Browsing by Subject "Parsing (Computer grammar)"
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Item Extensible language implementation(2002) Kolbly, Donovan Michael; Novak, Gordon S.Item Incremental nonmonotonic parsing through semantic self-organization(2003) Mayberry, Marshall Reeves; Miikkulainen, RistoSubsymbolic systems have been successfully used to model several aspects of human language processing. Subsymbolic parsers are appealing because they allow combining syntactic, semantic, and thematic constraints in sentence interpretation and nonmonotonically revising that interpretation while incrementally processing a sentence. Such parsers are also cognitively plausible: processing is robust and multiple interpretations are simultaneously activated when the input is ambiguous. Yet, it has proven very difficult to scale them up to realistic language. They have limited memory capacity, training takes a long time, and it is difficult to represent linguistic structure. A new connectionist model, INSOMNet, scales up the subsymbolic approach by utilizing semantic self-organization. INSOMNet was trained on semantic dependency graph representations from the recently-released LinGO Redwoods HPSG Treebank of sentences from the VerbMobil project. The results show that INSOMNet accurately learns to represent these semantic dependencies and generalizes to novel structures. Further evaluation of INSOMNet on the original VerbMobil sentences transcribed with annotations for spoken language demonstrates robust parsing of noisy input, while graceful degradation in performance from adding noise to the network weights underscores INSOMNet’s tolerance to damage. Finally, the cognitive plausibility of the model is shown on a standard psycholinguistic benchmark, in which INSOMNet demonstrates expectations and defaults, coactivation of multiple interpretations, nonmonotonicity, and semantic priming.Item Learning for semantic parsing and natural language generation using statistical machine translation techniques(2007) Wong, Yuk Wah, 1979-; Mooney, Raymond J. (Raymond Joseph)One of the main goals of natural language processing (NLP) is to build au- tomated systems that can understand and generate human lanugages. This goal has so far remained elusive. Existing hand-crafted systems can provide in-depth anal- ysis of domain sub-languages, but are often notoriously fragile and costly to build. Existing machine-learned systems are considerably more robust, but are limited to relatively shallow NLP tasks. In this thesis, we present novel statistical methods for robust natural lan- guage understanding and generation. We focus on two important sub-tasks, seman- tic parsing and tactical generation. The key idea is that both tasks can be treated as the translation between natural languages and formal meaning representation lan- guages, and therefore, can be performed using state-of-the-art statistical machine translation techniques. Specifically, we use a technique called synchronous pars- ing, which has been extensively used in syntax-based machine translation, as the unifying framework for semantic parsing and tactical generation. The parsing and generation algorithms learn all of their linguistic knowledge from annotated cor- pora, and can handle natural-language sentences that are conceptually complex. A nice feature of our algorithms is that the semantic parsers and tactical gen- erators share the same learned synchronous grammars. Moreover, charts are used as the unifying language-processing architecture for efficient parsing and generation. Therefore, the generators are said to be the inverse of the parsers, an elegant prop- erty that has been widely advocated. Furthermore, we show that our parsers and generators can handle formal meaning representation languages containing logical variables, including predicate logic. Our basic semantic parsing algorithm is called WASP. Most of the other parsing and generation algorithms presented in this thesis are extensions of WASP or its inverse. We demonstrate the effectiveness of our parsing and generation al- gorithms by performing experiments in two real-world, restricted domains. Ex- perimental results show that our algorithms are more robust and accurate than the currently best systems that require similar supervision. Our work is also the first attempt to use the same automatically-learned grammar for both parsing and gen- eration. Unlike previous systems that require manually-constructed grammars and lexicons, our systems require much less knowledge engineering and can be easily ported to other languages and domains.Item Learning for semantic parsing with kernels under various forms of supervision(2007) Kate, Rohit Jaivant, 1978-; Mooney, Raymond J. (Raymond Joseph)Semantic parsing involves deep semantic analysis that maps natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical for developing computing systems that understand natural language input. This thesis presents a new machine learning approach for semantic parsing based on string-kernel-based classification. It takes natural language sentences paired with their formal meaning representations as training data. For every production in the formal language grammar, a Support-Vector Machine (SVM) classifier is trained using string similarity as the kernel. Meaning representations for novel natural language sentences are obtained by finding the most probable semantic parse using these classi- fiers. This method does not use any hard-matching rules and unlike previous and other recent methods, does not use grammar rules for natural language, probabilistic or otherwise, which makes it more robust to noisy input. Besides being robust, this approach is also flexible and able to learn under a wide range of supervision, from extra to weaker forms of supervision. It can easily utilize extra supervision given in the form of syntactic parse trees for natural language sentences by using a syntactic tree kernel instead of a string kernel. Its learning algorithm can also take advantage of detailed supervision provided in the form of semantically augmented parse trees. A simple extension using transductive SVMs enables the system to do semi-supervised learning and improve its performance utilizing unannotated sentences which are usually easily available. Another extension involving EM-like retraining makes the system capable of learning under ambiguous supervision in which the correct meaning representation for each sentence is not explicitly given, but instead a set of possible meaning representations is given. This weaker and more general form of supervision is better representative of a natural training environment for a language-learning system requiring minimal human supervision. For a semantic parser to work well, conformity between natural language and meaning representation grammar is necessary. However meaning representation grammars are typically designed to best suit the application which will use the meaning representations with little consideration for how well they correspond to natural language semantics. We present approaches to automatically transform meaning representation grammars to make them more compatible with natural language semantics and hence more suitable for learning semantic parsers. Finally, we also show that ensembles of different semantic parser learning systems can obtain the best overall performance.