published_or_final_version / Computer Science and Information Systems / Doctoral / Doctor of Philosophy

Understanding interactions among words is fundamental for natural language applications. However, many statistical NLP methods still ignore this important characteristic of language. For example, information retrieval models still assume word independence. This work focuses on the creation of lexical affinity models and their applications to natural language problems. The thesis develops two approaches for computing lexical affinity. In the first, the co-occurrence frequency is the calculated by point estimation. The second uses parametric models for co-occurrence distances. For the point estimation approach, we study several alternative methods for computing the degree of affinity by making use of point estimates for co-occurrence frequency. We propose two new point estimators for co-occurrence and evaluate the measures and the estimation procedures with synonym questions. In our evaluation, synonyms are checked directly by their co-occurrence and also by comparing them indirectly, using other lexical units as supporting evidence. For the parametric approach, we address the creation of lexical affinity models by using two parametric models for distance co-occurrence: an independence model and an affinity model. The independence model is based on the geometric distribution; the affinity model is based on the gamma distribution. Both fit the data by maximizing likelihood. Two measures of affinity are derived from these parametric models and applied to the synonym questions, resulting in the best absolute performance on these questions by a method not trained to the task. We also explore the use of lexical affinity in information retrieval tasks. A new method to score missing terms by using lexical affinities is proposed. In particular, we adapt two probabilistic scoring functions for information retrieval to allow all query terms to be scored. One is a document retrieval method and the other is a passage retrieval method. Our new method, using replacement terms, shows significant improvement over the original methods.

Each individual has a distinguished writing style. But natural language generation systems pro- duce text with much less variety. Is it possible to produce more human-like text from natural language generation systems by mimicking the style of particular authors? We start by analysing the text of real authors. We collect a corpus of texts from a single genre (food recipes) with each text identified with its author, and summarise a variety of writing features in these texts. Each author's writing style is the combination of a set of features. Analysis of the writing features shows that not only does each individual author write differently but the differences are consistent over the whole of their corpus. Hence we conclude that authors do keep consistent style consisting of a variety of different features. When we discuss notions such as the style and meaning of texts, we are referring to the reac- tion that readers have to them. It is important, therefore, in the field of computational linguistics to experiment by showing texts to people and assessing their interpretation of the texts. In our research we move the thesis from simple discussion and statistical analysis of the properties of text and NLG systems, to perform experiments to verify the actual impact that lexical preference has on real readers. Through experiments that require participants to follow a recipe and prepare food, we conclude that it is possible to alter the lexicon of a recipe without altering the actions performed by the cook, hence that word choice is an aspect of style rather than semantics; and also that word choice is one of the writing features employed by readers in identifying the author of a text. Among all writing features, individual lexical preference is very important both for analysing and generating texts. So we choose individual lexical choice as our principal topic of research. Using a modified version of distributional similarity CDS) helps us to choose words used by in- dividual authors without the limitation of many other solutions such as a pre-built thesauri. We present an algorithm for analysis and rewriting, and assess the results. Based on the results we propose some further improvements.

Approved for public release; distribution is unlimited / Influencing one's adversary has always been an objective in warfare. However, to date the majority of influence operations have been geared toward the masses or to very small numbers of individuals. Although marginally effective, this approach is inadequate with respect to larger numbers of high value targets and to specific subsets of the population. Limited human resources have prevented a more tailored approach, which would focus on segmentation, because individual targeting demands significant time from psychological analysts. This research examined whether or not Information Technology (IT) tools, specializing in text mining, are robust enough to automate the categorization/segmentation of individual profiles for the purpose of psychological operations (PSYOP). Research indicated that only a handful of software applications claimed to provide adequate functionality to perform these tasks. Text mining via neural networks was determined to be the best approach given the constraints of the profile data and the desired output. Five software applications were tested and evaluated for their ability to reproduce the results of a social psychologist. Through statistical analysis, it was concluded that the tested applications are not currently mature enough to produce accurate results that would enable automated segmentation of individual profiles based on supervised linguistic processing. / Captain, United States Marine Corps / Lieutenant, United States Navy

