A Review of the Literature on the Empathy Construct Using Cluster Analysis <s> Document Projection <s> How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other theories, phenomena, and problems are sketched. <s> BIB001 </s> A Review of the Literature on the Empathy Construct Using Cluster Analysis <s> Document Projection <s> 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. <s> BIB002
The process of projection related the IS empathy research to the clustering that resulted from the analysis of the empathy research in other disciplines. To project a new text unit (e.g., a paragraph) into a k-dimensional space obtained from a SVD of a corpus, perform the following steps: 1) Using the set of root words from the original analysis, construct a term-frequency (TF) column containing the frequency of each root in the new text unit. This will be an m x 1 matrix. Label this column x. 2) Weight the column from (1). a. Using the same local weighting algorithm (e.g., log (1 + tf)), weight the TF column, x. b. Using the global weight values computed from the original corpus, multiply the results from (2a) by the corresponding global weight for each term. Label this column a. 3) Project a into the k-dimensional space resulting from the SVD of the original corpus. That is, z is the 1 x k projection of a pseudo-document into this space. 4) Compute the cosine between the z vector and any other text unit weighted by the singular value for each dimension. The resulting cosine from the projection is commonly referred to as the measure of semantic relatedness [Wolfe et al. 1998 ], where a cosine of one indicates maximal relatedness and a cosine of zero indicates minimal relatedness BIB002 . Cosines have been shown to reliably match human similarity judgments of documents BIB001 . Even though past research has referred to cosines as measures of semantic similarity, we are uncomfortable with using this terminology because it has never been empirically validated. Instead, we refer to cosines as a measure of similarity in the following sections because a cosine simply determines the angle between two vectors in a k-dimensional space.