Kimberly D. Voll

Lexicon-based methods for sentiment analysis

We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the texts opinion towards its main subject matter. We show that SO-CALs performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.

Improving the Utility of Speech Recognition Through Error Detection

Despite the potential to dominate radiology reporting, current speech recognition technology is thus far a weak and inconsistent alternative to traditional human transcription. This is attributable to poor accuracy rates, in spite of vendor claims, and the wasted resources that go into correcting erroneous reports. A solution to this problem is post-speech-recognition error detection that will assist the radiologist in proofreading more efficiently. In this paper, we present a statistical method for error detection that can be applied after transcription. The results are encouraging, showing an error detection rate as high as 96% in some cases.

AN ASSUMPTIVE LOGIC PROGRAMMING METHODOLOGY FOR PARSING

We show how two novel tools in logic programming for AI (namely, continuation-based linear and timeless assumptions, and Datalog grammars) can assist us in producing terse treatments of difficult language processing phenomena. As a proof of concept, we present a concise parser for Datalog grammars (logic grammars where strings are represented with numbered word boundaries rather than as lists of words) that uses assumptions and a combination of left-corner parsing and charting. We then study two test cases of this parsers application: complete constituent coordination, and error diagnosis and correction.

Pedagogical transformations in the UBC CS science education initiative

The UBC CS Science Education Initiative (CSSEI) has resulted in a number of research projects. New teaching methods and student assessment instruments are introduced to engage student learning and evaluations of their understanding. In this paper, we report four of these recent initiatives and their initial findings.

A Hybrid Approach to Improving Automatic Speech Recognition Via NLP

In many domains, automated speech recognition (ASR) demands highly robust and accurate recognition software. Unfortunately, in such domains, even a 99% accurate recognizer is inadequate, and other methods for increasing the reliability and performance of ASR must be considered. As a possible solution to this problem, post-speech-recognition error detection can assist in proofreading more efficiently. To this end, we have developed a multi-heuristic algorithm using natural language processing to detect recognition errors. As a proof of concept, we have applied this algorithm to the radiology domain. The results are encouraging, showing a 22% increase in the recall performance, and a 6% increase in the precision performance, over the best individual technique.

An assumptive logic programming methodology for parsing

We show how several novel tools in logic programming for AI (namely, continuation based linear and timeless assumptions, and datalog grammars) can assist us in producing terse treatments of difficult language processing phenomena. As a proof of concept, we present a concise parser for datalog grammars (logic grammars where strings are represented with numbered word boundaries rather than as lists of words), that uses assumptions and a combination of left-corner parsing and charting. We then study two test cases of this parsers application: complete constituent coordination, and error diagnosis and correction.

Probing student problem solving skills in mathematical induction using a scenario based think aloud protocol

Think aloud protocol has traditionally been used to probe thinking and problem solving skills. In a series of student interview sessions where think aloud protocol was used to study student problem solving skills in mathematical induction at the University of British Columbia, a significant percentage of the students had difficulty verbalizing their thoughts while solving a problem. At other times, students felt pressured in coming up with a solution, or they were afraid of making incorrect steps and wanted to be certain before they actually articulated their thought process. As a result, a number of interviews resulted in almost complete silence and little was gained. To this end, we developed an enhanced, scenario based think-aloud protocol, designed to engage subjects during the interviews without having them feeling pressured or having to perform in front of an interviewer. In the process, the subject becomes less and less conscious of being studied and becomes more immersed in the actual problem solving with her own thoughts and ideas. In this paper we will describe this augmented think aloud protocol, and discuss our experiences of using this tool to assess mathematical induction reasoning in computer science students. A number of essential skills are identified through these interviews that may help instructors identify improved pedagogical methods for teaching the subject.

Circuits and logic in the lab: toward a coherent picture of computation

We describe extensive modifications made over time to a first year computer science course at the University of British Columbia covering logic and digital circuits (among other topics). Smoothly integrating the hardware-based labs with the more theory-based lectures into a cohesive picture of computation has always been a challenge in this course. The seeming disconnect between implementation and abstraction has historically led to frustration and dissatisfaction among students. We describe changes to the lab curriculum, equipment logistics, the style of in-lab activities and evaluation. We have also made logistical changes to the management and ongoing training of teaching assistants, allowing us to better anchor our larger course story into the lab curriculum. These changes have greatly improved student and TA opinions of the lab experience, as well as the overall course.