This dissertation addresses an unanswered question in Arabic psycholinguistics. Arabic words are characterized by their nonconcatenative structure, in which a consonantal root that encodes the main semantic content is interleaved with a derivational pattern (called "binyan", pl. "binyanim"), which is typically vocalic but may also contain consonantal elements and contributes grammatical information. The canonical example of the Semitic root and binyan system is the combination of root /ktb/ which denotes the broad semantic sense of "writing" with verbal binyan /CaCaC/ (with Cindicating a root consonant) to form [katab] "he wrote" and with nominal place binyan/maCCaC/ to form [maktab] "office". Although significant work has been done on the psycholinguistic reality of Arabic morphemes by exploring various phonological, morphological and semantic features across numerous experimental modalities in both the visual and auditory domains (Boudelaa and Marslen-Wilson, 2004, 2005, 2011), no study has investigated the roles of base/morpheme frequency and surface/word frequency and their implications for underlying morphological structure in the lexicon of Arabic as has been done for English, Dutch, and Finnish (Baayen et al., 1997; Alegre and Gordon, 1999; New et al., 2004; Taft, 1979, 2004). Competing models of word recognition propose various integrations of morphology. Whole-word models suggest that there are no separate representations for morphemes, and that the co-activation of related words can be attributed to similarity in form and meaning (Norris and McQueen, 2008; Tyler et al., 1988). Decomposition models posit that words are recognized by accessing the words' constituent morphemes (Meunierand Segui, 1999; Taft et al., 1986; Wurm, 2000). Hybrid models incorporate multiple pathways to recognition. Words are either recognized holistically or by their constituent morphemes depending on multiple factors (Balling and Baayen, 2008; Taftand Nguyen-Hoan, 2010; Lopez-Villasenor, 2012). Of most relevance to the current study is the role of the productivity of the words' derivational affixes: words with unproductive affixes are processed holistically whereas words with productive affixes are processed as a function of their morphemes. This dissertation presents results from four auditory lexical decision experiments performed with native Jordanian speakers in Amman, Jordan, and provides evidence that binyan productivity determines whether the frequency of the base morpheme affects the speed of recognition. By manipulating root and word frequency for three binyanim, one more productive and two less productive, I provide evidence that verbs in the productive binyan are fully decomposable during lexical access and verbs in less productive binyanim are recognized holistically. For a more productive binyan, I examine Binyan I of the form /CaCaC/, and two less productive binyanim are Binyan VIII of the form /iCtaCaC/and Binyan X of the form /staCCaC/. These results together support a hybrid model of lexical access in which some words are recognized via decomposition into the morphemes they are composed of, and others are recognized by their whole word form. These results are consistent with those of Balling and Baayen (2008); Taft and Nguyen-Hoan (2010); Bertramet al. (2000), among others, as derivational affix productivity is the deciding factor determining whether a word will be recognized holistically or decomposed during lexical access.

