A unified framework to identify and extract uncertainty cues, holders, and scopes in one fell-swoop

Uncertainty refers to the language aspects that express hypotheses and speculations where propositions are held as (un)certain, (im)probable, or (im)possible. Automatic uncertainty analysis is crucial for several Natural Language Processing (NLP) applications that need to distinguish between factual (i.e. certain) and nonfactual (i.e. negated or uncertain) information. Typically, a comprehensive automatic uncertainty analyzer has three machine learning models for uncertainty detection, attribution, and scope extraction. To-date, and to the best of my knowledge, current research on uncertainty automatic analysis has only focused on uncertainty attribution and scope extraction, and has typically tackled each task with a different machine learning approach. Furthermore, current research on uncertainty automatic analysis has been restricted to specific languages, particularly English, and to specific linguistic genres, including biomedical and newswire texts, Wikipedia articles, and product reviews. In this research project, I attempt to address the aforementioned limitations of current research on automatic uncertainty analysis. First, I develop a machine learning model for uncertainty attribution, the task typically neglected in automatic uncertainty analysis. Second, I propose a unified framework to identify and extract uncertainty cues, holders, and scopes in one-fell swoop by casting each task as a supervised token sequence labeling problem. Third, I choose to work on the Arabic language, in contrast to English, the most commonly studied language in the literature of automatic uncertainty analysis. Finally, I work on the understudied linguistic genre of tweets. This research project results in a novel NLP tool, i.e., a comprehensive automatic uncertainty analyzer for Arabic tweets, with a practical impact on NLP applications that rely on uncertainty automatic analysis. The tool yields an F1 score of 0.759, averaged across its three machine learning models. Furthermore, through this research, the research community and I gain insights into (1) the challenges presented by Arabic as an agglutinative morphologically-rich language with a flexible word order, in contrast to English; (2) the challenges of the linguistic genre of tweets for uncertainty automatic analysis; and (3) the type of challenges that my proposed unified framework successfully addresses and boosts performance for

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

The Influence of CEO Narratives in Organizational Path Dependence

CEOs have long been considered the grand architects of firm strategy. Research not only supports their pivotal role in strategy making, but also suggests that CEOs directly influence outcomes of strategy. CEOs are often blamed for corporate failure and credited for corporate success. What is less clear is how CEOs influence strategy. Although research attributes the CEO’s influence on performance to demographics, personality and previous experience, how a CEO influences the firm’s strategic trajectory over time is less clear. -- This research investigates the role of CEO strategy narratives on the firm’s trajectory. Grounded in organizational path dependence theory, the thesis analyzes how CEO narratives, produced for both internal and external consumption, influence strategic direction. -- Organizational path dependence theory argues that the future is influenced by the past and that, over time, through a series of incremental decisions, options are reduced and outcomes are constrained. Core to the argument is the theorized existence of rent-seeking self-reinforcing social processes that may eventually lead the organization towards rigidity and an inefficient locked-in organizational state. -- Narrative in strategy is considered a primary means of sensemaking for organizational stakeholders. Narratives both represent and construct organizational reality by giving meaning to the past and setting expectations of the future, while creating and reinforcing organizational identity through the communication of norms, values, and beliefs. Strategy narratives focus on creating a discourse of direction to represent the past and influence the behaviour of organizational members. -- This thesis joins theory on organizational path dependence and strategy narratives to investigate the influence of CEO narratives on strategic lock-in. Through a historical document analysis, the thesis presents a case study of a Canadian-based multinational firm (Nortel) using strategy texts authored by the company’s four Chief Executive Officers from 2002 to 2010. The texts include public documents (shareholder letters, news releases, media articles, and analyst conference calls) along with a unique data set consisting of 168 CEO all-employee emails. -- Findings indicate that, collectively, CEO narratives act as a self-reinforcing process that encourages the adoption of other firm-level processes. CEO narratives represent a discourse of direction and a discourse of behaviour, to encourage both the understanding of firm strategy and the implementation of associated processes. Although the CEO narratives initially create benefits, over time they are constrained by what was said in the past and contribute toward the persistence of the firm’s organizational path

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia

Handbook of Lexical Functional Grammar

Lexical Functional Grammar (LFG) is a nontransformational theory of linguistic structure, first developed in the 1970s by Joan Bresnan and Ronald M. Kaplan, which assumes that language is best described and modeled by parallel structures representing different facets of linguistic organization and information, related by means of functional correspondences. This volume has five parts. Part I, Overview and Introduction, provides an introduction to core syntactic concepts and representations. Part II, Grammatical Phenomena, reviews LFG work on a range of grammatical phenomena or constructions. Part III, Grammatical modules and interfaces, provides an overview of LFG work on semantics, argument structure, prosody, information structure, and morphology. Part IV, Linguistic disciplines, reviews LFG work in the disciplines of historical linguistics, learnability, psycholinguistics, and second language learning. Part V, Formal and computational issues and applications, provides an overview of computational and formal properties of the theory, implementations, and computational work on parsing, translation, grammar induction, and treebanks. Part VI, Language families and regions, reviews LFG work on languages spoken in particular geographical areas or in particular language families. The final section, Comparing LFG with other linguistic theories, discusses LFG work in relation to other theoretical approaches

The Viet Nam Generation Big Book

An anthology of essays, narrative, poetry and graphics published in lieu of a 1993 issue of Viet Nam Generation, intended to be used as a textbook for teaching about the 1960s. Edited by Dan Duffy and Kali Tal. Contributing editors: Renny Christopher. David DeRose, Alan Farrell. Cynthia Fuchs, William M. King. Bill Shields, Tony Williams, and David Willson

Bowdoin Orient v.139, no.1-26 (2009-2010)

https://digitalcommons.bowdoin.edu/bowdoinorient-2010s/1000/thumbnail.jp