Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 14 of 14 for “"Sentence Embeddings"”.
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AI-Based Speech Assessment of Cognitive Impairment Disorders
… with a root mean squared error of 4.56, using sentence embeddings. We present the Crowdsourced Language Assessment Corpus (CLAC), a corpus that we created to provide the community with a collection of audio samples from various speakers that can be used to learn a general representation for …
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Contextual topics: advancing text segmentation through pre-trained models and contextual keywords
… of BERT. It is also found that infusing sentence embeddings with unsupervised features, such as the ones gathered from Latent Dirichlet Allocation (LDA), provides comparable results to current state-of-the-art (SOTA) TS systems. In addition to this, unsupervised features derived from LDA …
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Pairwise embedding for event coreference resolution
… These features include but are not limited to sentence embeddings, trigger embeddings, and argument type representations. The extracted features are then used to fine-tune a pre-trained language model, BERT (Bidirectional Encoder Representations from Transformers), to perform event coreference …
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Automatic Summarization of Financial Reports
… with various non-parametric methods to generate sentence vectors which can facilitate the efficient selection of important sentences in the document as part of the summarization algorithm. We illustrate the superiority of a financially tuned language model. Further, it is shown that simple …
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Hollywood Workers vs Tech: In Theory and In the News
… processing techniques. Sentiment analysis and sentence embeddings are used to quantify and compare news articles across different characteristics. The results of the analysis are inconclusive.
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Weakly supervised learning from referring expression: Challenge and directions
… expression task is performed associated with a sentence phrase, e.g. “the dude on the dolphin”. Previous approaches use LSTM and fully convolutional network and have fairly good results under fully supervised setting. However, the fully supervised setting is limited by manual labeling of …
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Natural Language Financial Forecasting: The South African Context
… techniques were replaced with state-of-the-art sentence embeddings. Deep learning models (Deep Neural Network (DNN)) were then compared to Classical Models (Logistic Regression (LR)). These models were trained, optimized and deployed using the Tensorflow Machine Learning (ML) framework on Google …
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Towards Explainable Event Detection and Extraction
… events of interest and from those extracting the sentences that might contain the event related information. This task is challenging because it is easier to obtain labels at the document level than finegrained annotations at the sentence level. Current approaches for this task are suboptimal …
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On the Ethics and Linguistic Impacts of Using the Bible as Training Data for Yucatec Maya-to-Spanish Machine Translation
… a semantic similarity analysis using BETO sentence embeddings, and a Bible n-gram contamination analysis using log-likelihood ratio statistics. Results show that increasing the proportion of Bible training data consistently degraded translation quality across both models. For …
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Improving Reader Motivation with Machine Learning
… learning, deep neural networks, and sentence embeddings. This thesis, presented in an integrated-article format, contains three core contributions among its three articles. In the first article, we propose a flexible and insightful approach to coherence estimation. This approach uses …
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Multiple Choice Question Answering using a Large Corpus of Information
… 2 I review popular algorithms to create word and sentence embeddings given the surrounding context. These embeddings are a numerical representation of the language data that can be used in downhill models such as logistic regression. In Chapter 3 I present a novel method to create a domain specific …
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Injecting Inductive Biases into Distributed Representations of Text
… vector representations of text (a.k.a. embeddings), learned by neural networks, encode various (linguistic) knowledge. To encode this knowledge into the embeddings the common approach is to train a large neural network on large corpora. There is, however, a growing concern regarding the …
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Text Generation and Evaluation for Human-Machine Collaborative Writing
… time-consuming. We introduce methods based on sentence mover's similarity; our automatic metrics evaluate text in a continuous space using word and sentence embeddings. We find that sentence-based metrics correlate with human judgments significantly better than Rouge and can be used as a reward …
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Exploring Loss Functions in Machine Learning
… of contrastive loss in large document embeddings. Existing model pretraining methods primarily focus on local information. For instance, in the widely used token masking strategy, words closer to the masked token are given more importance for prediction than words further away. While …