Abstract: A system and methods and data structure for quantitatively assessing associations between terms or symbols in natural language and non-natural language contents. Some of the terms represent objects or concepts or semantic attributes; some other terms represent properties associated with the objects or concepts or attributes. The methods include obtaining a first group of text contents, specifying a target term or symbol, and identifying contextual attributes of the target term or symbol. The contextual attributes include grammatical and semantic attributes as well as positional and distance attributes. Association strength values are calculated for related terms or symbols based on the contextual attributes of the target term or symbol, and terms or symbols are selected to represent the properties of an object or concept or attribute.

Abstract: System and methods are disclosed for determining the connotation or sentiment type of a text unit comprising multiple terms and with a grammatical structure, such as subject+verb, verb+object, adjective+noun, noun+noun, noun+preposition+noun. The connotation or sentiment type of the text unit is determined by applying context rules where the context of the grammatical structure may change the inherent or default connotations of individual terms in the text unit. The methods provide a solution to the challenge of correctly or accurately determining the sentiment type of various linguistic structures under different context, and to the simplistic approach of using the inherent connotation of individual terms for the linguistic structure containing such terms.

Abstract: A system, methods and user interface are disclosed for determining the connotation or sentiment type of a text unit comprising multiple terms and with a grammatical structure, such as subject+verb, verb+object, adjective+noun, noun+noun, noun+preposition+noun, and for highlighting or extracting such text units. The connotation or sentiment type of the text unit is determined by applying context rules where the context of the grammatical structure may change the inherent or default connotations of individual terms in the text unit. The methods provide a solution to the challenge of correctly or accurately determining the sentiment type of various linguistic structures under different context, and to the simplistic approach of using the inherent connotation of individual terms for the linguistic structure containing such terms, and highlighting only such terms without considering the contextual effects.

Abstract: System and methods are disclosed for quantitatively assessing information in natural language contents related to an object name, or a concept or topic name, and for determining the relevance between a text content and one or more concepts or topics. The methods include identifying the grammatical or semantic attributes and other contextual information of terms in the text content, and retrieving an object-specific dataset related to the object name, or an equivalent to a concept or a topic name. The data set includes property names and association-strength values. The methods further include matching the terms in the text content with the property terms in the dataset, and calculating a score as a quantitative measure of the relevance between the text content and the concept or topic.

Abstract: A system and methods and data structure for quantitatively assessing associations between terms or symbols in natural language and non-natural language contents. Some of the terms or symbols represent objects or concepts or semantic attributes; some other terms or symbols represent properties associated with the objects or concepts or semantic attributes. The methods include obtaining a first group of text contents or non-natural language data content, specifying a target term or symbol or semantic attribute, and identifying contextual attributes of the target term or symbol. The contextual attributes include grammatical and semantic attributes as well as positional and distance attributes. Association strength values are calculated for related terms or symbols based on the contextual attributes of the target term or symbol or semantic attribute.

Abstract: Systems, methods, and data structures are disclosed for discovering and representing object-properties associations using textual data. The object can be a physical object or an abstract object. A system comprises a storage medium configured to store a data package containing one or more terms each comprising a word or phrase as names of properties or attributes associated with the object. The terms in the data package can collectively serve as an associative representation of the object. The data package can be used in a system for searching and classifying information based on concepts. The data package can be obtained by analyzing a plurality of text contents each containing a term defined as the object name, and other terms that are not the object name, and counting the number of occurrences of the text contents containing the object name and non-object name, or using a weighting co-efficient based on the grammatical roles of the terms, or using the frequencies of the terms in the external documents.

Abstract: A system with methods and user interface for discovering and presenting information in text content with different view formats, including steps of identifying the grammatical, semantic, contextual, and topical attributes of the words or phrases in the text content, and providing user interface objects to allow the user to specify an attribute of the words or phrases in the content and perform an action on the words or phrases. The supported actions include extracting, displaying in a specific format, showing or hiding, or highlighting the words or phrases.

Abstract: A computer-assisted method for discovering topics and categorizing contents in a document includes the steps of calculating an importance score for a term based on grammatical roles, parts of speech, and semantic attributes, selecting terms based on the importance score values of the respective terms, and outputting terms comprising the selected term to represent topics in the document, and building a category structure based on the selected terms.

Abstract: System and methods are disclosed for discovering topics in sub-segments of documents, and extracting terms from a sub-segment representing topics or summaries of the sub-segment, and displaying such terms in connection with the sub-segment or with the document, which can also function as automatically generated tags or labels for the segments or for the documents. Methods are also disclosed for building search indexes based on specific sub-segments of documents, such that, users can search for contents in a specific segment of the document. One embodiment of such a search index is with emails, blogs, and forum articles that typically contain segmented contents added at different times or by different authors in a format known as a thread, and searching in a specific segment such as the most recently added segment can help quickly find the most relevant information without repeating the same information in other segments in the thread.

Abstract: The present application discloses a computer-assisted method for determining relevance between a first text content and a second text content, and displaying relevant contents.

