Class-based statistical models for lexical knowledge acquisition
This thesis is about the automatic acquisition of a particular kind of lexical knowledge, namely the knowledge of which noun senses can fill the argument slots of predicates. The knowledge is represented using probabilities, which agrees with the intuition that there are no absolute constraints on the arguments of predicates, but that the constraints are satisfied to a certain degree; thus the problem of knowledge acquisition becomes the problem of probability estimation from corpus data. The problem with defining a probability model in terms of senses is that this involves a huge number of parameters, which results in a sparse data problem. The proposal here is to define a probability model over senses in a semantic hierarchy, and exploit the fact that senses can be grouped into classes consisting of semantically similar senses. A novel class-based estimation technique is developed, together with a procedure that determines a suitable class for a sense (given a predicate and argument position). The problem of determining a suitable class can be thought of as finding a suitable level of generalisation in the hierarchy. The generalisation procedure uses a statistical test to locate areas consisting of semantically
Clark, Stephen
Computational linguistics
Class-based statistical models for lexical knowledge acquisition. This thesis is about the automatic acquisition of a particular kind of lexical knowledge, namely the knowledge of which noun senses can fill the argument slots of predicates. The knowledge is represented using probabilities, which agrees with the intuition that there are no absolute constraints on the arguments of predicates, but that the constraints are satisfied to a certain degree; thus the problem of knowledge acquisition becomes the problem of probability estimation from corpus data. The problem with defining a probability model in terms of senses is that this involves a huge number of parameters, which results in a sparse data problem. The proposal here is to define a probability model over senses in a semantic hierarchy, and exploit the fact that senses can be grouped into classes consisting of semantically similar senses. A novel class-based estimation technique is developed, together with a procedure that determines a suitable class for a sense (given a predicate and argument position). The problem of determining a suitable class can be thought of as finding a suitable level of generalisation in the hierarchy. The generalisation procedure uses a statistical test to locate areas consisting of semantically
class_based statistical_model lexical_knowledge acquisition automatic acquisition particular_kind lexical_knowledge namely knowledge noun sens fill argument slot predicate knowledge represent use probability agree intuition absolute constraint argument predicate constraint satisfy certain_degree thus problem knowledge_acquisition become problem probability estimation corpus_data problem define probability model term sense involves huge number parameter sparse data problem proposal define probability model sense semantic hierarchy exploit fact sens group class consist semantically similar sens novel class_based estimation technique develop together procedure determine suitable class sense give predicate_argument position problem determine suitable class think find suitable level generalisation hierarchy generalisation procedure use_statistical test locate area consist semantically