Resolving pronominal anaphora using commonsense knowledge

Coreference resolution is the task of resolving all expressions in a text that refer to the same entity. Such expressions are often used in writing and speech as shortcuts to avoid repetition. The most frequent form of coreference is the anaphor. To resolve anaphora not only grammatical and syntactical strategies are required, but also semantic approaches should be taken into consideration. This dissertation presents a framework for automatically resolving pronominal anaphora by integrating recent findings from the field of linguistics with new semantic features. Commonsense knowledge is the routine knowledge people have of the everyday world. Because such knowledge is widely used it is frequently omitted from social communications such as texts. It is understandable that without this knowledge computers will have difficulty making sense of textual information. In this dissertation a new set of computational and linguistic features are used in a supervised learning approach to resolve the pronominal anaphora in document. Commonsense knowledge sources such as ConceptNet and WordNet are used and similarity measures are extracted to uncover the elaborative information embedded in the words that can help in the process of anaphora resolution. The anaphoric system is tested on 350 Wall Street Journal articles from the BBN corpus. When compared with other systems available such as BART (Versley et al. 2008) and Charniak and Elsner 2009, our system performed better and also resolved a much wider range of anaphora. We were able to achieve a 92% F-measure on the BBN corpus and an average of 85% F-measure when tested on other genres of documents such as children stories and short stories selected from the web

Efficient Hardware Architecture for Correlation-Based Spike Detection and Unsupervised Clustering

This chapter presents a novel hardware architecture for correlation-based spike detection and unsupervised clustering. The architecture is able to utilize the information extracted from the results of spike clustering for efficient spike detection. The architecture supports the fast computation for the normalized correlation and OSORT operations. The normalized correlation is used for template matching for accurate spike detection. The OSORT algorithm is adopted for unsupervised classification of the detected spikes. The mean of spikes of each cluster produced by the OSORT algorithm is used as the templates for subsequent detection. The architecture adopts postnormalization technique for reducing the area costs. Modified OSORT operations are also proposed for facilitating unsupervised clustering by hardware. The proposed architecture is implemented by field programmable gate array (FPGA) for performance evaluation. In addition to attaining high detection and classification accuracy for spike sorting, experimental results reveal that the proposed architecture is an efficient design providing low area cost and high throughput for real-time offline spike sorting applications

A Stalnakerian Analysis of Metafictive Statements

Because Stalnaker’s common ground framework is focussed on cooperative information exchange, it is challenging to model fictional discourse. To this end, I develop an extension of Stalnaker’s analysis of assertion that adds a temporary workspace to the common ground. I argue that my framework models metafictive discourse better than competing approaches that are based on adding unofficial common grounds

Head-Driven Phrase Structure Grammar

Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)