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 20 of 66 for “"language modeling"”.
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Adaptive statistical language modeling
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
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Language Modeling for limited-data domains
… focus of speech recognition and natural language processing applications on domains with limited amount of in-domain training data, enhanced system performance often relies on approaches involving model adaptation and combination. In such domains, language models are often constructed by …
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Language Modeling from Visually Grounded Speech
Recent advancements in spoken language processing have significantly reduced automatic speech recognition (ASR) error rates, driven by large-scale supervised training on paired speech–text data and, more recently, self-supervised pre-training on unpaired speech and audio. These methods have …
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Higher Order Recurrent Neural Network for Language Modeling
… In this work, we have examined HORNNs for the language modeling task using two popular data sets, namely the Penn Treebank (PTB) and English text8 data sets. Experimental results have shown that the proposed HORNNs yield the state-of-the-art performance on both data sets, significantly …
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Noisy language modeling framework using neural network techniques
… research develops a novel intermediate layer language modeling framework called ALMIL (i. e. Adaptive Language Modelling Intermediate Layer) which is seen as a communication language layer between human and computer to analyze noisy language stream and provide users with two fundamental …
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Evaluating pre-trained language modeling approaches for author name disambiguation
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01
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Non-linguistic Notions in Language Modeling: Learning, Retention, and Applications
Language modeling, especially through the use of transformer-based large language models (LLMs), has drastically changed how we view and use artificial intelligence (AI) and machine learning (ML) in our daily lives. Although LLMs have showcased remarkable linguistic proficiency in their abilities …
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BantuBERTa : using language family grouping in multilingual language modeling for Bantu languages
… were used. The resulting multilingual language model (BantuBERTa) from this pretraining data proved to be predictive across multiple Bantu languages on a higher-order NLP task (NER) and in a simpler NLP task (classification). This proves that this dataset can be used for Bantu …
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Lexical and Language Modeling of Diacritics and Morphemes in Arabic Automatic Speech Recognition
Arabic is a morphologically rich language which rarely displays diacritics. These two features of the language pose challenges when building Automatic Speech Recognition (ASR) systems. Morphological complexity leads to many possible combinations of stems and affixes to form words, and produces …
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Improving the effectiveness of language modeling approaches to information retrieval: bridging the theory-effectiveness gap
… model would benefit every search engine. The language modeling approach to information retrieval has recently attracted much attention. In the language modeling approach, we assume that a query is a sample drawn from a language model: given a query Q and a document D, we compute the likelihood …
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NeuroYara: Learning to Rank for Yara Rules Generation through Deep Language Modeling & Discriminative N-gram Encoding
Signature-based malware detection methods are simple, explainable, and efficient. One of the most ubiquitous tools is Yara. It is a widely-used syntax for writing malware signatures. Compared to machine learning models, Yara rules have a lower false-positive rate and better maintainability of the …
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The use of aided language modeling to support social interactions of children who use augmentative and alternative communication: training and coaching siblings
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms
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Neural attentions for natural language understanding and modeling
… attention mechanisms for improving natural language representation learning, a fundamental concept for modern natural language processing. With the proposed attention algorithms, our model made significant improvements in both language modeling and natural language understanding tasks. We …
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A theory of (almost) zero resource speech recognition
… popularity, however, is still limited to languages such as English, Japanese, and German, where vast amounts of labeled training data are available. For most other languages, it is prohibitively expensive to 1) collect and transcribe the speech data required to learn good acoustic models; …
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Learning the language of biomolecular interactions
… critical to develop computational approaches for modeling these interactions. Unsupervised language models trained on amino acid sequences, namely protein language models, learn patterns in sequence evolution that encode protein structure and function. These protein language models are thus a …
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Structurally Motivated Deep Learning for Genome Scale Protein Interaction Prediction
… across species. We combine advances in neural language modeling and structurally-motivated design to develop D-SCRIPT, a deep learning model which is interpretable and generalizable to species with limited training data. We show that a D-SCRIPT model trained on 38,345 human PPIs enables …
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Prosody Dependent Speech Recognition on American Radio News Speech
… interaction between the acoustic model and the language model. We conduct detailed experiments to determine the set of allophonic HMMs or probability distributions that are sensitive to prosody, under the guidance of linguistic prior knowledge and empirical selection rules. We measure the …
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Representation Learning beyond Semantic Similarity: Character-aware and Function-specific Approaches
… area within machine learning and natural language processing (NLP) concerned with building machine-understandable representations of discrete units of text. Continuous representations are at the core of modern machine learning applications, and representation learning has thereby become …
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Learning deep patient representations for the teleICU
… by recent machine learning literature in language modeling. The utility of these representations is evaluated in various prediction outcome tasks, in which they were able to outperform linear and neural baselines. Also examined are the probability distributions of various patient …
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Crosslingual Sharing for Low-Resource Natural Language Processing
… successful at a wide variety of tasks, including language modeling and structured prediction problems such as syntactic and semantic parsing. This is due in large part to the use of supervised neural networks and more recently to unsupervised contextualized representations. However, these …
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