Search for dissertations about: "distributional learning"
Showing result 1 - 5 of 18 swedish dissertations containing the words distributional learning.
-
1. Cross-language Ontology Learning : Incorporating and Exploiting Cross-language Data in the Ontology Learning Process
Abstract : An ontology is a knowledge-representation structure, where words, terms or concepts are defined by their mutual hierarchical relations. Ontologies are becoming ever more prevalent in the world of natural language processing, where we currently see a tendency towards using semantics for solving a variety of tasks, particularly tasks related to information access. READ MORE
-
2. Reinforcement Learning and Dynamical Systems
Abstract : This thesis concerns reinforcement learning and dynamical systems in finite discrete problem domains. Artificial intelligence studies through reinforcement learning involves developing models and algorithms for scenarios when there is an agent that is interacting with an environment. READ MORE
-
3. Ensembles of Semantic Spaces : On Combining Models of Distributional Semantics with Applications in Healthcare
Abstract : Distributional semantics allows models of linguistic meaning to be derived from observations of language use in large amounts of text. By modeling the meaning of words in semantic (vector) space on the basis of co-occurrence information, distributional semantics permits a quantitative interpretation of (relative) word meaning in an unsupervised setting, i. READ MORE
-
4. Splitting rocks: Learning word sense representations from corpora and lexica
Abstract : The representation of written language semantics is a central problem of language technology and a crucial component of many natural language processing applications, from part-of-speech tagging to text summarization. These representations of linguistic units, such as words or sentences, allow computer applications that work with language to process and manipulate the meaning of text. READ MORE
-
5. Decision-Making in Autonomous Driving using Reinforcement Learning
Abstract : The main topic of this thesis is tactical decision-making for autonomous driving. An autonomous vehicle must be able to handle a diverse set of environments and traffic situations, which makes it hard to manually specify a suitable behavior for every possible scenario. READ MORE