Abstract. Associative information, e.g., the associated documents, associated keywords, freelinks, and categories are potential sources for a divergent thinking support. This paper compares four divergent thinking support engines using the associative information extracted from the Wikipedia. The first two engines adapt the association search engine GETA [1], and the last two engines finds the association by using the document structure. Their quality is compared by experiments in both quantitative and qualitative evaluations by using Eureka! interface, which is inspired by "Hasso-Tobi 2" divergent thinking support groupware [2].
Most existing methods of effort estimations in software development are manual, labor-intensive and subjective, resulting in overestimation with bidding fail, and underestimation with money loss. This paper investigates effectiveness of sequence models on estimating development effort, in the form of man-months, from software project data. Four architectures; (1) Average word-vector with Multi-layer Perceptron (MLP), (2) Average word-vector with Support Vector Regression (SVR), (3) Gated Recurrent Unit (GRU) sequence model, and (4) Long short-term memory (LSTM) sequence model are compared in terms of man-months difference. The approach is evaluated using two datasets; ISEM (1,573 English software project descriptions) and ISBSG (9,100 software projects data), where the former is a raw text and the latter is a structured data table explained the characteristic of a software project. The LSTM sequence model achieves the lowest and the second lowest mean absolute errors, which are 0.705 and 14.077 man-months for ISEM and ISBSG datasets respectively. The MLP model achieves the lowest mean absolute errors which is 14.069 for ISBSG datasets.
Associative search is information retrieval based on the similarity between two different items of text information. This paper reports experiments on associative search on a large number of short documents containing a small set of words. We also show the extension of the set of words with a semantic relation, and investigate its effect. As an instance, experiments were performed on 49,767 professional (non-handicapped) Shogi game records with 1,923 next move problems for evaluation. The extension of the set of words by pairing under semantic relations, called semantic coupling, is examined to see the effect of enlarging the word space from unigrams to bigrams. Although the search results are not as precise as next move search, we observe improvement by filtering the unigram search result with the bigram search, especially in the early phase of Shogi games. This also fits our general feeling that the bigram search detects castle patterns well.
Kobkrit VIRIYAYUDHAKORN†a) , Nonmember and Susumu KUNIFUJI †b) , Member SUMMARY Recent idea visualization programs still lack automatic idea summarization capabilities. This paper presents a knowledge-based method for automatically providing a short piece of English text about a topic to each idea group in idea charts. This automatic topic identification makes used Yet Another General Ontology (YAGO) and Wordnet as its knowledge bases. We propose a novel topic selection method and we compared its performance with three existing methods using two experimental datasets constructed using two idea visualization programs, i.e., the KJ Method (Kawakita Jiro Method) and mind-mapping programs. Our proposed topic identification method outperformed the baseline method in terms of both performance and consistency. key words: creativity support system, idea summarization, KJ method, knowledge-based automatic topic identification, mind map
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.