Abstract: Questions about a passage of text that includes a sequence of two or more sentences are generated. Each question covers the content of a plurality of sentences in the passage, and includes a context portion of the passage and a question statement that is contextually related to the context portion of the passage. A user is also provided with questions about a passage of text they are reading. Each question is presented to the user, where this presentation includes displaying the context portion of the passage and the question statement that is contextually related to the context portion of the passage.

Abstract: Questions about a passage of text that includes a sequence of two or more sentences are generated. Each question covers the content of a plurality of sentences in the passage, and includes a context portion of the passage and a question statement that is contextually related to the context portion of the passage. A user is also provided with questions about a passage of text they are reading. Each question is presented to the user, where this presentation includes displaying the context portion of the passage and the question statement that is contextually related to the context portion of the passage.

Abstract: Questions about a passage of text that includes a sequence of two or more sentences are generated. Each question covers the content of a plurality of sentences in the passage, and includes a context portion of the passage and a question statement that is contextually related to the context portion of the passage. A user is also provided with questions about a passage of text they are reading. Each question is presented to the user, where this presentation includes displaying the context portion of the passage and the question statement that is contextually related to the context portion of the passage.

Abstract: Questions about a passage of text that includes a sequence of two or more sentences are generated. Each question covers the content of a plurality of sentences in the passage, and includes a context portion of the passage and a question statement that is contextually related to the context portion of the passage. A user is also provided with questions about a passage of text they are reading. Each question is presented to the user, where this presentation includes displaying the context portion of the passage and the question statement that is contextually related to the context portion of the passage.

Abstract: Computing functionality converts an input linguistic item into a normalized linguistic item, representing a normalized counterpart of the input linguistic item. In one environment, the input linguistic item corresponds to a complaint by a person receiving medical care, and the normalized linguistic item corresponds to a definitive and error-free version of that complaint. In operation, the computing functionality uses plural reference resources to expand the input linguistic item, creating an expanded linguistic item. The computing functionality then forms a graph based on candidate tokens that appear in the expanded linguistic item, and then finds a shortest path through the graph; that path corresponds to the normalized linguistic item. The computing functionality may use a statistical language model to assign weights to edges in the graph, and to determine whether the normalized linguistic incorporates two or more component linguistic items.

Abstract: One or more automatically generated questions regarding subject matter of a body of text are presented (e.g., displayed) to a user. A user input of one or more answers to the one or more automatically generated questions is received, and the body of text is presented to the user, adapted based on a correctness of the one or more answers. The body of text is adapted to emphasize portions of the body of text that are estimated as not having been mastered by the user based on estimated probabilities of user mastery of the various portions of the body of text generated based on the correctness of the one or more answers.

Abstract: Functionality is described herein for converting an input linguistic item into a normalized linguistic item, representing a normalized counterpart of the input linguistic item. In one environment, the input linguistic item corresponds to a complaint by a person receiving medical care, and the normalized linguistic item corresponds to a definitive and error-free version of that complaint. In operation, the functionality uses plural reference resources to expand the input linguistic item, creating an expanded linguistic item. The functionality then forms a graph based on candidate tokens that appear in the expanded linguistic item, and then finds a shortest path through the graph; that path corresponds to the normalized linguistic item. The functionality may use a statistical language model to assign weights to edges in the graph, and to determine whether the normalized linguistic incorporates two or more component linguistic items.

Abstract: Methods and computer-readable media are provided for summarizing the content of a plurality of documents and presenting the results of such multiple-document summarization to a user in such a way that the user is able to quickly and easily discern what, if any, unique information each document contains. Each sentence of each document is assigned a score based upon the perceived importance of the information contained therein. The sentences receiving the highest scores are then compared with one another to identify and remove any duplicate sentences. The remaining high-scoring sentences are extracted from the corresponding documents and presented to the user, for instance, in a bulleted list format. The user can then simply scan the list and can quickly and easily discern the unique information contained in each document.

Abstract: A summarization system and method. The summarization method includes utilizing a first body of information to obtain a second body of information, which is identified (by a hyperlink, an attachment identifier, a reference, etc.) in the first body of information. A summary of the obtained second body of information is then computed. The computed summary can be displayed to a user and/or stored for later use.

