Abstract: A processor may transcribe an electronic representation of a dialogue. The processor may identify one or more question-answer pairs from the electronic representation. The processor may generate based upon the one or more identified question-answer pairs a summary of the dialogue. The processor may display the summary of the dialogue to a user.

Abstract: An example system includes a processor to receive unlabeled data, few-shot training data, and a pre-trained model. The processor can split the unlabeled data into a number of groups corresponding to different perspectives. The processor can generate weakly labeled data for each of the number of groups using a respective associated heuristic. The processor can inter-train a model for each different perspective based on respective weakly labeled data. The processor can fine-tune each inter-trained model based on the few-shot training data for each different perspective to generate a final model for each different perspective.

Abstract: A processor may transcribe an electronic representation of a dialogue. The processor may identify one or more question-answer pairs from the electronic representation. The processor may generate based upon the one or more identified question-answer pairs a summary of the dialogue. The processor may display the summary of the dialogue to a user.

Abstract: In an approach to improve abstract summarization with question and answer rewards embodiments generate, by a question and answer generator, questions and answers corresponding to a generated summary. Further, embodiments evaluate received answers for the generated questions by analyzing received answers associated with the generated summary against answers received for an original summary, and calculate a reward based on the similarity between answers associated with generated summary and the original summary. Additionally, embodiments update the generation model by applying the calculated reward to further train the summary generation model.

Abstract: Summarization of customer service dialogs by: receiving, as input, a two-party multi-turn dialog; applying a trained next response prediction (NRP) machine learning model to the received dialog, to determine a level of significance of each utterance in the dialog with respect to performing an NRP task over the dialog; assigning a score to each of the utterances in the dialog, based, at least in part, on the determined level of significance; and selecting one or more of the utterances for inclusion in an extractive summarization of the dialog, based, at least in part, on the assigned scores.

Abstract: An example system includes a processor to receive a summary of a conversation to be generated. The processor can input the summary into a trained summary-grounded conversation generator. The processor can receive a generated conversation from the trained summary-grounded conversation generator.

Abstract: Method and apparatus for training and using a classifier for words. Embodiments include receiving a first plurality of sentences comprising a first word that is associated with a class and a second plurality of sentences comprising a second word that is not associated with the class. Embodiments include training a classifier using positive training data for the class that is based on the first plurality of sentences and negative training data for the class that is based on the second plurality of sentences. Embodiments include determining a measure of correlation between a third word and the class by using a sentence comprising the third word as an input to the classifier. Embodiments include using the measure of correlation to perform an action selected from the following list: selecting content to provide to a user; determining an automatic chat response; or filtering a set of content.

Abstract: Method and apparatus for training and using a classifier for words. Embodiments include receiving a first plurality of sentences comprising a first word that is associated with a class and a second plurality of sentences comprising a second word that is not associated with the class. Embodiments include training a classifier using positive training data for the class that is based on the first plurality of sentences and negative training data for the class that is based on the second plurality of sentences. Embodiments include determining a measure of correlation between a third word and the class by using a sentence comprising the third word as an input to the classifier. Embodiments include using the measure of correlation to perform an action selected from the following list: selecting content to provide to a user; determining an automatic chat response; or filtering a set of content.

Abstract: Method, system, and computer program product are provided for sentiment estimation of a web browsing user. The method includes: estimating for pages of a website a sentiment based on background content; receiving a path of pages browsed by a user to a current page; and estimating the user's sentiment to a current page based on the path taken to the current page and the sentiments based on the background content of the visited pages. The method may also include dynamically changing website content provided to the user based on the user's estimated sentiment to a current page.

Abstract: A method and system for searching numerical terms are provided. The method includes processing a numerical term to a stem, the stem being in the form of a number, and a distance measurement of the numerical term to the stem, wherein a numerical term is a string of characters identified as a number by a numeric parser, and indexing the numerical term in a search index by the stem for search retrieval. The method includes receiving a query and processing the query to divide it into terms, including a numerical term. The numerical terms of the query are also processed to a stem and a distance measurement and document results are retrieved for the stem from an index.

Abstract: A method for information retrieval includes extracting from a video document visual data items and textual data items that occur in the document at respective occurrence times. Indexing records, which index both the visual and the textual data items by their respective occurrence times, are constructed and stored in a memory.

Abstract: A computer-implemented method for searching a corpus of documents includes defining a query as a twig including a root annotation operator having an associated tag specifying a span and having an associated expression indicative of one or more terms whose occurrence within the span will satisfy the query. An object is recursively selected from a group of objects that consists of the tag and the expression, and is used in advancing through the corpus until a candidate document is found that contains the tag and satisfies the expression. The candidate document is evaluated to determine whether the one or more terms indicated by the expression occur within the span in the candidate document so as to satisfy the annotation operator.

Abstract: A method and system for searching numerical terms are provided. The method includes processing a numerical term to a stem, the stem being in the form of a number, and a distance measurement of the numerical term to the stem, wherein a numerical term is a string of characters identified as a number by a numeric parser, and indexing the numerical term in a search index by the stem for search retrieval. The method includes receiving a query and processing the query to divide it into terms, including a numerical term. The numerical terms of the query are also processed to a stem and a distance measurement and document results are retrieved for the stem from an index.

Abstract: A method for information retrieval includes extracting from a video document visual data items and textual data items that occur in the document at respective occurrence times. Indexing records, which index both the visual and the textual data items by their respective occurrence times, are constructed and stored in a memory.

Abstract: A computer-implemented method for searching a corpus of documents includes defining a query as a twig including a root annotation operator having an associated tag specifying a span and having an associated expression indicative of one or more terms whose occurrence within the span will satisfy the query. An object is recursively selected from a group of objects that consists of the tag and the expression, and is used in advancing through the corpus until a candidate document is found that contains the tag and satisfies the expression. The candidate document is evaluated to determine whether the one or more terms indicated by the expression occur within the span in the candidate document so as to satisfy the annotation operator.

Abstract: A computer-implemented method, for searching a corpus of documents having an index, includes receiving a complex query, which includes a plurality of words conjoined by operators including a root operator and at least one intermediate operator. Respective advancement potentials are assigned to the words in the complex query. A query processor applies a consultation method to the words and operators in the complex query in order to choose one of the words responsively to the advancement potentials. The query processor advances through the index in order to find a document containing the chosen one of the words, and evaluates the document to determine whether the document satisfies the complex query.