Global ETD Search
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Showing 1 to 10 of 10 for “"distributional semantics"”.
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Functional Distributional Semantics: Learning Linguistically Informed Representations from a Precisely Annotated Corpus
The aim of distributional semantics is to design computational techniques that can automatically learn the meanings of words from a body of text. The twin challenges are: how do we represent meaning, and how do we learn these representations? The current state of the art is to represent meanings as …
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Distributional semantic phrases vs. semantic distributional nonsense: Adjective Modification in Compositional Distributional Semantics
… thesis, I study the ability of compositional distributional semantics to model adjective modification. I present three studies that explore the degree to which semantic intuitions are grounded in the distributional representations of adjective-noun phrases, as well as provide insight into …
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Modeling False Memory in the Deese-Roediger-McDermott Paradigm: An Approach Using Holographic Declarative Memory
… Declarative Memory (HDM) model which integrates distributional semantics with cognitive control processes (HDM, Kelly et al., 2020) to simulate the DRM paradigm. We demonstrate that the model captures primacy and recency effects, semantically related intrusions, and the dynamic interaction …
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Predicting controlled vocabulary based on text and citations: Case studies in medical subject headings in MEDLINE and patents
… is a new partial matching measure based on distributional semantics. The second contribution is a probabilistic model based on text similarity and citations. The third contribution is a case study of cross-domain vocabulary prediction in US Patents. Medical subject headings (MeSH) are an …
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Transparent Analysis of Multi-Modal Embeddings
Vector Space Models of Distributional Semantics – or Embeddings – serve as useful statistical models of word meanings, which can be applied as proxies to learn about human concepts. One of their main benefits is that not only textual, but a wide range of data types can be mapped to a space, where …
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Understanding Semantic Implicit Learning through distributional linguistic patterns: A computational perspective
… but upon semantic-like knowledge gained through distributional analysis of massive linguistic input. Using methods borrowed from the machine learning and artificial intelligence literature, we construct computational models, which can simulate the performance observed during behavioural tasks of …
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Distributional graph: Connecting language towards a representation of knowledge and meaning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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Machine learning methods for vector-based compositional semantics
Rich semantic representations of linguistic data are an essential component to the development of machine learning algorithms for natural language processing. This thesis explores techniques to model the meaning of phrases and sentences as dense vectors, which can then be further analysed and …
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Cold-start universal information extraction
… elements by combining their symbolic and distributional semantics using unsupervised hierarchical clustering. How can machines benefit from available resources, e.g., large-scale ontologies or existing human annotations? My research has shown that pre-defined types can also be encoded by …
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Supporting word learning with language-internal distributional statistics: A place for the recurrent neural network language model?
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01