Automatically identifying multi-word expressions

A method in a computing system to establish a trained machine learning model, the method comprising:

• accessing a plurality of training example each comprising:
• a sentence;
• information identifying one or more multi-word expressions occurring in the sentence;
• and
• for each multi-word expression identified as occurring in the sentence, an indication of whether the multi-word expression is a noun multi-word expression or a verb multi-word expression;
• for each of the plurality of training examples:
• subjecting the training example's sentence to natural language preprocessing to obtain natural language preprocessing results;
• for each of a plurality of constituent models, invoking the constituent model against the training example's sentence and natural language preprocessing results to obtain a constituent model result for the training example's sentence that identifies one or more portions of the training example's sentence each as a multi-word expression and specifies whether each multi-word expression is a noun multi-word expression or a verb multi-word expression;
• constructing a training observation corresponding to the training example that comprises:
• independent variable values comprising:
• the training example's sentence;
• the natural language preprocessing results for the training example's sentence;
• and
• the constituent model result obtained for each of the plurality of constituent models;
• and
• dependent variable values comprising:
• the training example's information identifying one or more multi-word phrases occurring in the sentence;
• and
• the training example's indications, for each multi-word phrase identified as occurring in the sentence, of whether the multi-word phrase is a noun multi-word expression or a verb multi-word expression;
• and
• using the constructed training observation to train the machine learning model to predict dependent variable values based on independent variable values.

One or more instances of computer-readable media collectively having contents configured to cause a computing system to perform a method, none of the instances of computer-readable media constituting a transitory signal per se, the method comprising:

• receiving a sentence;
• subjecting the received sentence to natural language preprocessing to obtain natural language preprocessing results for the received sentence;
• for each of a plurality of constituent models, invoking the constituent model against the received sentence and the natural language preprocessing results for the received sentence to obtain a constituent model result for the received sentence that identifies one or more portions of the received sentence each as a multi-word expression and specifies whether each multi-word expression is a noun multi-word expression or a verb multi-word expression;
• and
• applying a trained machine learning model to the received sentence, the natural language preprocessing results for the received sentence, and the constituent model results to obtain a prediction identifying one or more multi-word expressions occurring in the received sentence, and, for each multi-word expression identified as occurring in the sentence, an indication of whether the multi-word expression is a noun multi-word expression or a verb multi-word expression.