1. A computer-implemented method, the method comprising: obtaining a collection of phrases from at least one electronic resource; generating, from a collection of search queries, respective counts of each n-gram of terms that appears in search queries in the collection of search queries; determining, based at least in part on the collection of phrases and the respective counts of the n-grams, a respective segmentation for each search query in the collection of search queries, wherein the search query comprises a plurality of terms, and wherein the segmentation identifies at least one phrase using at least one term of the search query; storing, in a database of a service provider environment, the service provider environment including a plurality electronic resources available over a network, the respective segmentation for each search query in the collection of search queries to be used as training data for training a predictive model, wherein the training data includes a plurality of search queries in the collection of search queries as an input and the corresponding segmentation for the search queries as an output; and generating the predictive model using at least the training data, wherein the trained predictive model is configured to predict respective segmentations for new search queries.
1. A computer-implemented method, the method comprising: obtaining a collection of phrases from at least one electronic resource; generating, from a collection of search queries, respective counts of each n-gram of terms that appears in search queries in the collection of search queries; determining, based at least in part on the collection of phrases and the respective counts of the n-grams, a respective segmentation for each search query in the collection of search queries, wherein the search query comprises a plurality of terms, and wherein the segmentation identifies at least one phrase using at least one term of the search query; storing, in a database of a service provider environment, the service provider environment including a plurality electronic resources available over a network, the respective segmentation for each search query in the collection of search queries to be used as training data for training a predictive model, wherein the training data includes a plurality of search queries in the collection of search queries as an input and the corresponding segmentation for the search queries as an output; and generating the predictive model using at least the training data, wherein the trained predictive model is configured to predict respective segmentations for new search queries. 2. The computer-implemented method of claim 1 , wherein generating the respective segmentation for a search query further comprises: determining a respective score for each possible segmentation of the search query, wherein each segmentation identifies at least one potential phrase using at least one term in the search query, wherein the score is based at least in part on (i) matching the at least one potential phrase with at least one phrase in the collection of phrases and (ii) a frequency with which the at least one potential phrase appears in the collection of search queries.