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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 18 of 18 1 Computational Analysis of Arguments and Persuasive Strategies in Political Discourse Naderi, Nona. - 2020 BASE Show details 2 Exploiting Linguistic Knowledge in Lexical and Compositional Semantic Models Wang, Tong. - 2017 BASE Show details 3 A Computational Model of the Acquisition of Mental State Verbs Barak, Libby Lea. - 2017 BASE Show details 4 Automatic Text and Speech Processing for the Detection of Dementia Fraser, Kathleen. - 2017 BASE Show details 5 Computational Modeling of Word Learning: The Role of Cognitive Processes Nematzadeh Chekoudar, Aida. - 2016 BASE Show details 6 Structured Approaches for Exploring Interpersonal Relationships in Natural Language Text Chaturvedi, Snigdha. - 2016 BASE Show details 7 RST-style Discourse Parsing and Its Applications in Discourse Analysis Feng, Vanessa Wei. - 2015 BASE Show details 8 Distributional Semantics for Robust Automatic Summarization Cheung, Jackie Chi Kit. - 2015 BASE Show details 9 Using Synchronized Audio Mapping to Predict Velar and Pharyngeal Wall Locations during Dynamic MRI Sequences Rahimian, Pooya. - : East Carolina University, 2013 BASE Show details 10 A Computational Theory of the Use-Mention Distinction in Natural Language Wilson, Shomir. - 2011 BASE Show details 11 A distributional and syntactic approach to fine-grained opinion mining Sayeed, Asad Basheer. - 2011 BASE Show details 12 The Circle of Meaning: From Translation to Paraphrasing and Back Madnani, Nitin. - 2010 BASE Show details 13 Combining Linguistic and Machine Learning Techniques for Word Alignment Improvement Ayan, Necip Fazil. - 2005 BASE Show details 14 Converting English text to speech : a machine learning approach Bakiri, Ghulum. - : Oregon State University BASE Show details 15 Hierarchical Bayesian Models of Verb Learning in Children Parisien, Christopher. - NO_RESTRICTION BASE Show details 16 Measuring Semantic Distance using Distributional Profiles of Concepts Mohammad, Saif. - NO_RESTRICTION BASE Show details 17 Computational Approaches to Style and the Lexicon Brooke, Julian. - NO_RESTRICTION BASE Show details 18 Exploiting Linguistic Knowledge to Infer Properties of Neologisms Cook, C. Paul. - NO_RESTRICTION Abstract: Neologisms, or newly-coined words, pose problems for natural language processing (NLP) systems. Due to the recency of their coinage, neologisms are typically not listed in computational lexicons---dictionary-like resources that many NLP applications depend on. Therefore when a neologism is encountered in a text being processed, the performance of an NLP system will likely suffer due to the missing word-level information. Identifying and documenting the usage of neologisms is also a challenge in lexicography, the making of dictionaries. The traditional approach to these tasks has been to manually read a lot of text. However, due to the vast quantities of text being produced nowadays, particularly in electronic media such as blogs, it is no longer possible to manually analyze it all in search of neologisms. Methods for automatically identifying and inferring syntactic and semantic properties of neologisms would therefore address problems encountered in both natural language processing and lexicography. Because neologisms are typically infrequent due to their recent addition to the language, approaches to automatically learning word-level information relying on statistical distributional information are in many cases inappropriate. Moreover, neologisms occur in many domains and genres, and therefore approaches relying on domain-specific resources are also inappropriate. The hypothesis of this thesis is that knowledge about etymology---including word formation processes and types of semantic change---can be exploited for the acquisition of aspects of the syntax and semantics of neologisms. Evidence supporting this hypothesis is found in three case studies: lexical blends (e.g., "webisode" a blend of "web" and "episode"), text messaging forms (e.g., "any1" for "anyone"), and ameliorations and pejorations (e.g., the use of "sick" to mean `excellent', an amelioration). Moreover, this thesis presents the first computational work on lexical blends and ameliorations and pejorations, and the first unsupervised approach to text message normalization. ; PhD Keyword: 0984; Computational linguistics; Computer science; Lexical acquisition; Natural language processing; Neologisms URL: http://hdl.handle.net/1807/26140 BASE Hide details

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