Two Steps Feature Selection and Neural Network Classification for the TREC-8 Routing
<|reference_start|>Two Steps Feature Selection and Neural Network Classification for the TREC-8 Routing: For the TREC-8 routing, one specific filter is built for each topic. Each filter is a classifier trained to recognize the documents that are relevant to the topic. When presented with a document, each classifier estimates the probability for the document to be relevant to the topic for which it has been trained. Since the procedure for building a filter is topic-independent, the system is fully automatic.   By making use of a sample of documents that have previously been evaluated as relevant or not relevant to a particular topic, a term selection is performed, and a neural network is trained. Each document is represented by a vector of frequencies of a list of selected terms. This list depends on the topic to be filtered; it is constructed in two steps. The first step defines the characteristic words used in the relevant documents of the corpus; the second one chooses, among the previous list, the most discriminant ones. The length of the vector is optimized automatically for each topic. At the end of the term selection, a vector of typically 25 words is defined for the topic, so that each document which has to be processed is represented by a vector of term frequencies.   This vector is subsequently input to a classifier that is trained from the same sample. After training, the classifier estimates for each document of a test set its probability of being relevant; for submission to TREC, the top 1000 documents are ranked in order of decreasing relevance.<|reference_end|>
@article{stricker2000two,
    title={Two Steps Feature Selection and Neural Network Classification for the
  TREC-8 Routing},
    author={Mathieu Stricker, Frantz Vichot, Gerard Dreyfus, Francis Wolinski},
    journal={arXiv preprint arXiv:cs/0007016},
    year={2000},
    archivePrefix={arXiv},
    eprint={cs/0007016},
    primaryClass={cs.CL cs.AI}
}