Learning Methods and Algorithms for Semantic Text Classification across Multiple Domains

Information is nowadays a key resource: machine learning and data mining techniques have been developed to extract high-level information from great amounts of data. As most data comes in form of unstructured text in natural languages, research on text mining is currently very active and dealing with practical problems. Among these, text categorization deals with the automatic organization of large quantities of documents in priorly defined taxonomies of topic categories, possibly arranged in large hierarchies. In commonly proposed machine learning approaches, classifiers are automatically trained from pre-labeled documents: they can perform very accurate classification, but often require a consistent training set and notable computational effort. Methods for cross-domain text categorization have been proposed, allowing to leverage a set of labeled documents of one domain to classify those of another one. Most methods use advanced statistical techniques, usually involving tuning of parameters. A first contribution presented here is a method based on nearest centroid classification, where profiles of categories are generated from the known domain and then iteratively adapted to the unknown one. Despite being conceptually simple and having easily tuned parameters, this method achieves state-of-the-art accuracy in most benchmark datasets with fast running times. A second, deeper contribution involves the design of a domain-independent model to distinguish the degree and type of relatedness between arbitrary documents and topics, inferred from the different types of semantic relationships between respective representative words, identified by specific search algorithms. The application of this model is tested on both flat and hierarchical text categorization, where it potentially allows the efficient addition of new categories during classification. Results show that classification accuracy still requires improvements, but models generated from one domain are shown to be effectively able to be reused in a different one.

Technology acquisition through licensing. Implications for firm strategy

Nowadays licensing practices have increased in importance and relevance driving the widespread diffusion of markets for technologies. Firms are shifting from a tactical to a strategic attitude towards licensing, addressing both business and corporate level objectives. The Open Innovation Paradigm has been embraced. Firms rely more and more on collaboration and external sourcing of knowledge. This new model of innovation requires firms to leverage on external technologies to unlock the potential of firms’ internal innovative efforts. In this context, firms’ competitive advantage depends both on their ability to recognize available opportunities inside and outside their boundaries and on their readiness to exploit them in order to fuel their innovation process dynamically. Licensing is one of the ways available to firm to ripe the advantages associated to an open attitude in technology strategy. From the licensee’s point view this implies challenging the so-called not-invented-here syndrome, affecting the more traditional firms that emphasize the myth of internal research and development supremacy. This also entails understanding the so-called cognitive constraints affecting the perfect functioning of markets for technologies that are associated to the costs for the assimilation, integration and exploitation of external knowledge by recipient firms. My thesis aimed at shedding light on new interesting issues associated to in-licensing activities that have been neglected by the literature on licensing and markets for technologies. The reason for this gap is associated to the “perspective bias” affecting the works within this stream of research. With very few notable exceptions, they have been generally concerned with the investigation of the so-called licensing dilemma of the licensor – whether to license out or to internally exploit the in-house developed technologies, while neglecting the licensee’s perspective. In my opinion, this has left rooms for improving the understanding of the determinants and conditions affecting licensing-in practices. From the licensee’s viewpoint, the licensing strategy deals with the search, integration, assimilation, exploitation of external technologies. As such it lies at the very hearth of firm’s technology strategy. Improving our understanding of this strategy is thus required to assess the full implications of in-licensing decisions as they shape firms’ innovation patterns and technological capabilities evolution. It also allow for understanding the so-called cognitive constraints associated to the not-invented-here syndrome. In recognition of that, the aim of my work is to contribute to the theoretical and empirical literature explaining the determinants of the licensee’s behavior, by providing a comprehensive theoretical framework as well as ad-hoc conceptual tools to understand and overcome frictions and to ease the achievement of satisfactory technology transfer agreements in the marketplace. Aiming at this, I investigate licensing-in in three different fashions developed in three research papers. In the first work, I investigate the links between licensing and the patterns of firms’ technological search diversification according to the framework of references of the Search literature, Resource-based Theory and the theory of general purpose technologies. In the second paper - that continues where the first one left off – I analyze the new concept of learning-bylicensing, in terms of development of new knowledge inside the licensee firms (e.g. new patents) some years after the acquisition of the license, according to the Dynamic Capabilities perspective. Finally, in the third study, Ideal with the determinants of the remuneration structure of patent licenses (form and amount), and in particular on the role of the upfront fee from the licensee’s perspective. Aiming at this, I combine the insights of two theoretical approaches: agency and real options theory.