A Text Mining Model for Strategic Alliance Discovery

Strategic alliances among organizations are one of the central drivers of innovation and economy and have raised strong interest among policymakers, strategists and economists. However, discovery of alliances has relied on pure manual search and has limited scope. This research addresses the limitations by proposing a text mining framework that automatically extracts alliances from news articles. The model not only integrates meta-search, entity extraction and shallow and deep linguistic parsing techniques, but also proposes an innovative ADT-based relation extraction method to deal with the extremely skewed and noisy news articles and AC Rank to further improve the precision using various linguistic features. Evaluation from an IBM alliances case study showed that ADT-based extraction achieved 78.1% in recall, 44.7% in precision and 0.569 in F-measure and eliminated over 99% of the noise in document collections. AC Rank further improved precision to 97% with the top-20% extracted alliance instances. Our case study also showed that the widely cited Thomson SDC database only covered less than 20% of total alliances while our automatic approach can covered 67%.