Globally distributed software teams are widespread throughout industry. But finding reliable methods that can properly assess a team's activities is a real challenge. Methods such as surveys and manual coding of activities are too time consuming and are often unreliable. Recent advances in information retrieval and linguistics, however, suggest that automated and/or semi-automated text classification algorithms could be an effective way of finding differences in the communication patterns among individuals and groups. Communication among group members is frequent and generates a significant amount of data. Thus having a web-based tool that can automatically analyze the communication patterns among global software teams could lead to a better understanding of group performance. The goal of this thesis, therefore, is to compare automatic and semi-automatic measures of communication and evaluate their effectiveness in classifying different types of group activities that occur within a global software development project. In order to achieve this goal, we developed a web-based component that can be used to help clean and classify communication activities. The component was then used to compare different automated text classification techniques on various group activities to determine their effectiveness in correctly classifying data from a global software development team project.

Last decade witnessed the rise of online social networks, especially Twitter. Today, Twitteris a giant social platform with over 250 million users |who produce massive amounts of data everyday. This creates many research opportunities, speci cally for Natural Language Processing (NLP) in which text is utilized to extract information that could be used in many applications. One problem NLP might help solving is geolocation inference or geolocation detection from online social networks. Detecting the location of Twitter users based on the text of their tweets is useful since not many users publicly declare their locations or geotag their tweets. Location information is crucial for a variety of applications such as event detection, disease and illness tracking and user pro ling. These tasks are not trivial, because online content is often noisy; it includes misspellings, incomplete words or phrases, idiomatic expressions, abbreviations, acronyms, and Twitter-speci c literature. In this work, we attempted to detect the location of Canadian users |and tweets sent from Canada |at metropolitan areas and province level; this was not done before, to the best of our knowledge. In order to do this, we collected two di erent datasets, and applied a variety of machine learning, including deep learning methods. Besides, we also attempted to geolocate users based on their social graph (i.e., user's friends and followers) as a novel approach.

No description available.

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

by Ho, Kei Shiu Edward. / "June 1996." / Thesis (Ph.D.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (p. 186-193). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.

Many state-of-the-art results in natural language processing (NLP) are achieved with statistical models involving latent variables. Unfortunately, computational problems associated with such models (for instance, finding the optimal parameter values) are typically intractable, forcing practitioners to rely on heuristic methods without strong guarantees. While heuristics are often sufficient for empirical purposes, their de-emphasis on theoretical aspects has certain negative ramifications. First, it can impede the development of rigorous theoretical understanding which can generate new ideas and algorithms. Second, it can lead to black art solutions that are unreliable and difficult to reproduce. In this thesis, we argue that spectral methods---that is, methods that use singular value decomposition or other similar matrix or tensor factorization---can effectively remedy these negative ramifications. To this end, we develop spectral methods for two unsupervised language processing tasks. The first task is learning lexical representations from unannotated text (e.g., hierarchical clustering of a vocabulary). The second task is estimating parameters of latent-variable models used in NLP applications (e.g., for unsupervised part-of-speech tagging). We show that our spectral algorithms have the following advantages over previous methods: 1. The algorithms provide a new theoretical framework that is amenable to rigorous analysis. In particular, they are shown to be statistically consistent. 2. The algorithms are simple to implement, efficient, and scalable to large amounts of data. They also yield results that are competitive with the state-of-the-art.

Concept-to-text generation suffers from what can be called generation bottlenecks - aspects of the generated text which should change for different subject domains, and which are usually hard to obtain or require manual work. Some examples are domain-specific content, a type system, a dictionary, discourse style and lexical style. These bottlenecks have stifled attempts to create generation systems that are generic, or at least apply to a wide range of domains in non-trivial applications. This thesis is comprised of two parts. In the first, we propose data-driven solutions that automate obtaining the information and models required to solve some of these bottlenecks. Specifically, we present an approach to mining domain-specific paraphrasal templates from a simple text corpus; an approach to extracting a domain-specific taxonomic thesaurus from Wikipedia; and a novel document planning model which determines both ordering and discourse relations, and which can be extracted from a domain corpus. We evaluate each solution individually and independently from its ultimate use in generation, and show significant improvements in each. In the second part of the thesis, we describe a framework for creating generation systems that rely on these solutions, as well as on hybrid concept-to-text and text-to-text generation, and which can be automatically adapted to any domain using only a domain-specific corpus. We illustrate the breadth of applications that this framework applies to with three examples: biography generation and company description generation, which we use to evaluate the framework itself and the contribution of our solutions; and justification of machine learning predictions, a novel application which we evaluate in a task-based study to show its importance to users.