Abstract: A computer-assisted method for discovering topics in a document collection is disclosed. The method includes obtaining a group of text units in the document collection, tokenizing the words in the group of text units to produce a plurality of tokens that include a jth token, and adding a weighting coefficient to a parameter token_j_count for each text unit in the first group that includes the jth token. The weighting coefficient is dependent on the grammatical role of the jth token. The method includes calculating an internal term prominence value (ITP) using token_j_count, selecting one or more tokens from the tokens based on the ITP values of the respective tokens, and outputting the one or more selected tokens as topic terms associated with the document collection.

Abstract: System and methods are disclosed for determining the connotation or sentiment type of a text unit comprising multiple terms and with a grammatical structure, such as subject+verb, verb+object, adjective+noun, noun+noun, noun+preposition+noun. The connotation or sentiment type of the text unit is determined by applying context rules where the context of the grammatical structure may change the inherent or default connotations of individual terms in the text unit. The methods provide a solution to the challenge of correctly or accurately determining the sentiment type of various linguistic structures under different context, and to the simplistic approach of using the inherent or default connotation of individual terms for the linguistic structure containing such terms.

Abstract: Computer-implemented systems and methods for discovering and presenting important information about a text object are disclosed. The system comprises a user interface for displaying important information extracted from a text object, serving as a preview of the topics, or as an automatically-generated tag or a summary of the content in the text object. The information is discovered by a method based on the grammatical attributes and occurrence information of terms in the text object, as well as in external text objects.

Abstract: A system and methods and user interface are disclosed for searching documents based on conceptual association, and for ranking documents based on content characteristics. A computer processing system receives a query containing a word or phrase that is a name of an object or concept, and can also receive a value for a customized ranking parameter. A computer storage device stores a dataset related to the object or concept name, wherein the dataset includes a plurality of property names and can also include association-strength values. A computer processing system can count a first frequency of a first property name and count a second frequency of a second property name in a document containing text in a natural language, calculate a relevance score as a function of the first frequency and the second frequency, and rank the plurality of documents using their respective relevance scores, and return one or more documents to the user based on the ranking of the plurality of documents.

Abstract: System and methods are disclosed for advertising based on user intention detection. The methods include performing linguistic analysis of user expressions, identifying grammatical or semantic attributes associated with terms in the expression, and their relationships, and determining a relevance score for one or more terms in the expression that are associated with the name of a product or service. Based on the relevance score, an advertisement can be displayed to the user who produced the expression within a given period of time. The user can be a social network user, or an email user, or a text messaging user, or a user of other text communication media. Furthermore, electronic advertising space or time can be sold or auctioned to advertisers based on the relevance score in contrast to the conventional method of being based on keywords.

Abstract: Systems and methods are disclosed for presenting information related to an object including a word or phrase in a text content that is considered as unstructured data. Methods include identifying a term in the text content as a candidate for presenting such information. Once the term is identified, various data sources including document or email search indexes and relational or non-relational databases are searched, and data units related to the selected terms are retrieved and displayed in a user interface with a concurrent view of both the text content containing the term and the related information. Other methods for utilizing various visual effects to concurrently display relevant information associated with various user interface objects are also disclosed.

Abstract: A computer-assisted method for assisting a user to search for documents or other file objects includes receiving a query comprising a queried term from a user, wherein the queried term comprises a sequence of characters entered by the user, obtaining a first context term list comprising a first context term semantically related to the queried term, obtaining and displaying a first search result, dynamically displaying the first context term list in response to each character being entered in the query, allowing the user to select the first context term in the first context term list, obtaining a second context term list comprising a second context term semantically related to the queried term and the first context term, and displaying a second search result and the second context term list in a user interface in response to the selection of the first context term in the first context term list.

Abstract: A computer-assisted method for discovering topics in a document includes the steps of calculating a term prominence value for a token, selecting tokens based on the term prominence values of the respective tokens, and outputting topic terms comprising the selected tokens to represent the topics in the document.

Abstract: A method for quantitatively assessing associations between different terms in a natural language includes obtaining a first group of sentences, at least some of which comprise an object name, parsing each of the first group of sentences to identify a subject and a predicate, assigning a sentence type to each of the first group of sentences according to the location of the object name in the sentence, tokenizing each of the first group of sentences to produce a plurality of tokens that includes a jth token, and adding a weighting coefficient to a parameter token_j_count for each sentence that includes the jth token, wherein the weighting coefficient is dependent on the sentence type. A cumulative value of the parameter token_j_count obtained from the first group of sentences is divided by the total number of sentences in the first group to produce an internal association strength for the jth token.

Abstract: A system is described for assessing information in natural language contents. A user interface receives an object name as a query term and a value for a customized ranking parameter from a user. A computer storage device stores an object-specific data set related to the object name, wherein the object-specific data set includes a plurality of property names and association-strength values. A computer processing system can count a first frequency of a first property name and count a second frequency of a second property name in a document containing text in a natural language, calculate a relevance score as a function of the first frequency and the second frequency, and rank the plurality of documents using their respective relevance scores, and return one or more documents to the user based on the ranking of the plurality of documents. The function is in part defined by the customized ranking parameter.