Abstract: A method for determining an application error from a screenshot includes receiving, by a computing device, a computer application screenshot image indicating a computer error has occurred. The computing device analyzes the computer application screenshot image using a machine learning image analysis to determine one or more error regions in the computer application screenshot image. The computing device further processes the analyzed computer application screenshot to extract text from the one or more error regions in the computer application screenshot image. The computing device determines the application error based upon the extracted text. The computing device further automatically applies an automated error fix based upon the determined application error.

Abstract: A method modifies a social media post in response to determining that a hashtag for the social media post diverges from how the hashtag is used by other social media posts. A computing device receives a social media post, which contains a hashtag, and analyzes the social media post using natural language processing and a sentiment analysis to determine a sense and sentiment of the social media post. The computing device receives a plurality of reference social media posts, where each of the reference social media posts contains the hashtag, and utilizes the natural language processing and the sentiment analysis to determine a sense and sentiment of the reference social media posts. In response to determining that the sense and sentiment of the social media post do not match the sense and sentiment of the reference social media posts, the computing device causes the social media post to be altered.

Abstract: A method for determining an application error from a screenshot includes receiving, by a computing device, a computer application screenshot image indicating a computer error has occurred. The computing device analyzes the computer application screenshot image using a machine learning image analysis to determine one or more error regions in the computer application screenshot image. The computing device further processes the analyzed computer application screenshot to extract text from the one or more error regions in the computer application screenshot image. The computing device determines the application error based upon the extracted text. The computing device further automatically applies an automated error fix based upon the determined application error.

Abstract: A method modifies a social media post in response to determining that a hashtag for the social media post diverges from how the hashtag is used by other social media posts. A computing device receives a social media post, which contains a hashtag, and analyzes the social media post using natural language processing and a sentiment analysis to determine a sense and sentiment of the social media post. The computing device receives a plurality of reference social media posts, where each of the reference social media posts contains the hashtag, and utilizes the natural language processing and the sentiment analysis to determine a sense and sentiment of the reference social media posts. In response to determining that the sense and sentiment of the social media post do not match the sense and sentiment of the reference social media posts, the computing device causes the social media post to be altered.

Abstract: Providing answers to questions. A LAT, a rankable criterion, and a probabilistic criterion are identified in a received query. A set of candidate answers to the question that are instances of the LAT identified in a first data source is generated. Based on the rankable criterion, the candidate answers are ranked. Each candidate answer is assigned a likelihood that the candidate answer satisfies the probabilistic criterion, based on a statistic of occurrences of terms related to the candidate answer and the probabilistic criterion in text passages from a second data source. One or more candidate answers are selected based on the rank and the likelihood of the candidate answers. In another aspect of the invention, a Boolean criterion is also identified in the query and the set of candidate answers is reduced by applying the Boolean criterion.

Abstract: Providing answers to questions. A LAT, a rankable criterion, and a probabilistic criterion are identified in a received query. A set of candidate answers to the question that are instances of the LAT identified in a first data source is generated. Based on the rankable criterion, the candidate answers are ranked. Each candidate answer is assigned a likelihood that the candidate answer satisfies the probabilistic criterion, based on a statistic of occurrences of terms related to the candidate answer and the probabilistic criterion in text passages from a second data source. One or more candidate answers are selected based on the rank and the likelihood of the candidate answers. In another aspect of the invention, a Boolean criterion is also identified in the query and the set of candidate answers is reduced by applying the Boolean criterion.

Abstract: Providing answers to questions. A LAT, a rankable criterion, and a probabilistic criterion are identified in a received query. A set of candidate answers to the question that are instances of the LAT identified in a first data source is generated. Based on the rankable criterion, the candidate answers are ranked. Each candidate answer is assigned a likelihood that the candidate answer satisfies the probabilistic criterion, based on a statistic of occurrences of terms related to the candidate answer and the probabilistic criterion in text passages from a second data source. One or more candidate answers are selected based on the rank and the likelihood of the candidate answers. In another aspect of the invention, a Boolean criterion is also identified in the query and the set of candidate answers is reduced by applying the Boolean criterion.

Abstract: Providing answers to questions. A LAT, a rankable criterion, and a probabilistic criterion are identified in a received query. A set of candidate answers to the question that are instances of the LAT identified in a first data source is generated. Based on the rankable criterion, the candidate answers are ranked. Each candidate answer is assigned a likelihood that the candidate answer satisfies the probabilistic criterion, based on a statistic of occurrences of terms related to the candidate answer and the probabilistic criterion in text passages from a second data source. One or more candidate answers are selected based on the rank and the likelihood of the candidate answers. In another aspect of the invention, a Boolean criterion is also identified in the query and the set of candidate answers is reduced by applying the Boolean criterion.