Automatic reading comprehension (RC) systems integrate various kinds of natural language processing (NLP) technologies to analyze a given passage and generate or extract answers in response to questions about the passage. Previous work applied a lot of NLP technologies including shallow syntactic analyses (e.g. base noun phrases), semantic analyses (e.g. named entities) and discourse analyses (e.g. pronoun referents) in the bag-of-words (BOW) matching approach. This thesis proposes a novel RC approach that integrates a set of NLP technologies in a maximum entropy (ME) framework to estimate candidate answer sentences' probabilities being answers. In contrast to previous RC approaches, which are in English-only, the presented RC approach is the first one for both English and Chinese, the two languages used by most people in the world. In order to support the evaluation of the bilingual RC systems, a parallel English and Chinese corpus is also designed and developed. Annotations deemed relevant to the RC task are also included in the corpus. In addition, useful NLP technologies are explored from a new perspective---referring the pedagogical guidelines of humans, reading skills are summarized and mapped to various NLP technologies. Practical NLP technologies, categorized as shallow syntactic analyses (i.e. part-of-speech tags, voices and tenses) and deep syntactic analyses (i.e. syntactic parse trees and dependency parse trees) are then selected for integration. The proposed approach is evaluated on an English corpus, namely Remedia and our bilingual corpus. The experimental results show that our approach significantly improves the RC results on both English and Chinese corpora. / Xu, Kui. / Adviser: Helen Mei-Ling Meng. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3618. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 132-141). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Unsupervised approaches to learning the morphology of a language play an important role in computer processing of language from a practical and theoretical perspective, due their minimal reliance on manually produced linguistic resources and human annotation. Such approaches have been widely researched for the problem of concatenative affixation, but less attention has been paid to the intercalated (non-concatenative) morphology exhibited by Arabic and other Semitic languages. The aim of this research is to learn the root and pattern morphology of Arabic, with accuracy comparable to manually built morphological analysis systems. The approach is kept free from human supervision or manual parameter settings, assuming only that roots and patterns intertwine to form a word. Promising results were obtained by applying a technique adapted from previous work in concatenative morphology learning, which uses machine learning to determine relatedness between words. The output, with probabilistic relatedness values between words, was then used to rank all possible roots and patterns to form a lexicon. Analysis using trilateral roots resulted in correct root identification accuracy of approximately 86% for inflected words. Although the machine learning-based approach is effective, it is conceptually complex. So an alternative, simpler and computationally efficient approach was then devised to obtain morpheme scores based on comparative counts of roots and patterns. In this approach, root and pattern scores are defined in terms of each other in a mutually recursive relationship, converging to an optimized morpheme ranking. This technique gives slightly better accuracy while being conceptually simpler and more efficient. The approach, after further enhancements, was evaluated on a version of the Quranic Arabic Corpus, attaining a final accuracy of approximately 93%. A comparative evaluation shows this to be superior to two existing, well used manually built Arabic stemmers, thus demonstrating the practical feasibility of unsupervised learning of non-concatenative morphology.

