A computer-implemented system, comprising: a storage device comprising a corpus of articles and an index comprising a plurality of topics; a finite state modeler adapted for obtaining, for each of the plurality of topics, a training set of articles in the corpus selected by a user as on-topic positive training examples and finding, for each of the topics, a fine-grained topic model associated with the topic, the fine-grained topic model matching the on-topic positive training examples for the topic, each fine-grained topic model being specified as a finite state pattern matchable against the articles; and a characteristic word modeler adapted for: selecting a random sampling of articles out of the corpus; identifying characteristic words comprised in the articles of the random sampling and each of the training sets of articles; determining frequencies of occurrence of the characteristic words in the random sampling and each of the training sets of articles; for each of the plurality of topics, determining a term frequency-inverse document frequency weighting that indentifies a ratio of the frequencies of occurrence of the characteristic words in the training set of articles for the topic to the frequencies of occurrence of the characteristic words in the random sampling; and for each of the plurality of topics, building a coarse-grained topic model comprising the characteristic words comprised in the articles relating to the topic and weights assigned to those characteristic words, the weights being based on said term frequency-inverse document frequency weightings determined for said topic; the computer-implemented system being further configured for: filtering new articles through the fine-grained topic models, wherein candidate articles being new articles that match the fine-grained topic model associated with one of the plurality of topics are identified; for each of the candidate articles, determining a score measuring the similarity of the candidate article to the center of the one of the plurality of topics using the weights of the characteristic words of the coarse-grained topic model for the one of the plurality of topics; and identifying noise articles among the candidate articles, the noise articles having a score smaller than a threshold score. a storage device comprising a corpus of articles and an index comprising a plurality of topics; a finite state modeler adapted for obtaining, for each of the plurality of topics, a training set of articles in the corpus selected by a user as on-topic positive training examples and finding, for each of the topics, a fine-grained topic model associated with the topic, the fine-grained topic model matching the on-topic positive training examples for the topic, each fine-grained topic model being specified as a finite state pattern matchable against the articles; and a characteristic word modeler adapted for: selecting a random sampling of articles out of the corpus; identifying characteristic words comprised in the articles of the random sampling and each of the training sets of articles; determining frequencies of occurrence of the characteristic words in the random sampling and each of the training sets of articles; for each of the plurality of topics, determining a term frequency-inverse document frequency weighting that indentifies a ratio of the frequencies of occurrence of the characteristic words in the training set of articles for the topic to the frequencies of occurrence of the characteristic words in the random sampling; and for each of the plurality of topics, building a coarse-grained topic model comprising the characteristic words comprised in the articles relating to the topic and weights assigned to those characteristic words, the weights being based on said term frequency-inverse document frequency weightings determined for said topic; the computer-implemented system being further configured for: selecting a random sampling of articles out of the corpus; identifying characteristic words comprised in the articles of the random sampling and each of the training sets of articles; determining frequencies of occurrence of the characteristic words in the random sampling and each of the training sets of articles; for each of the plurality of topics, determining a term frequency-inverse document frequency weighting that indentifies a ratio of the frequencies of occurrence of the characteristic words in the training set of articles for the topic to the frequencies of occurrence of the characteristic words in the random sampling; and for each of the plurality of topics, building a coarse-grained topic model comprising the characteristic words comprised in the articles relating to the topic and weights assigned to those characteristic words, the weights being based on said term frequency-inverse document frequency weightings determined for said topic; the computer-implemented system being further configured for: filtering new articles through the fine-grained topic models, wherein candidate articles being new articles that match the fine-grained topic model associated with one of the plurality of topics are identified; for each of the candidate articles, determining a score measuring the similarity of the candidate article to the center of the one of the plurality of topics using the weights of the characteristic words of the coarse-grained topic model for the one of the plurality of topics; and identifying noise articles among the candidate articles, the noise articles having a score smaller than a threshold score.