Knowledge Discovery from Financial Text

The abundance of on-line electronic financial news articles has opened up new possibilities for intelligent systems that could extract and organize relevant knowledge automatically in a usable format. While most typical information extraction systems require a hand-built dictionary of templates and, subsequently, are subject to ceaseless modification to accommodate new patterns that are observed in the text, in this research, we propose a novel text-based decision support system (DSS) that will (i) extract event sequences from shallow text patterns and (ii) predict the likelihood of the occurrence of events using a classifier-based inference engine. We investigated more than 2,000 financial reports with 28,000 sentences. Experiments show the DSS outperforms other similar statistical models

Coherence Identification of Business Documents: Towards an Automated Message Processing System

This paper describes our recent efforts in developing a text segmentation technique in our business document management system. The document analysis is based upon a knowledge-based analysis of the documents’ contents, by automating the coherence identification process, without a full semantic understanding. In the technique, document boundaries can be identified by observing the shifts of segments from one cluster to another. Our experimental results show that the combination of the heterogeneous knowledge is capable to address the topic shifts. Given the increasing recognition of document structure in the fields of information retrieval as well as knowledge management, this approach provides a quantitative model and automatic classification of documents in a business document management system. This will beneficial to the distribution of documents or automatic launching of business processes in a workflow management system

Knowledge sharing and social media: Altruism, perceived online attachment motivation, and perceived online relationship commitment

Social media, such as Facebook and Twitter, have become extremely popular. Facebook, for example, has more than a billion registered users and thousands of millions of units of information are shared every day, including short phrases, articles, photos, and audio and video clips. However, only a tiny proportion of these sharing units trigger any type of knowledge exchange that is ultimately beneficial to the users. This study draws on the theory of belonging and the intrinsic motivation of altruism to explore the factors contributing to knowledge sharing behavior. Using a survey of 299 high school students applying for university after the release of the public examination results, we find that perceived online attachment motivation (β = 0.31, p \u3c 0.001) and perceived online relationship commitment (β = 0.49, p \u3c 0.001) have positive, direct, and significant effects on online knowledge sharing (R2 0.568). Moreover, when introduced into the model, altruism has a direct and significant effect on online knowledge sharing (β = 0.46, p \u3c 0.001) and the total variance explained by the extended model increases to 64.9%. The implications of the findings are discussed

Cutting temperatures when ball nose end milling γ-TiAl intermetallic alloys

AbstractExperimental results are presented for Ti–45Al–2Mn–2Nb+0.8vol% TiB2XD and Ti–45Al–8Nb–0.2C alloys. Three approaches were employed involving a constantan-workpiece thermocouple arrangement, implanted K-type thermocouples and IR thermography. New and worn (~300μm flank wear) coated carbide tools were used under dry conditions when down milling at 50–345m/min, with workpieces mounted horizontally and at 45°. Despite slight variation in ancillary finishing parameters there was generally good agreement between data sets for the different evaluation techniques employed and for both alloys. Higher temperatures were measured with the workpiece at 45°, with constantan-workpiece thermocouple temperatures of 375°C and 413°C for new and worn tools respectively at 345m/min