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Measuring Semantic Relatedness Using Salient Encyclopedic Concepts

Description: While pragmatics, through its integration of situational awareness and real world relevant knowledge, offers a high level of analysis that is suitable for real interpretation of natural dialogue, semantics, on the other end, represents a lower yet more tractable and affordable linguistic level of analysis using current technologies. Generally, the understanding of semantic meaning in literature has revolved around the famous quote ``You shall know a word by the company it keeps''. In this thesis we investigate the role of context constituents in decoding the semantic meaning of the engulfing context; specifically we probe the role of salient concepts, defined as content-bearing expressions which afford encyclopedic definitions, as a suitable source of semantic clues to an unambiguous interpretation of context. Furthermore, we integrate this world knowledge in building a new and robust unsupervised semantic model and apply it to entail semantic relatedness between textual pairs, whether they are words, sentences or paragraphs. Moreover, we explore the abstraction of semantics across languages and utilize our findings into building a novel multi-lingual semantic relatedness model exploiting information acquired from various languages. We demonstrate the effectiveness and the superiority of our mono-lingual and multi-lingual models through a comprehensive set of evaluations on specialized synthetic datasets for semantic relatedness as well as real world applications such as paraphrase detection and short answer grading. Our work represents a novel approach to integrate world-knowledge into current semantic models and a means to cross the language boundary for a better and more robust semantic relatedness representation, thus opening the door for an improved abstraction of meaning that carries the potential of ultimately imparting understanding of natural language to machines.
Date: August 2011
Creator: Hassan, Samer
Partner: UNT Libraries

Rhythms of Interaction in Global Software Development Teams

Description: Researchers have speculated that global software teams have activity patterns that are dictated by work-place schedules or a client's need. Similar patterns have been suggested for individuals enrolled in distant learning projects that require students to post feedback in response to questions or assignments. Researchers tend to accept the notion that students' temporal patterns adjust to academic or social calendars and are a result of choices made within these constraints. Although there is some evidence that culture do have an impact on communication activity behavior, there is not a clear how each of these factors may relate to work done in online groups. This particular study represents a new approach to studying student-group communication activities and also pursues an alternative approach by using activity data from students participating in a global software development project to generate a variety of complex measures that capture patterns about when students work. Students work habits are also often determined by where they live and what they are working on. Moreover, students tend to work on group projects in cycles, which correspond to a start, middle, and end time period. Knowledge obtained from this study should provide insight into current empirical research on global software development by defining the different time variables that can also be used to compare temporal patterns found in real-world teams. It should also inform studies about student team projects by helping instructors schedule group activities.
Date: August 2010
Creator: Kesavan Nair Meena, Suneetha Nair
Partner: UNT Libraries

Extracting Temporally-Anchored Knowledge from Tweets

Description: Twitter has quickly become one of the most popular social media sites. It has 313 million monthly active users, and 500 million tweets are published daily. With the massive number of tweets, Twitter users share information about a location along with the temporal awareness. In this work, I focus on tweets where author of the tweets exclusively mentions a location in the tweet. Natural language processing systems can leverage wide range of information from the tweets to build applications like recommender systems that predict the location of the author. This kind of system can be used to increase the visibility of the targeted audience and can also provide recommendations interesting places to visit, hotels to stay, restaurants to eat, targeted on-line advertising, and co-traveler matching based on the temporal information extracted from a tweet. In this work I determine if the author of the tweet is present in the mentioned location of the tweet. I also determine if the author is present in the location before tweeting, while tweeting, or after tweeting. I introduce 5 temporal tags (before the tweet but > 24 hours; before the tweet but < 24 hours; during the tweet is posted; after the tweet is posted but < 24 hours; and after the tweet is posted but > 24 hours). The major contributions of this paper are: (1) creation of a corpus of 1062 tweets containing 1200 location named entities, containing annotations whether author of a tweet is or is not located in the location he tweets about with respect to 5 temporal tags; (2) detailed corpus analysis including real annotation examples and label distributions per temporal tag; (3) detailed inter-annotator agreements, including Cohen's kappa, Krippendorff's alpha and confusion matrices per temporal tag; (4) label distributions and analysis; and (5) supervised learning experiments, along with ...
Date: May 2018
Creator: Doudagiri, Vivek Reddy
Partner: UNT Libraries

Detecting Component Failures and Critical Components in Safety Critical Embedded Systems using Fault Tree Analysis

Description: Component failures can result in catastrophic behaviors in safety critical embedded systems, sometimes resulting in loss of life. Component failures can be treated as off nominal behaviors (ONBs) with respect to the components and sub systems involved in an embedded system. A lot of research is being carried out to tackle the problem of ONBs. These approaches are mainly focused on the states (i.e., desired and undesired states of a system at a given point of time to detect ONBs). In this paper, an approach is discussed to detect component failures and critical components of an embedded system. The approach is based on fault tree analysis (FTA), applied to the requirements specification of embedded systems at design time to find out the relationship between individual component failures and overall system failure. FTA helps in determining both qualitative and quantitative relationship between component failures and system failure. Analyzing the system at design time helps in detecting component failures and critical components and helps in devising strategies to mitigate component failures at design time and improve overall safety and reliability of a system.
Date: May 2018
Creator: Bhandaram, Abhinav
Partner: UNT Libraries