Squeezing the last drop: Cluster-based classification algorithm

In this paper we propose a new approach for classification problems and apply it to eight problems. A classification problem with a large feature set is partitioned using clustering on a subset of the features. A multi-label classifier is then trained individually on each cluster, using automatic feature selection to customize the feature set for the cluster. The algorithm achieves one to two percent higher accuracy for most of the problems investigated in this study.Classification Clustering Feature selection

Online Content Trading: Sharing Mart System and Auction Experiments

Abstract—This paper introduces an operational monetar

Question-Answering: CNLP at TREC-10 Question Answering Track

This paper describes the retrieval experiments for the main task and list task of the TREC-10 questionanswering track. The question answering system described automatically finds answers to questions in a large document collection. The system uses a two-stage retrieval approach to answer finding based on matching of named entities, linguistic patterns, and keywords. In answering a question, the system carries out a detailed query analysis that produces a logical query representation, an indication of the question focus, and answer clue words

Question Answering: CNLP at the TREC-2002 Question Answering Track

This paper describes the retrieval experiments for the main task and list task of the TREC-2002 question-answering track. The question answering system described automatically finds answers to questions in a large document collection. The system uses a two-stage retrieval approach to answer finding based on matching of named entities, linguistic patterns, keywords, and the use of a new inference module. In answering a question, the system carries out a detailed query analysis that produces a logical query representation, an indication of the question focus, and answer clue words

Automatic Metadata Generation & Evaluation

The poster reports on a project in which we are investigating methods for breaking the human metadata-generation bottleneck that plagues Digital Libraries. The research question is whether metadata elements and values can be automatically generated from the content of educational resources, and correctly assigned to mathematics and science educational materials. Natural Language Processing and Machine Learning techniques were implemented to automatically assign values of the GEMgenerate metadata element set tofor learning resources provided by the Gateway for Education (GEM), a service that offers web access to a wide range of educational materials. In a user study, education professionals evaluated the metadata assigned to learning resources by either automatic tagging or manual assignment. Results show minimal difference in the eyes of the evaluators between automatically generated metadata and manually assigned metadata

Stem cell therapy in spinal cord injury: in vivo and postmortem tracking of bone marrow mononuclear or mesenchymal stem cells.

OBJECTIVE: The aim of this study was to address the question of whether bone marrow-originated mononuclear cells (MNC) or mesenchymal stem cells (MSC) induce neural regeneration when implanted intraspinally. MATERIALS AND METHODS: The study design included 4 groups of mice: Group 1, non-traumatized control group; Groups 2, 3 and 4 spinal cord traumatized mice with 1 g force Tator clips, which received intralesionally either no cellular implants (Group 2), luciferase (Luc) (+) MNC (Group 3) or MSC (Group 4) obtained from CMV-Luc or beta-actin Luc donor transgenic mice. Following the surgery until decapitation, periodical radioluminescence imaging (RLI) and Basso Mouse Scale (BMS) evaluations was performed to monitor neural activity. Postmortem immunohistochemical techniques were used to analyze the fate of donor type implanted cells. RESULTS: All mice of Groups 3 and 4 showed various degrees of improvement in the BMS scores, whereas there was no change in Groups 1 and 2. The functional improvement was significantly better in Group 4 compared to Group 3 (18 vs 8, p=0.002). The immunohistochemical staining demonstrated GFP(+)Luc(+) neuronal/glial cells that were also positive with one or more of these markers: nestin, myelin associated glycoprotein, microtubule associated protein or myelin oligodendrocyte specific protein, which is considered as indicator of donor type neuronal regeneration. Frequency of donor type neuronal cells; Luc + signals and median BMS scores were observed 48-64% and 68-72%; 44-80%; 8 and 18 within Groups III and IV respectively. DISCUSSION: MSCs were more effective than MNC in obtaining neuronal recovery. Substantial but incomplete functional improvement was associated with donor type in vivo imaging signals more frequently than the number of neuronal cells expressing donor markers in spinal cord sections in vitro. Our results are in favor of functional recovery arising from both donor MSC and MNCs, contributing to direct neuronal regeneration and additional indirect mechanisms