By Hui Cheng, Tessai Hayama, Susumu Kunifuji (auth.), Thanaruk Theeramunkong, Susumu Kunifuji, Virach Sornlertlamvanich, Cholwich Nattee (eds.)
This publication constitutes the completely refereed post-conference complaints of the fifth foreign convention on wisdom, info, and Creativity aid platforms, KCIS 2010, held in Chang Mai, Thailand, in November 2010.
The 23 revised complete papers offered have been rigorously reviewed and chosen from seventy two submissions. The papers hide a vast variety of themes regarding all wisdom science-related parts together with creativity help, determination technological know-how, wisdom technological know-how, info mining, desktop studying, databases, records, wisdom acquisition, computerized clinical discovery, data/knowledge visualization, and knowledge-based systems.
Read Online or Download Knowledge, Information, and Creativity Support Systems: 5th International Conference, KICSS 2010, Chiang Mai, Thailand, November 25-27, 2010, Revised Selected Papers PDF
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Extra resources for Knowledge, Information, and Creativity Support Systems: 5th International Conference, KICSS 2010, Chiang Mai, Thailand, November 25-27, 2010, Revised Selected Papers
Sample text
For automatic assessment it is necessary to integrate high-level linguistic features. Similar observations are also prevalent in Information Retrieval tasks. The advances in natural language processing will certainly have implications in modeling IR as well as Computer Aided Assessment of narrative answers. The online assessment system outlined here is based on a modular design. Therefore, the existing approaches can be easily augmented further. New approaches can also be integrated with ease. Moreover, the online assessment system allows us to archive historical data (students’ answers along with human validated grades).
On HMS, every confidence of a link is given by the user when the link is added but every confidence of a label is automatically given by calculation. , conf(ln )) where li (1 ≤ i ≤ n) is an element of v∈src(e) minG(e, v) and g is a function which combines confidences of the labels. In this paper, Semantics of a Graphical Model for Representing Hypotheses 41 Table 1. The relation of hypothesis graphs V, { ie1 , ie2 , ie3 , ie4 , e1 , e2 } V, { ie1 , ie2 , ie3 , e1 , e2 } V, { ie1 , ie2 , ie3 , ie4 , e1 , e3 } V, { ie1 , ie2 , ie4 , e1 , e3 } V, { ie1 , ie3 , ie4 , e1 , e3 } V, { ie1 , ie2 , ie3 , ie4 , e3 } V, { ie2 , ie3 , ie4 , e3 } the notebook notebook 1 notebook 1 notebook 2 notebook 2 notebook 2 notebook 3 notebook 4 the book book 1 book 1 book 1 book 2 book 3 book 1 book 1 the author author 1 author 2 author 1 author 1 author 1 author 1 author 1 Table 2.
Additional preprocessing was not necessary for KEA based approaches since KEA has stemmer and stop-word removal built-in. For the Keyphrase with Synonym Resolution approach, we also use a thesaurus for synonym resolution. IDF weight is a weight often used in information retrieval and text mining in term weighting. IDF weight weighs each word (or phrase) in the text document according to how unique it is [9]. The similarity score between the query and the documents in a document collection shows how relevant a document is for that particular query.