Ashutosh Joshi

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Many researchers have used lexical networks and ontologies to mitigate synonymy and polysemy problems in Question Answering (QA), systems coupled with taggers, query classifiers, and answer extractors in complex and ad-hoc ways. We seek to make QA systems reproducible with shared and modest human effort, carefully separating knowledge from algorithms. To(More)
Symbolic Indirect Correlation (SIC) is a new classification method for unsegmented patterns. SIC requires two levels of comparisons. First, the feature sequences from an unknown query signal and a known multi-pattern reference signal are matched. Then, the order of the matched features is compared with the order of matches between every lexicon(More)
Letter-polygram based Symbolic Indirect Correlation is a new method that offers significant advantages for ordered unsegmented signals. However, its application to on-line, cursive handwriting requires solving several difficult problems. (1) Reference strings of words must satisfy certa in uniformity properties on their lexical match with the lexicon of(More)
Symbolic indirect correlation (SIC) is a new approach for bringing lexical context into the recognition of unseg-mented signals that represent words or phrases in printed or spoken form. One way of viewing the SIC problem is to find the correspondence, if one exists, between two bipartite graphs, one representing the matching of the two lexical strings and(More)
Symbolic Indirect Correlation (SIC) is a non-parametric method that offers significant advantages for recognition of ordered unsegmented signals. A previously introduced formulation of SIC based on subgraph-isomorphism requires very large reference sets in the presence of noise. In this paper, we seek to address this issue by formulating SIC classification(More)
Symbolic indirect correlation (SIC) is a new approach for bringing lexical context into the recognition of unsegmented signals that represent words or phrases in printed or spoken form. One way of viewing the SIC problem is to find the correspondence, if one exists, between two bipartite graphs, one representing the matching of the two lexical strings and(More)
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