Abstract: A document translation system is described. In various embodiments, the document translation system improves translations of text or speech (“documents”) that are performed by computing devices (“machine translations”). Upon translating a document into a target language, the system can provide a user interface containing both the original document and a translated document. The translated document can include portions that are translated automatically by a machine translation service, and other portions that have been amended or modified by users. Upon providing the translated document, the system may receive input from a user describing portions of the translated document that should be modified. These modifications may then be stored in corpora that the document translation system can employ during future requests to translate the document.

Abstract: Methods and computer-readable media are provided for summarizing the content of a plurality of documents and presenting the results of such multiple-document summarization to a user in such a way that the user is able to quickly and easily discern what, if any, unique information each document contains. Each sentence of each document is assigned a score based upon the perceived importance of the information contained therein. The sentences receiving the highest scores are then compared with one another to identify and remove any duplicate sentences. The remaining high-scoring sentences are extracted from the corresponding documents and presented to the user, for instance, in a bulleted list format. The user can then simply scan the list and can quickly and easily discern the unique information contained in each document.

Abstract: A mining system applies queries to retrieve result items from an unstructured resource. The unstructured resource may correspond to a repository of network-accessible resource items. The result items that are retrieved may correspond to text segments (e.g., sentence fragments) associated with resource items. The mining system produces a structured training set by filtering the result items and establishing respective pairs of result items. A training system can use the training set to produce a statistical translation model. The translation model can be used in a monolingual context to translate between semantically-related phrases in a single language. The translation model can also be used in a bilingual context to translate between phrases expressed in two respective languages. Various applications of the translation model are also described.

Abstract: Document summarization is performed by scoring individual words in sentences in a document or document cluster. Sentences from the document or document cluster are selected to form a summary based on the scores of the words contained in those sentences.

Abstract: A document translation system is described. In various embodiments, the document translation system improves translations of text or speech (“documents”) that are performed by computing devices (“machine translations”). Upon translating a document into a target language, the system can provide a user interface containing both the original document and a translated document. The translated document can include portions that are translated automatically by a machine translation service, and other portions that have been amended or modified by users. Upon providing the translated document, the system may receive input from a user describing portions of the translated document that should be modified. These modifications may then be stored in corpora that the document translation system can employ during future requests to translate the document.

Abstract: The application discloses embodiments of a summarization tool for a dialogue sequence or message thread. In the embodiments disclosed, the summarization tool utilizes a topic shift component to identify a topic start to define a topic group for the dialogue sequence or message thread. A summary component uses the topic start to generate a summary output for the topic group of the dialogue sequence or message thread. In illustrated embodiments, the summary output includes one or more of a context summary, a thread summary, and scope data or information.

Abstract: A summarization system and method. The summarization method includes utilizing a first body of information to obtain a second body of information, which is identified (by a hyperlink, an attachment identifier, a reference, etc.) in the first body of information. A summary of the obtained second body of information is then computed. The computed summary can be displayed to a user and/or stored for later use.

Abstract: The present invention is a method and system for identifying words, text fragments, or concepts of interest in a corpus of text. A graph is built which covers the corpus of text. The graph includes nodes and links, where nodes represent a word or a concept and links between the nodes represent directed relation names. A score is then computed for each node in the graph. Scores can also be computed for larger sub-graph portions of the graph (such as tuples) The scores are used to identify desired sub-graph portions of the graph, those sub-graph portions being referred to as graph fragments.

Abstract: A lexical knowledge base is compiled automatically from a machine-readable source (such as an on-line dictionary or unstructured text). The preferred embodiment of the invention makes use of “backward linking,” by which inverse semantic relations are discerned from the text and used to augment the knowledge base. By this arrangement, on-line dictionaries and other texts can provide formidable sources of “common sense” knowledge about the world.

Abstract: Document summarization is performed by scoring individual words in sentences in a document or document cluster. Sentences from the document or document cluster are selected to form a summary based on the scores of the words contained in those sentences.

Abstract: The present invention can be used in a natural language processing system to determine a relationship (such as similarity in meaning) between two textual segments. The relationship can be identified or determined based on logical graphs generated from the textual segments. A relationship between first and second logical graphs is determined. This is accomplished regardless of whether there is an exact match between the first and second logical graphs. In one embodiment, the first graph represents an input textual discourse unit. The second graph, in one embodiment, represents information in a lexical knowledge base (LKB). The input graph can be matched against the second graph, if they have similar meaning, even if the two differ lexically or structurally.