Abstract: Computer technology for determining and tagging parts of speech in a text (that is PoS ragging), where the context used by the natural language processing machine logic (for example, NLP software) includes both: (i) other words in the sentence under analysis where a given word to be tagged appears; and (ii) words in the other sentences besides the sentence under analysis. Other context sentences may be selected randomly, by Next Sentence Prediction technology and/or by choosing sentences in textual proximity to the sentence under analysis.

Abstract: Methods and systems for sentence acceptability judgment. A word frequency distribution for a predetermined textual data set is obtained. A replacement rate is determined based on an obtained word frequency distribution for a predetermined textual data set and every occurrence of a word having a frequency lower than the replacement rate is replaced in text of a training data set with a corresponding tag to generate revised text of the training data set (the training data set comprising at least a portion of the predetermined textual data set). A plurality of language models are trained with the revised text and a best performing trained language model of the plurality of trained language models is selected. An acceptability of each sentence of a plurality of candidate sentences is rated using the selected trained language model and a best sentence of the plurality of candidate sentences is selected based on the ratings.

Abstract: A motion state monitoring system, a training support system, a motion state monitoring method, and a program capable of suitably managing measurement results according to an attaching direction of a sensor are provided. A motion state monitoring system according to the present disclosure monitors a motion state of a target part of a subject's body. The motion state monitoring system includes an acquisition unit, an attaching direction detection unit, and a control processing unit. The acquisition unit acquires sensing information of a sensor attached to the target part. The attaching direction detection unit detects an attaching direction of the sensor. The control processing unit outputs information related to the sensing information in association with the attaching direction.

Abstract: A computer-implemented method is provided for estimating output confidence of a black box Application Programming Interface (API). The method includes generating paraphrases for an input text. The method further includes calculating a distance between the input text and each respective one of the paraphrases. The method also includes sorting the paraphrases in ascending order of the distance. The method additionally includes selecting a top predetermined number of the paraphrases. The method further includes inputting the input text and the selected paraphrases into the API to obtain an output confidence score for each of the input text and the selected paraphrases. The method also includes estimating, by a hardware processor, the output confidence of the input text from a robustness of output scores of the input text and the selected paraphrases.

Abstract: A computer-implemented method for graphical user interface element accessibility is disclosed. The computer-implemented method includes generating a graphical user interface element rendered on a web page as a virtual button displayed on a display screen of a device. The virtual button is not rendered on the webpage and includes an invocable function mapped to the graphical user interface element. The computer-implemented method further includes moving the virtual button along a periphery of the display screen of the device. A rate at which the virtual button moves along the periphery of the display screen of the device is dynamically modified based on a detected identity of a user of the device.

Abstract: A computer-implemented method is provided for data augmentation. The method includes receiving a set of different base models already pretrained and a set of different test cases. The method further includes collecting a plurality of prediction results of the set of different test cases from the set of different base models. The method also includes identifying a test case as a candidate for the data augmentation based on a number of models in the set of different base models which fail to solve the test case. The method additionally includes augmenting, by a processor device, the identified test case with additional data to form an augmented training dataset. The method further includes retraining at least some of the different base models with the augmented training dataset.

Abstract: A server device 100 includes an estimation neutral expression parameter generation unit 104 generating an estimation neutral expression parameter indicating a face neutral expression estimated from a dialogue sound generated in accordance with dialogue information of a user, an appearance neutral expression parameter generation unit 107 generating an appearance neutral expression parameter indicating a face neutral expression appearing on a captured face image obtained by capturing a face of an operator, and a neutral expression parameter transmission unit 110 selecting either the estimation neutral expression parameter or the appearance neutral expression parameter to transmit to a client device, and in the client device, by applying a neutral expression specified on the basis of the neutral expression parameter transmitted from the server device 100 to a target face image, a face image of a neutral expression corresponding to the dialogue sound or the captured face image of the operator generated by the ser

Abstract: A computer-implemented method is provided for estimating output confidence of a black box Application Programming Interface (API). The method includes generating paraphrases for an input text. The method further includes calculating a distance between the input text and each respective one of the paraphrases. The method also includes sorting the paraphrases in ascending order of the distance. The method additionally includes selecting a top predetermined number of the paraphrases. The method further includes inputting the input text and the selected paraphrases into the API to obtain an output confidence score for each of the input text and the selected paraphrases. The method also includes estimating, by a hardware processor, the output confidence of the input text from a robustness of output scores of the input text and the selected paraphrases.

Abstract: Computer hardware and/or software that performs the following operations: (i) identifying a test input for a system under test, the system under test configured to determine a strength of a relationship between received inputs; (ii) determining, from a data store of potential inputs, a set of inputs for which the strength of the relationship to the identified test input according to the system under test meets a first threshold; and (iii) utilizing the determined set of inputs to test a transitive consistency of an output of the system under test.

