Abstract: Disclosed are systems, methods, and products for language learning software that automatically extracts text from resources using various natural-language processing features, which can be combined with custom-designed learning activities to offer a needs-based, adaptive learning methodology. The system may receive a text, extract keywords pedagogically valuable to non-native language learning, assign a difficulty score to the text using various linguistic attributes of the text, generate a list of potential distractors for each keyword related to a resource to implement in learning activities. Distractors may be of various types, which are dynamically selected from a distractor store depending on a learning activity chosen to meet a learner's needs. Distractors may vary in difficulty, and may be dynamically selected based on a learner's overall proficiency or based on a learner's abilities in specific language skills.

Abstract: Disclosed are systems, methods, and products for language learning that may extract text from various resources having text, using various natural-language processing features, which can be combined with custom-designed learning activities to offer a needs-based, adaptive learning methodology. The system may receive a resource, extract keywords pedagogically valuable to non-native language learning and academic exercises. Metadata describing various aspects of resources from which keywords are extracted may be associated with keywords. Metadata describing various aspects of keywords may also be associated with keywords. Extracted keywords may be stored into a keyword store along with any metadata associated with keywords.

Abstract: Disclosed are systems, methods, and products for language learning that automatically extracts keywords from resources using various natural-language processing product features, which can be combined with custom-designed learning activities to offer a needs-based, adaptive learning methodology. The system may receive resources having text and then determine a text difficulty score that predicts how difficult the resource is for language learners based on any number of factors, including any number of semantic and syntactic features of the text. Training resources labeled with metadata may be used to train a statistical model for determining difficulty scores of newly received text. Resources may be grouped based on difficulty score, and groups of resources may correspond to language learners' proficiency levels.

Abstract: Methods and apparatus are provided for classifying a spoken utterance into at least one of a plurality of categories. A spoken utterance is translated into text and a confidence score is provided for one or more terms in the translation. The spoken utterance is classified into at least one category, based upon (i) a closeness measure between terms in the translation of the spoken utterance and terms in the at least one category and (ii) the confidence score. The closeness measure may be, for example, a measure of a cosine similarity between a query vector representation of said spoken utterance and each of said plurality of categories. A score is optionally generated for each of the plurality of categories and the score is used to classify the spoken utterance into at least one category. The confidence score for a multi-word term can be computed, for example, as a geometric mean of the confidence score for each individual word in the multi-word term.