Abstract: A method and system are provided for assisting users in a conversation. The method includes identifying concepts in the conversation. The method further includes linking identified concepts in the conversation by matching the identified concepts in the conversation to concepts in a knowledge base. The method also includes generating and displaying on the display device, one or more context dependent suggestions for the conversation based on attributes and values associated with the linked concepts in the knowledge base.

Abstract: A method and system are provided for assisting users in a conversation. The method includes identifying concepts in the conversation. The method further includes linking identified concepts in the conversation by matching the identified concepts in the conversation to concepts in a knowledge base. The method also includes generating and displaying on the display device, one or more context dependent suggestions for the conversation based on attributes and values associated with the linked concepts in the knowledge base.

Abstract: An automated speech processing method, system and computer program product are disclosed. In one embodiment, a speech-to-text (STT) engine is used for converting an audio input to text data in a source language, and a machine translation (MT) engine is used for translating this text data to text data in a target language. In this embodiment, the text data in the target language is rendered on a display device, and different visualization schemes are applied to different parts of the rendered text data based on defined characteristics of the STT engine and the MT engine. In one embodiment, the defined characteristics include a defined confidence value representing the accuracy of the rendered text. For example, this confidence value may be based on both the accuracy of the conversion of the audio input and the accuracy of the translation of the text data to the target language.

Abstract: A method, apparatus and article of manufacture for determining annotation quality, including obtaining N annotations on an artifact, wherein each annotation includes a feature and N annotations include three or more annotations including an annotation provided by a first human annotator, zero or more human annotations and zero or more imposter annotations, selectively displaying the N annotations to the first human annotator, wherein the annotation provided by the first human annotator is completely visible and each of the other N?1 annotations includes a feature hidden, and determining to annotation quality of one of the N?1 annotations based on input from the first human annotator regarding the displayed annotations, wherein ability of the first human annotator to identify imposter annotations or recognize that no imposter annotation exists is gated by the quality of the first human's annotation via hiding other annotations and requiring a probe based on the first human annotator's annotation.

Abstract: An automated speech processing method, system and computer program product are disclosed. In one embodiment, a speech-to-text (STT) engine is used for converting an audio input to text data in a source language, and a machine translation (MT) engine is used for translating this text data to text data in a target language. In this embodiment, the text data in the target language is rendered on a display device, and different visualization schemes are applied to different parts of the rendered text data based on defined characteristics of the STT engine and the MT engine. In one embodiment, the defined characteristics include a defined confidence value representing the accuracy of the rendered text. For example, this confidence value may be based on both the accuracy of the conversion of the audio input and the accuracy of the translation of the text data to the target language.