We use a supervised whole-document approach to English Entity Linking with simple clustering approaches. The system extends our TAC 2012 system (Radford et al., 2012), introducing new features for modelling local entity description and type-specific matching as well type-specific supervised models and supervised NIL classification. Our rule-based clustering takes advantage of local description ...

Although there is a large and growing literature that tackles the semi-supervised clustering problem (i.e., using some labeled objects or cluster-guiding constraints like “must-link” or “cannot-link”), the evaluation of semi-supervised clustering approaches has rarely been discussed. The application of cross-validation techniques, for example, is far from straightforward in the semi-supervised ...

Modular construction allows for a faster, safer, better controlled, and more productive process, yielding quality results with low risk controlled costs. However, despite the potential advantages of this methodology, its adoption has remained slow due to reasonably high degree standardisation repetition that projects require, inexorably clashing unique building designs created meet clients’ nee...

This work centers on a novel data mining technique we term supervised clustering. Unlike traditional clustering, supervised clustering assumes that the examples are classified and has the goal of identifying class-uniform clusters that have high probability densities. Three representative–based algorithms for supervised clustering are introduced: two greedy algorithms SRIDHCR and SPAM, and an e...

Data mining is the process of finding the previously unknown and potentially interesting patterns and relation in database. Data mining is the step in the knowledge discovery in database process (KDD) .The structures that are the outcome of the data mining process must meet certain condition so that these can be considered as knowledge. These conditions are validity, understandability, utility,...

We propose a supervised word sense disambiguation (WSD) system that uses features obtained from clustering results of word instances. Our approach is novel in that we employ semi-supervised clustering that controls the fluctuation of the centroid of a cluster, and we select seed instances by considering the frequency distribution of word senses and exclude outliers when we introduce “must-link”...