This is our second participation in the TREC Legal Track. The TREC Legal Track 2011 featured only the Learning Task. We participated in Topics 401 and 403. We used Lemur 4.11 for Boolean retrieval and followed it with a clustering technique, where we chose members from each cluster (which we called seeds) for relevance judgement by the TA and assumed all other members of the cluster whose seeds...

In this paper, we compare learning techniques based on statistical classiication to traditional methods of relevance feedback for the document routing problem. We consider three classiication techniques which have decision rules that are derived via explicit error minimization: linear discriminant analysis, logistic regression , and neural networks. We demonstrate that the classiiers perform 10...

This paper demonstrates that a lot of time, cost, and complexities can be saved avoided would otherwise used to label the text data for classification purposes. The AI world realizes importance labelled its use various NLP applications. Here, we have categorized close 6,000 unlabelled samples into five distinct classes. dataset was further multi-class classification. Data labelling task using t...

Label smoothing has a wide range of applications in machine learning field. Nonetheless, label only softs the targets by adding uniform distribution into one-hot vectors, which cannot truthfully reflect underlying relations among categories. However, category is vital importance many fields such as emotion taxonomy and open set recognition. In this work, we propose method to obtain for each (ca...

Abstract Machine learning is a clear indication that technological progress accelerating right now. All jobs must be done instantly and quickly in the digital world, making businessmen more competitive. There also pressing demand for machine education. As far as future policies go, this relevant. Included issue challenge of evaluating substance student’s thesis abstract accuracy, appropriatenes...