Biomedical subjects

Katja Wiemer-Hastings

Publications and source records attributed to Katja Wiemer-Hastings.

4 recordsLinked to original sources

PubMed2004

Automatic classification of dysfunctional thoughts: a feasibility test.

The identification of dysfunctional thoughts is a central effort in cognitive therapy. This paper describes the first version of a computer module that classifies dysfunctional thoughts automatically. It is part of COGNO, a system we are developing to give automatic feedback on dysfunctional thoughts. The system uses rules that were developed from language markers identified in a sample of 149 dysfunctional thoughts. The system was tested with an independent set of 112 example thoughts. The system detects the majority of dysfunctional thoughts, but works reliably only for some thought categories. Automatic thought classification may be a first step toward developing natural dialogue systems in cognitive therapy.

Artificial Intelligence↗

PubMed2004

Identifying reading strategies using latent semantic analysis: comparing semantic benchmarks.

We explored methods of using latent semantic analysis (LSA) to identify reading strategies in students' self-explanations that are collected as part of a Web-based reading trainer. In this study, college students self-explained scientific texts, one sentence at a time. ISA was used to measure the similarity between the self-explanations and semantic benchmarks (groups of words and sentences that together represent reading strategies). Three types of semantic benchmarks were compared: content words, exemplars, and strategies. Discriminant analyses were used to classify global and specific reading strategies using the LSA cosines. All benchmarks contributed to the classification of general reading strategies, but the exemplars did the best in distinguishing subtle semantic differences between reading strategies. Pragmatic and theoretical concerns of using LSA are discussed.

Adult↗

PubMed2003

Computerizing reading training: evaluation of a latent semantic analysis space for science text.

The effectiveness of a domain-specific latent semantic analysis (LSA) in assessing reading strategies was examined. Students were given self-explanation reading training (SERT) and asked to think aloud after each sentence in a science text. Novice and expert human raters and two LSA spaces (general reading, science) rated the similarity of each think-aloud protocol to benchmarks representing three different reading strategies (minimal, local, and global). The science LSA space correlated highly with human judgments, and more highly than did the general reading space. Also, cosines from the science LSA spaces can distinguish between different levels of semantic similarity, but may have trouble in distinguishing local processing protocols. Thus, a domain-specific LSA space is advantageous regardless of the size of the space. The results are discussed in the context of applying the science LSA to a computer-based version of SERT that gives online feedback based on LSA cosines.

Artificial Intelligence↗

PubMed2002

Using latent semantic analysis to assess reader strategies.

We tested a computer-based procedure for assessing reader strategies that was based on verbal protocols that utilized latent semantic analysis (LSA). Students were given self-explanation-reading training (SERT), which teaches strategies that facilitate self-explanation during reading, such as elaboration based on world knowledge and bridging between text sentences. During a computerized version of SERT practice, students read texts and typed self-explanations into a computer after each sentence. The use of SERT strategies during this practice was assessed by determining the extent to which students used the information in the current sentence versus the prior text or world knowledge in their self-explanations. This assessment was made on the basis of human judgments and LSA. Both human judgments and LSA were remarkably similar and indicated that students who were not complying with SERT tended to paraphrase the text sentences, whereas students who were compliant with SERT tended to explain the sentences in terms of what they knew about the world and of information provided in the prior text context. The similarity between human judgments and LSA indicates that LSA will be useful in accounting for reading strategies in a Web-based version of SERT.

Humans↗