Spoken document representations for probabilistic retrieval

This paper presents some developments in query expansion and document representation of our spoken document retrieval system and shows how various retrieval techniques affect performance for different sets of transcriptions derived from a common speech source. Modifications of the document representation are used, which combine several techniques for query expansion, knowledge-based on one hand and statistics-based on the other. Taken together, these techniques can improve Average Precision by over 19% relative to a system similar to that which we presented at TREC-7. These new experiments have also confirmed that the degradation of Average Precision due to a word error rate (WER) of 25% is quite small (3.7% relative) and can be reduced to almost zero (0.2% relative). The overall improvement of the retrieval system can also be observed for seven different sets of transcriptions from different recognition engines with a WER ranging from 24.8% to 61.5%. We hope to repeat these experiments when larger document collections become available, in order to evaluate the scalability of these techniques.

Why do firms acquire external technologies? Understanding the motivations for technology acquisitions

Why do firms acquire external technologies? Previous research indicates that there are a wide variety of motivations. These include the need to acquire valuable knowledge-based resources, to improve strategic flexibility, to experiment), to overcome organisational inertia, to mitigate risk and uncertainty, to reduce costs and development time in new product development, and the perception that the firm has the absorptive capacity to integrate acquisitions. In this paper we provide an in-depth literature review of the motivations for the acquisition of external technologies by firms. We find that these motivations can be broadly classed into four categories: (1) the development of technological capabilities, (2) the development of strategic options, (3) efficiency improvements, and (4) responses to the competitive environment. In light of this categorisation, we comment on how these different motivations connect to the wider issues of technology acquisition. © 2010 IEEE.

The role of artificial intelligence as 'text' within design

The paper describes a new approach to artificial intelligence (AI) and its role in design. This approach argues that AI can be seen as 'text', or in other words as a medium for the communication of design knowledge and information between designers. This paper will apply these ideas to reinterpreting an existing knowledge-based system (KBS) design tool, that is, CADET - a product design evaluation tool. The paper will discuss the authorial issues, amongst others, involved in the development of AI and KBS design tools by adopting this new approach. Consequently, the designers' rights and responsibilities will be better understood as the knowledge medium, through its concern with authorship, returns control to users rather than attributing the system with agent status. © 1998 Elsevier Science Ltd. All rights reserved.