Abstract: A computer implemented method of hypothesis scoring based on causal relationships is provided. The computer implemented method includes creating a causal relationship model utilizing a plurality of hypotheses and a causal relationship between each of two or more pairs of hypotheses, and obtaining pro and con sentiment scores for each hypothesis utilizing a scoring function. The computer implemented method further includes assigning the obtained pro and con sentiment scores to each hypothesis in the causal relationship model, and propagating the pro and con sentiment scores from leaf hypotheses to a root hypothesis utilizing axioms to test the propagating scores for reasonableness. The computer implemented method further includes determining a final pro and con score for the root hypothesis, and presenting the final pro and con scores representing a prediction of the hypotheses to a user.

Abstract: Dependency trees are generated for questions and answers of a question answering (QA) corpus in which the answers are associated with the questions. Generating the dependency trees includes identifying root nodes. A word near an identified root node of one of the questions is compared to words of answers associated with the one of the questions. The word is determined to be in less than a threshold number of the associated answers. The word is identified as a stop word.

Abstract: A method is provided for dictionary expansion. The method acquires an object from a user and adds the object to a set of objects previously acquired from the user that form an expandable dictionary. The method calculates a centroid based on the set. The method calculates a similarity score of each of a plurality of objects relative to the centroid for each of a plurality of object features to calculate a weighted sum of similarity scores for each of the plurality of objects. The method presents candidate objects selected among the plurality of objects based on the weighted sum. The method acquires, from the user, a preferred candidate object among the candidate objects. The method updates weights of the plurality of features to maximize the weighed sum of similarity scores for the preferred candidate object. The method expands the dictionary by adding the preferred candidate object to the expandable dictionary.

Abstract: A computer-implemented method is provided for data augmentation. The method includes receiving a set of different base models already pretrained and a set of different test cases. The method further includes collecting a plurality of prediction results of the set of different test cases from the set of different base models. The method also includes identifying a test case as a candidate for the data augmentation based on a number of models in the set of different base models which fail to solve the test case. The method additionally includes augmenting, by a processor device, the identified test case with additional data to form an augmented training dataset. The method further includes retraining at least some of the different base models with the augmented training dataset.

Abstract: A method is provided for determining support of a hypothesis by opinion sentences. The method converts sentence structures in the opinion sentences using various sentence structure conversion methods to obtain converted opinion sentences. For each converted opinion sentence, the method calculates a difference between a proximity label value indicating proximity to the hypothesis and an intermediate score before and after a conversion, adopts the conversion responsive to a condition being met relative to the difference, and adopts the opinion sentence instead responsive to the condition being unmet. The method creates sub-opinions using the various methods applied to adopted conversions and opinion sentences, and obtains an intermediate score for each sub-opinion.

Abstract: A computer-implemented method, a computer program product, and a computer system for dependency tree-based data augmentation for sentence well-formedness judgement. A computer applies a dependency parser to generate a dependency tree for a sentence. The computer removes one or more nodes in the dependency tree, according to a removal ratio for a predetermined rating score. The computer generates, from the dependency tree, a partial tree for the sentence. The computer outputs a rated sentence based on the partial tree. The rated sentence is used as training data.

Abstract: Frequent sequences extracted from a set of documents according to a common rule are obtained. Based on comparing occurrence frequencies of various sequences, confidence of the first frequent sequence being a label expression representing a document part in a target document is evaluated. Keywords are extracted from the target document based on evaluation of the confidence.

Abstract: A system and method for expanding a question and answer (Q&A) database. The method includes obtaining a set of Q&A documents and speech recognition results, each Q&A document in the set having an identifier, and each speech recognition result having an identifier common with the identifier of a relevant Q&A document, and adding one or more repetition parts extracted from the speech recognition results to a corresponding Q&A document in the set to generate an expanded set of Q&A documents for increasing Q&A document extraction accuracy.

Abstract: Methods and systems for information retrieval include analyzing audio data to produce word hypotheses. Displaying the word hypotheses in motion at different respective speeds at once across a graphical display. Information is retrieved in accordance with one or more selected terms from the displayed word hypotheses.

Abstract: A method, computer program product and computer system are provided to promote device usage compliance. A processor retrieves a current position for a device. A processor determines at least one nearby location based on the current position of the device. A processor retrieves search data regarding the at least one nearby location. A processor determines compliance information regarding the at least one location based, at least in part, on the search data. A processor configures the device based on the compliance information regarding the at least one location.

Abstract: A method is provided for validating a hypothesis sentence. The method extracts, from a document database D using a hypothesis sentence that includes a causal part and a consequence part, a set D1 of documents related to the causal part. The method extracts, from set D1, a set S of sentences that include expressions of opinion. The method obtains a word list W of words that have a high co-occurrence in the set D1. The method selects, from the set S, a set S1 of sentences that are positionally close to any of the words in the word list W. The method selects, from the set S, a set S2 of sentences that are related to the words in the consequence part of the hypothesis. The method extracts and displays sentences included in both the set S1 and the set S2 as opinion sentences in relation to the hypothesis sentence.