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Leung Wai Kwong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 90-95). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Objective --- p.3 / Chapter 1.3 --- Organization of thesis --- p.3 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- Chinese Noun Phrases Parsing --- p.5 / Chapter 2.2 --- Chinese Noun Phrases --- p.6 / Chapter 2.3 --- Problems with Syntactic Parsing --- p.11 / Chapter 2.3.1 --- Conjunctive Noun Phrases --- p.11 / Chapter 2.3.2 --- De-de Noun Phrases --- p.12 / Chapter 2.3.3 --- Compound Noun Phrases --- p.13 / Chapter 2.4 --- Observations --- p.15 / Chapter 2.4.1 --- Inadequacy in Part-of-Speech Categorization for Chi- nese NLP --- p.16 / Chapter 2.4.2 --- The Need of Semantic in Noun Phrase Parsing --- p.17 / Chapter 2.5 --- Summary --- p.17 / Chapter 3 --- Hybrid Tag-set --- p.19 / Chapter 3.1 --- Objectives --- p.19 / Chapter 3.1.1 --- Resolving Parsing Ambiguities --- p.19 / Chapter 3.1.2 --- Investigation of Nominal Compound Noun Phrases --- p.20 / Chapter 3.2 --- Definition of Hybrid Tag-set --- p.20 / Chapter 3.3 --- Introduction to Cilin --- p.21 / Chapter 3.4 --- Problems with Cilin --- p.23 / Chapter 3.4.1 --- Unknown words --- p.23 / Chapter 3.4.2 --- Multiple Semantic Classes --- p.25 / Chapter 3.5 --- Introduction to Chinese Word Formation --- p.26 / Chapter 3.5.1 --- Disyllabic Word Formation --- p.26 / Chapter 3.5.2 --- Polysyllabic Word Formation --- p.28 / Chapter 3.5.3 --- Observation --- p.29 / Chapter 3.6 --- Automatic Assignment of Hybrid Tag to Chinese Word --- p.31 / Chapter 3.7 --- Summary --- p.34 / Chapter 4 --- Automatic Semantic Assignment --- p.35 / Chapter 4.1 --- Previous Researches on Semantic Tagging --- p.36 / Chapter 4.2 --- SAUW - Automatic Semantic Assignment of Unknown Words --- p.37 / Chapter 4.2.1 --- POS-to-SC Association (Process 1) --- p.38 / Chapter 4.2.2 --- Morphology-based Deduction (Process 2) --- p.39 / Chapter 4.2.3 --- Di-syllabic Word Analysis (Process 3 and 4) --- p.41 / Chapter 4.2.4 --- Poly-syllabic Word Analysis (Process 5) --- p.47 / Chapter 4.3 --- Illustrative Examples --- p.47 / Chapter 4.4 --- Evaluation and Analysis --- p.49 / Chapter 4.4.1 --- Experiments --- p.49 / Chapter 4.4.2 --- Error Analysis --- p.51 / Chapter 4.5 --- Summary --- p.52 / Chapter 5 --- Word Sense Disambiguation --- p.53 / Chapter 5.1 --- Introduction to Word Sense Disambiguation --- p.54 / Chapter 5.2 --- Previous Works on Word Sense Disambiguation --- p.55 / Chapter 5.2.1 --- Linguistic-based Approaches --- p.56 / Chapter 5.2.2 --- Corpus-based Approaches --- p.58 / Chapter 5.3 --- Our Approach --- p.60 / Chapter 5.3.1 --- Bi-gram Co-occurrence Probabilities --- p.62 / Chapter 5.3.2 --- Tri-gram Co-occurrence Probabilities --- p.63 / Chapter 5.3.3 --- Design consideration --- p.65 / Chapter 5.3.4 --- Error Analysis --- p.67 / Chapter 5.4 --- Summary --- p.68 / Chapter 6 --- Hybrid Tag-set for Chinese Noun Phrase Parsing --- p.69 / Chapter 6.1 --- Resolving Ambiguous Noun Phrases --- p.70 / Chapter 6.1.1 --- Experiment --- p.70 / Chapter 6.1.2 --- Results --- p.72 / Chapter 6.2 --- Summary --- p.78 / Chapter 7 --- Conclusion --- p.80 / Chapter 7.1 --- Summary --- p.80 / Chapter 7.2 --- Difficulties Encountered --- p.83 / Chapter 7.2.1 --- Lack of Training Corpus --- p.83 / Chapter 7.2.2 --- Features of Chinese word formation --- p.84 / Chapter 7.2.3 --- Problems with linguistic sources --- p.85 / Chapter 7.3 --- Contributions --- p.86 / Chapter 7.3.1 --- Enrichment to the Cilin --- p.86 / Chapter 7.3.2 --- Enhancement in syntactic parsing --- p.87 / Chapter 7.4 --- Further Researches --- p.88 / Chapter 7.4.1 --- Investigation into words that undergo semantic changes --- p.88 / Chapter 7.4.2 --- Incorporation of more information into the hybrid tag-set --- p.89 / Chapter A --- POS Tag-set by Tsinghua University (清華大學） --- p.96 / Chapter B --- Morphological Rules --- p.100 / Chapter C --- Syntactic Rules for Di-syllabic Words Formation --- p.104