Pang Chun Kiu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 101-107). / Chapter 1 --- Introduction --- p.2 / Chapter 1.1 --- Chinese Indexing using Noun Phrases --- p.6 / Chapter 1.2 --- Objectives --- p.8 / Chapter 1.3 --- An Overview of the Thesis --- p.8 / Chapter 2 --- Background --- p.10 / Chapter 2.1 --- Technology Influences on Information Retrieval --- p.10 / Chapter 2.2 --- Related Work --- p.13 / Chapter 2.2.1 --- Statistical/Keyword Approaches --- p.13 / Chapter 2.2.2 --- Syntactical approaches --- p.15 / Chapter 2.2.3 --- Semantic approaches --- p.17 / Chapter 2.2.4 --- Noun Phrases Approach --- p.18 / Chapter 2.2.5 --- Chinese Information Retrieval --- p.20 / Chapter 2.3 --- Our Approach --- p.21 / Chapter 3 --- Chinese Noun Phrases --- p.23 / Chapter 3.1 --- Different types of Chinese Noun Phrases --- p.23 / Chapter 3.2 --- Ambiguous noun phrases --- p.27 / Chapter 3.2.1 --- Ambiguous English Noun Phrases --- p.27 / Chapter 3.2.2 --- Ambiguous Chinese Noun Phrases --- p.28 / Chapter 3.2.3 --- Statistical data on the three NPs --- p.33 / Chapter 4 --- Index Extraction from De-de Conj. NP --- p.35 / Chapter 4.1 --- Word Segmentation --- p.36 / Chapter 4.2 --- Part-of-speech tagging --- p.37 / Chapter 4.3 --- Noun Phrase Extraction --- p.37 / Chapter 4.4 --- The Chinese noun phrase partial parser --- p.38 / Chapter 4.5 --- Handling Parsing Ambiguity --- p.40 / Chapter 4.6 --- Index Building Strategy --- p.41 / Chapter 4.7 --- The cross-set generation rules --- p.44 / Chapter 4.8 --- Example 1: Indexing De-de NP --- p.46 / Chapter 4.9 --- Example 2: Indexing Conjunctive NP --- p.48 / Chapter 4.10 --- Experimental results and Discussion --- p.49 / Chapter 5 --- Indexing Compound Nouns --- p.52 / Chapter 5.1 --- Previous Researches on Compound Nouns --- p.53 / Chapter 5.2 --- Indexing two-term Compound Nouns --- p.55 / Chapter 5.2.1 --- About the thesaurus《同義詞詞林》 --- p.56 / Chapter 5.3 --- Indexing Compound Nouns of three or more terms --- p.58 / Chapter 5.4 --- Corpus learning approach --- p.59 / Chapter 5.4.1 --- An Example --- p.60 / Chapter 5.4.2 --- Experimental Setup --- p.63 / Chapter 5.4.3 --- An Experiment using the third level of the Cilin --- p.65 / Chapter 5.4.4 --- An Experiment using the second level of the Cilin --- p.66 / Chapter 5.5 --- Contextual Approach --- p.68 / Chapter 5.5.1 --- The algorithm --- p.69 / Chapter 5.5.2 --- An Illustrative Example --- p.71 / Chapter 5.5.3 --- Experiments on compound nouns --- p.72 / Chapter 5.5.4 --- Experiment I: Word Distance Based Extraction --- p.73 / Chapter 5.5.5 --- Experiment II: Semantic Class Based Extraction --- p.75 / Chapter 5.5.6 --- Experiments III: On different boundaries --- p.76 / Chapter 5.5.7 --- The Final Algorithm --- p.79 / Chapter 5.5.8 --- Experiments on other compounds --- p.82 / Chapter 5.5.9 --- Discussion --- p.83 / Chapter 6 --- Overall Effectiveness --- p.85 / Chapter 6.1 --- Illustrative Example for the Integrated Algorithm --- p.86 / Chapter 6.2 --- Experimental Setup --- p.90 / Chapter 6.3 --- Experimental Results & Discussion --- p.91 / Chapter 7 --- Conclusion --- p.95 / Chapter 7.1 --- Summary --- p.95 / Chapter 7.2 --- Contributions --- p.97 / Chapter 7.3 --- Future Directions --- p.98 / Chapter 7.3.1 --- Word-sense determination --- p.98 / Chapter 7.3.2 --- Hybrid approach for compound noun indexing --- p.99 / Chapter A --- Cross-set Generation Rules --- p.108 / Chapter B --- Tag set by Tsinghua University --- p.110 / Chapter C --- Noun Phrases Test Set --- p.113 / Chapter D --- Compound Nouns Test Set --- p.124 / Chapter D.l --- Three-term Compound Nouns --- p.125 / Chapter D.1.1 --- NVN --- p.125 / Chapter D.1.2 --- Other three-term compound nouns --- p.129 / Chapter D.2 --- Four-term Compound Nouns --- p.133 / Chapter D.3 --- Five-term and six-term Compound Nouns --- p.134