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Showing 1 to 8 of 8 for “"Doc2Vec"”.
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Classifying Challenging Behaviors in Autism Spectrum Disorder with Neural Document Embeddings
… a TF-IDF weighted sum of Word2Vec embeddings, Doc2Vec embeddings which use hierarchical softmax as an output layer, and Doc2Vec which optimizes the original Doc2Vec architecture through Negative Sampling. Once created, these embeddings are initially used as input to a Support Vector Machine …
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A Computational Study in the Detection of English–Spanish Code-Switches
… detect code-switching. The third experiment uses Doc2Vec, where the model uses the mean vector of each document to learn and distinguish between code-switched and monolingual data. Each of these experiments are performed twice, once with tweets and once with the Miami Bangor corpus. The results …
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A Machine Learning Approach to Predicting Alcohol Consumption in Adolescents From Historical Text Messaging Data
… hardware and large data sets. Here we employ doc2vec, an unsupervised neural network, to capture the semantic content of text messages sent by adolescents during high school, and encode this semantic content as numeric vectors. These vectors effectively condense the text message data into …
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Modeling Similarities Among Autism Spectrum Patients Using Word Embeddings on Clinical Notes
… the challenging behaviors they present. We used Doc2Vec to create neural word embedding vectors on the clinical notes presented and K-means clustering to group the patients based on similarities in the notes. The clusters will give us greater insight into the examinations done by clinicians in …
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Query Expansion Study for Clinical Decision Support
… In addition, we have also considered using the doc2vec model to optimize retrieval. Finally, we have applied learning to rank which sorts documents based on relevance and put relevant documents in front of irrelevant documents, so as to return the relevant retrieved data on the top. We have …
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Robust Entity Linking in Heterogeneous Domains
… we present the neural-network-based approach Doc2Vec as a textual context matching technique for robust Entity Linking. Based on our previous findings and outcomes, our main contribution in this work is DoSeR (Disambiguation of Semantic Resources). DoSeR is a robust, knowledge-base-agnostic …
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Identifying Valuable Patents: A Deep Learning Approach
… title) into feature vectors using our developed Doc2Vec vector space model (VSM), that we assess using the t-distributed stochastic neighbour embedding (t-SNE) visualisation. The dataset is made publicly available for researchers to efficiently and effectively run fairly complex data analysis. We …
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Benchmarking authorship attribution techniques using over a thousand books by fifty Victorian era novelists
Authorship attribution (AA) is the process of identifying the author of a given text and from the machine learning perspective, it can be seen as a classification problem. In the literature, there are a lot of classification methods for which feature extraction techniques are conducted. In this …