors ; Volume 20 ; Issue 7 (2020)
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Generative Adversarial Network-Based Neural Audio Caption Model for Oral Evaluation
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In: Electronics ; Volume 9 ; Issue 3 (2020)
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| 13 |
Comparison of Word2vec with Hash2vec for Machine Translation
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In: Master's Projects (2020)
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| 14 |
Seq2Biseq: Bidirectional Output-wise Recurrent Neural Networks for Sequence Modelling
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In: CICLing 2019 - 20th International Conference on Computational Linguistics and Intelligent Text Processing ; https://hal.inria.fr/hal-02085093 ; CICLing 2019 - 20th International Conference on Computational Linguistics and Intelligent Text Processing, Apr 2019, La Rochelle, France (2019)
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| 15 |
Modeling Labial Coarticulation with Bidirectional Gated Recurrent Networks and Transfer Learning
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In: INTERSPEECH 2019 - 20th Annual Conference of the International Speech Communication Association ; https://hal.inria.fr/hal-02175780 ; INTERSPEECH 2019 - 20th Annual Conference of the International Speech Communication Association, Sep 2019, Graz, Austria (2019)
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Modeling the effects of entrenchment and memory development on second language acquisition ...
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| 17 |
Machine Learning Techniques for Detecting Identifying Linguistic Patterns in the News Media ...
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Machine Learning Techniques for Detecting Identifying Linguistic Patterns in the News Media ...
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| 19 |
Named entity recognition modeling for the Thai language from a disjointedly labeled corpus ...
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| 20 |
Multilabel text classification of public procurements using deep learning intent detection ; Textklassificering av offentliga upphandlingar med djupa artificiella neuronnät och avsåtsdetektering
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Abstract:
Textual data is one of the most widespread forms of data and the amount of such data available in the world increases at a rapid rate. Text can be understood as either a sequence of characters or words, where the latter approach is the most common. With the breakthroughs within the area of applied artificial intelligence in recent years, more and more tasks are aided by automatic processing of text in various applications. The models introduced in the following sections rely on deep-learning sequence-processing in order to process and text to produce a regression algorithm for classification of what the text input refers to. We investigate and compare the performance of several model architectures along with different hyperparameters. The data set was provided by e-Avrop, a Swedish company which hosts a web platform for posting and bidding of public procurements. It consists of titles and descriptions of Swedish public procurements posted on the website of e-Avrop, along with the respective category/categories of each text. When the texts are described by several categories (multi label case) we suggest a deep learning sequence-processing regression algorithm, where a set of deep learning classifiers are used. Each model uses one of the several labels in the multi label case, along with the text input to produce a set of text - label observation pairs. The goal becomes to investigate whether these classifiers can carry out different levels of intent, an intent which should theoretically be imposed by the different training data sets used by each of the individual deep learning classifiers. ; Data i form av text är en av de mest utbredda formerna av data och mängden tillgänglig textdata runt om i världen ökar i snabb takt. Text kan tolkas som en följd av bokstäver eller ord, där tolkning av text i form av ordföljder är absolut vanligast. Genombrott inom artificiell intelligens under de senaste åren har medfört att fler och fler arbetsuppgifter med koppling till text assisteras av automatisk textbearbetning. Modellerna som introduceras i denna uppsats är baserade på djupa artificiella neuronnät med sekventiell bearbetning av textdata, som med hjälp av regression förutspår tillhörande ämnesområde för den inmatade texten. Flera modeller och tillhörande hyperparametrar utreds och jämförs enligt prestanda. Datamängden som använts är tillhandahållet av e-Avrop, ett svenskt företag som erbjuder en webbtjänst för offentliggörande och budgivning av offentliga upphandlingar. Datamängden består av titlar, beskrivningar samt tillhörande ämneskategorier för offentliga upphandlingar inom Sverige, tagna från e-Avrops webtjänst. När texterna är märkta med ett flertal kategorier, föreslås en algoritm baserad på ett djupt artificiellt neuronnät med sekventiell bearbetning, där en mängd klassificeringsmodeller används. Varje sådan modell använder en av de märkta kategorierna tillsammans med den tillhörande texten, som skapar en mängd av text - kategori par. Målet är att utreda huruvida dessa klassificerare kan uppvisa olika former av uppsåt som teoretiskt sett borde vara medfört från de olika datamängderna modellerna mottagit.
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Keyword:
applied mathematics; artificiella neruonnät; deep learning; Maskininlärning; Natural language processing; Probability Theory and Statistics; recurrent neural network; Sannolikhetsteori och statistik; text classification; textklassificering; tillämpad matematik; word embedding
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URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252558
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