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Showing 1 to 20 of 23 for “"Word Representations"”.
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Neural Word Representations for Biomedical NLP
Word representations are mathematical objects which capture the semantic and syntactic properties of words in a way that is interpretable by machines. Recently, the encoding of word properties into a low-dimensional vector space using neural networks has become popular. Neural representations are …
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Word representations of bilingual adults and children
… the role of translation similarity in processing words in the two languages. Specifically, if processing of cognates (translations that share form) and non-cognates (translations with no form overlap) differ as a function of orthographic and phonological overlap between Greek and English. This …
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Automatic generation of tunable analogy benchmarks for word representations
… syntactic analogy datasets for the evaluation of word representations in an unsupervised manner. The automatic generation also allows for customization in terms of word-frequencies, syntactic rules, part-of-speech tags and size of the dataset. We show the ability of our method to generate …
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EVALUATING DISTRIBUTED WORD REPRESENTATIONS FOR PREDICTING MISSING WORDS IN SENTENCES
… recent years, the distributed representation of words in vector space or word embeddings have become very popular as they have shown significant improvements in many statistical natural language processing (NLP) tasks as compared to traditional language models like Ngram. In this thesis, we …
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Learning Morphology for Open-Vocabulary Neural Machine Translation
… have low accuracy in translating rare or unseen words due to the requirement of using a fixed-size word vocabulary during training. In addition to controlling the model complexity, this limitation is also related to the difficulty of learning accurate word representations under conditions of high …
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Representation Learning beyond Semantic Similarity: Character-aware and Function-specific Approaches
… 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 one of the central research areas in NLP. The induction of text …
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Enriching Word Representation Learning for Affect Detection and Affect-Aware Recommendations
… to improve affect detection in text by enhancing word representation learning. We enrich word representations in two ways: one by effective pre-processing of training word embeddings and second by incorporating both affective and contextual features deeply into text representations. We demonstrate …
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Geometries of word embeddings
Real-valued word embeddings have transformed natural language processing (NLP) applications, recognized for their ability to capture linguistic regularities. Popular examples are word2vec, GloVe, GPT and BERT. Both word2vec and GloVe are static whose word representations are independent of its …
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Inferring insulin regimen from clinical notes : using natural language processing techniques to extract data from free text records
… We also explore models using contextual word representations from the domain specific pretrained language models, character level embeddings and auxillary features constructed from external knowledge sources and analyze their performance. We find that our final Multi Layer Perceptron …
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Conditional Neural Language Models for Multimodal Learning and Natural Language Understanding
… neural language model for learning distributed representations of attributes and meta data. Our model allows for contextual word relatedness comparisons through decompositions of a word embedding tensor. Finally we show how we can abstract the skip-gram model for learning word representations to …
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Interpretable semantic representations from neural language models and computer vision
… semantic models is in the ambiguity of these representations, as the dimensions of these feature vectors are no longer characterised by clear, recognisable units of meaning. Even though these models have produced state-of-the-art performance on natural language tasks, it has come at the cost …
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Domain-agnostic named entity recognition on unstructured text
… compliment character embeddings to learn better word representations even with less training data. Experimental results demonstrate the effectiveness of our proposed domain-agnostic techniques on multiple datasets. We set the new state-of-the-art for BioNLP13CG and give a competitive performance …
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Representations from vision and language
… task. Achieving this requires building representations of concepts that manifest themselves visually, linguistically or through other senses. Furthermore concepts do not exist in isolation but are related to each other. In this work, we show how to build representations of concepts from …
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On the Evaluation and Modelling of Context-sensitive Lexical Semantics
A word can change its meaning in different contexts. The evaluation and the modelling of such contextual effect on lexical meaning are pivotal to natural language understanding. This thesis sets out to answer the following two-fold research question: (1) how can we design a reliable evaluation …
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Neural approaches to discourse coherence: modeling, evaluation and application
… score and the secondary task is to learn word-level syntactic features. Additionally, I examine the effect of using contextualised word representations in single-task and multi-task setups. I evaluate my models on a synthetic dataset where incoherent documents are created by shuffling the …
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Measuring and Manipulating State Representations in Neural Language Models
… states that LMs are simply modeling surface word co-occurrence statistics. However, we provide evidence for an alternative account (not mutually exclusive with the first): LMs represent and reason about the world they describe. In BART and T5 transformer LMs, we identify contextual word …
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MODELING THE LEADERSHIP OF LANGUAGE CHANGE FROM DIACHRONIC TEXT
… semantic leadership network using contextual word representations is proposed to investigate the link between semantic leadership and influence in the form of citations among publication venues that are part of the Association of Computational Linguistics. Taken together, these studies …
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Multilingual multitask joint neural information extraction
… of IE models. As most existing neural models use word embeddings as input features, they are sensitive to the quality of word representations. We investigate the possible factors that cause performance degradation when applying a name tagger to new data and tackle this issue from two aspects: 1. …
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Time series modeling of text data
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms
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Lexical entailment
… those that make use of knowledge bases such as WordNet. Interestingly, these methods make very different kinds of mistakes and so in this thesis, we construct a new entailment measure by combining these two paradigms in such a way that exploits their differences. We also experiment with …
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