Abstract: There is provided a method and a system for generating clusters of documents using a combined metric parameter. A first document and a second document are received, and for a potential cluster including the first document and the second document: a first metric parameter indicative of a degree of complementariness of document content in the potential cluster is determined, a second metric parameter indicative of a degree of dilution of the document content in the potential cluster is determined. The combined metric parameter is determined based on the first metric parameter and the second metric parameter. A cluster is generated based on the combined metric parameter, where the cluster includes the first and second documents. Other document(s) or clusters may be added to the cluster by determining an updated combined metric parameter for a potential cluster and comparing the updated combined metric parameter with the combined metric parameter.

Abstract: A method and server for training a machine learning algorithm (MLA) for determining a query-completion suggestion for a partial query is disclosed. The method comprises receiving and parsing past queries into n-grams. Each one of the n-grams being associated with respective n-gram features, the n-gram features being indicative of a pair-based co-occurrence of n-grams in the past queries. The method also comprises, for a given n-gram of a given past query: selecting at least one candidate n-gram from the n-grams based on the pair-based co-occurrence; generating respective feature vectors for the given n-gram and the at least one candidate n-gram; generating a training set for the given n-gram comprising an input portion and a label portion; and training the MLA based on the training set to determine a predicted group-based co-occurrence of at least one in-use candidate n-gram and at least one in-use n-gram.

Abstract: There is provided a method and a system for generating clusters of documents using a combined metric parameter. A first document and a second document are received, and for a potential cluster including the first document and the second document: a first metric parameter indicative of a degree of complementariness of document content in the potential cluster is determined, a second metric parameter indicative of a degree of dilution of the document content in the potential cluster is determined. The combined metric parameter is determined based on the first metric parameter and the second metric parameter. A cluster is generated based on the combined metric parameter, where the cluster includes the first and second documents. Other document(s) or clusters may be added to the cluster by determining an updated combined metric parameter for a potential cluster and comparing the updated combined metric parameter with the combined metric parameter.

Abstract: A method and server for training a machine learning algorithm (MLA) for determining a query-completion suggestion for a partial query is disclosed. The method comprises receiving and parsing past queries into n-grams. Each one of the n-grams being associated with respective n-gram features, the n-gram features being indicative of a pair-based co-occurrence of n-grams in the past queries. The method also comprises, for a given n-gram of a given past query: selecting at least one candidate n-gram from the n-grams based on the pair-based co-occurrence; generating respective feature vectors for the given n-gram and the at least one candidate n-gram; generating a training set for the given n-gram comprising an input portion and a label portion; and training the MLA based on the training set to determine a predicted group-based co-occurrence of at least one in-use candidate n-gram and at least one in-use n-gram.