Luk Po Chui. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 97-100). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Efficiency and Memory --- p.2 / Chapter 1.2 --- Ambiguity --- p.3 / Chapter 1.3 --- Robustness --- p.4 / Chapter 1.4 --- Thesis Organization --- p.5 / Chapter 2 --- Background --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Context-Free Grammars --- p.8 / Chapter 2.3 --- The LR Parsing Algorithm --- p.9 / Chapter 2.4 --- The Generalized LR Parsing Algorithm --- p.12 / Chapter 2.4.1 --- Graph-Structured Stack --- p.12 / Chapter 2.4.2 --- Packed Shared Parse Forest --- p.14 / Chapter 2.5 --- Time and Space Complexity --- p.16 / Chapter 2.6 --- Related Work on Parsing --- p.17 / Chapter 2.6.1 --- GLR* --- p.17 / Chapter 2.6.2 --- TINA --- p.18 / Chapter 2.6.3 --- PHOENIX --- p.19 / Chapter 2.7 --- Chapter Summary --- p.21 / Chapter 3 --- Grammar Partitioning --- p.22 / Chapter 3.1 --- Introduction --- p.22 / Chapter 3.2 --- Motivation --- p.22 / Chapter 3.3 --- Previous Work on Grammar Partitioning --- p.24 / Chapter 3.4 --- Our Grammar Partitioning Approach --- p.26 / Chapter 3.4.1 --- Definitions and Concepts --- p.26 / Chapter 3.4.2 --- Guidelines for Grammar Partitioning --- p.29 / Chapter 3.5 --- An Example --- p.30 / Chapter 3.6 --- Chapter Summary --- p.34 / Chapter 4 --- Parser Composition --- p.35 / Chapter 4.1 --- Introduction --- p.35 / Chapter 4.2 --- GLR Lattice Parsing --- p.36 / Chapter 4.2.1 --- Lattice with Multiple Granularity --- p.36 / Chapter 4.2.2 --- Modifications to the GLR Parsing Algorithm --- p.37 / Chapter 4.3 --- Parser Composition Algorithms --- p.45 / Chapter 4.3.1 --- Parser Composition by Cascading --- p.46 / Chapter 4 3.2 --- Parser Composition with Predictive Pruning --- p.48 / Chapter 4.3.3 --- Comparison of Parser Composition by Cascading and Parser Composition with Predictive Pruning --- p.54 / Chapter 4.4 --- Chapter Summary --- p.54 / Chapter 5 --- Experimental Results and Analysis --- p.56 / Chapter 5.1 --- Introduction --- p.56 / Chapter 5.2 --- Experimental Corpus --- p.57 / Chapter 5.3 --- ATIS Grammar Development --- p.60 / Chapter 5.4 --- Grammar Partitioning and Parser Composition on ATIS Domain --- p.62 / Chapter 5.4.1 --- ATIS Grammar Partitioning --- p.62 / Chapter 5.4.2 --- Parser Composition on ATIS --- p.63 / Chapter 5.5 --- Ambiguity Handling --- p.66 / Chapter 5.6 --- Semantic Interpretation --- p.69 / Chapter 5.6.1 --- Best Path Selection --- p.69 / Chapter 5.6.2 --- Semantic Frame Generation --- p.71 / Chapter 5.6.3 --- Post-Processing --- p.72 / Chapter 5.7 --- Experiments --- p.73 / Chapter 5.7.1 --- Grammar Coverage --- p.73 / Chapter 5.7.2 --- Size of Parsing Table --- p.74 / Chapter 5.7.3 --- Computational Costs --- p.76 / Chapter 5.7.4 --- Accuracy Measures in Natural Language Understanding --- p.81 / Chapter 5.7.5 --- Summary of Results --- p.90 / Chapter 5.8 --- Chapter Summary --- p.91 / Chapter 6 --- Conclusions --- p.92 / Chapter 6.1 --- Thesis Summary --- p.92 / Chapter 6.2 --- Thesis Contributions --- p.93 / Chapter 6.3 --- Future Work --- p.94 / Chapter 6.3.1 --- Statistical Approach on Grammar Partitioning --- p.94 / Chapter 6.3.2 --- Probabilistic modeling for Best Parse Selection --- p.95 / Chapter 6.3.3 --- Robust Parsing Strategies --- p.96 / Bibliography --- p.97 / Chapter A --- ATIS-3 Grammar --- p.101 / Chapter A.l --- English ATIS-3 Grammar Rules --- p.101 / Chapter A.2 --- Chinese ATIS-3 Grammar Rules --- p.104

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND HUMANITIES. / Includes bibliographies. / by Edward Palmer Stabler, Jr. / Ph.D.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by David Daniel McDonald. / Ph.D.