Abstract: Automated question answering is disclosed that relates to the selection of an answer to a question from a pool of potential answers which are manually or automatically extracted from a large collection of textual documents. The a feature extraction component, a feature combination component, an answer selection component, and an answer presentation component, among others, are included. The input to the system is a set of one or more natural language questions and a collection of textual document The output is a (possibly ranked) set of factual answers to the questions, these answers being extracted from the document collection.

Abstract: Automated question answering is disclosed that relates to the selection of an answer to a question from a pool of potential answers which are manually or automatically extracted from a large collection of textual documents. The a feature extraction component, a feature combination component, an answer selection component, and an answer presentation component, among others, are included. The input to the system is a set of one or more natural language questions and a collection of textual document. The output is a (possibly ranked) set of factual answers to the questions, these answers being extracted from the document collection.

Abstract: Automated question answering is disclosed that relates to the selection of an answer to a question from a pool of potential answers which awe manually or automatically extracted from a large collection of textual documents. The a feature extraction component, a feature combination component, an answer selection component, and an answer presentation component, among others, are included. The input to the system is a set of one or more natural language questions and a collection of textual document The output is a (possibly ranked) set of factual answers to the questions, these answers being extracted from the document collection.

Abstract: The present invention is a system, method, and program product that comprises a computer with a collection of documents to be searched. The documents contain free form (natural language) text. We define a set of labels called QA-Tokens, which function as abstractions of phrases or question-types. We define a pattern file, which consists of a number of pattern records, each of which has a question template, an associated question word pattern, and an associated set of QA-Tokens. We describe a query-analysis process which receives a query as input and matches it to one or more of the question templates, where a priority algorithm determines which match is used if there is more than one. The query-analysis process then replaces the associated question word pattern in the matching query with the associated set of QA-Tokens, and possibly some other words. This results in a processed query having some combination of original query tokens, new tokens from the pattern file, and QA-Tokens, possibly with weights.

Abstract: After a categorization process has been run, the scores of the top-two ranking categories along with the size or number of features in the object being categorized, are passed to a confidence assignment process. This determines a value for the confidence in the top category based on the evidence afforded by the input parameters. The magnitude of this confidence value will determine whether the system can accept the automatic categorization results, or whether human involvement is required. This invention also describes the process of determining the optimal value of an internal scaling parameter in the confidence assignment process. The construction of a threshold table based on this parameter is also described. The threshold table matches confidence values against error levels. For a given error rate the previously assigned confidence determines whether the categorization results can be accepted without need for human intervention.

Abstract: The present invention is a system and method for determining whether the best category for an object under investigation is a mixture of preexisting categories, and how the mixture is constituted. This invention is useful both for suggesting the need for new categories, and for a fixed set of categories, determining whether a document should be assigned to multiple categories. The objects of the categorization system are typically, but need not be, documents. Categorization may be by subject-matter, language or other criteria. The invention causes extra information to be stored in a category index, so that the determination of mixed categories using the methods presented here is performed extremely efficiently.

Abstract: A computer system has a document collection of one or more documents and one or more indexes that each include an inverted file with one or more terms. Each of the terms is associated with one or more document identifiers. The index further includes a document catalog that associates each of the document identifiers with one or more attributes, either intrinsic or non intrinsic. A search engine process produces a hit list having one or more hit list entries. Each hit list entry, with one or more hit list attributes, is associated with one of the documents that is determined by the search engine to be relevant to the query. A formatter processor selects one or more of the hit list attributes, identified by a hit list attribute selector and then compares the selected attributes of two or more entries on the hit list to determine whether or not documents associated with these entries are duplicate instances of one another.

Abstract: Topically relevant objects in an object database are first identified using any generally known methods to obtain a set of topically relevant objects (topically relevant set). Parents, and in alternative embodiments other ancestors, of one or more of the topically relevant objects are identified according to directional structural relationships that the parents have with respect to the topically relevant objects. These objects form a set of structurally relevant objects (structurally relevant set). In some embodiments, the user query identifies one or more of these structural relationships. The topically relevant objects are then organized under one or more of their respective parents to form a hierarchy level of both (topically relevant and structurally relevant) sets of objects. In some preferred embodiments, the process can iterate to create more than one hierarchy level.

Abstract: In an information retrieval system, a query issued by the user is analyzed by a query engine into query elements. After the query has been evaluated against the document collections, a resulting hit list is presented to the user, e.g., as a table. The presented hit list displays not only an overall rank of a document but also a contribution of each query element to the rank of the document. The user can reorder the hit list by prioritizing the contribution of individual query elements to override the overall rank and by assigning additional weight(s) to those contributions.