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We evaluate our approach using Natural Language Processing field, and propose a 2-g text mining process adapted for analyzing scientific publications. We rely on a combination of custom indicators with Stanford OpenIE SAO extractor to build a Bernoulli Na\u00efve Bayes classifier which is trained by using domain-specific vocabulary provided by the TeknoAssistant user. The 2-g contained in the abstracts of a scientific publication dataset are classified in either \u201cproblem\u201d, \u201csolution\u201d or \u201cnone\u201d categories, and a problem\u2013solution network is built, based on the co-occurrence of problems and solutions in the abstracts. We propose a combination of clustering technique, visualization and Social Network Analysis indicators for guiding a hypothetical user in a domain-specific problem solving process.<\/jats:p>","DOI":"10.1007\/s11192-022-04280-2","type":"journal-article","created":{"date-parts":[[2022,2,5]],"date-time":"2022-02-05T06:02:38Z","timestamp":1644040958000},"page":"5459-5473","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["TeknoAssistant : a domain specific tech mining approach for technical problem-solving support"],"prefix":"10.1007","volume":"127","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1913-3239","authenticated-orcid":false,"given":"Gaizka","family":"Garechana","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4244-9098","authenticated-orcid":false,"given":"Rosa","family":"R\u00edo-Belver","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2347-3885","authenticated-orcid":false,"given":"Enara","family":"Zarrabeitia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2110-0719","authenticated-orcid":false,"given":"Izaskun","family":"Alvarez-Meaza","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,5]]},"reference":[{"key":"4280_CR1","doi-asserted-by":"publisher","unstructured":"Abbas, A., Zhang, L., & Khan, S. 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