Abstract: Techniques are presented for producing demographics, in an automated fashion, from a search result of computer-accessible content. While the demographics can be determined for a research product that has been produced by any technique, they are particularly useful when applied to an automated frame-based search approach. Frame-based search engines are presented for technology profiling, healthcare-related search and brand research. Determination of a demographic proceeds at two levels: member and population. At the member level, a demographic characteristic can be determined applicable with either total or partial certainty. Each value assigned by a demographic, to a population member, has a confidence level associated with it and the assignments can be represented by a Confidence Distribution. Summarization of a demographic, at the population level, depends upon whether the certainty assignments, at the member level, are total or partial. Declarant Demographics are presented.

Abstract: A frame represents a concept with a set of roles and a set of linguistic rules. If a linguistic rule is satisfied, by a unit of natural language discourse (UNLD), the frame is invoked and a frame instance produced. A frame instance specifies how the UNLD, with particular values drawn from the UNLD, fulfills the roles of the frame. A frame-based search, of target content, is accomplished in response to a frame-based user query. The search result is comprised of records, where each record is a result of a match, of the frame-based query, at a location in the target content. If the frame-based query is implicit, a match requires only that the location of the target content invokes the appropriate frame. If the frame-based query is role-specific, in addition to invoking the appropriate frame, a query search term needs to be found in the value for a role of the frame instance produced.

Abstract: Pinnacle concepts are not amenable to detection by the use of keywords. A unit of natural language discourse (UNLD) “refers” to a pinnacle concept “C” when that UNLD uses linguistic expressions in such a way that “C” is regarded as expressed, used or invoked by an ordinary reader of “L.” A reference can have a “reference level” value that is proportional to: the “strength” with which the pinnacle concept is referenced, the probability that a pinnacle concept is referenced or both strength and probability. Pinnacle concepts can be divided into Quantifiers and non-Quantifiers. A Quantifier can modify the reference level assigned to a non-Quantifier. A concept “C,” that is determined to be referenced by a UNLD “x,” after application of its Quantifiers, is said to be asserted by “x.” Concept-based classification is the identification of whether a pinnacle concept “C” is asserted by a UNLD. Concept-based classification can be used for concept-based search.

Abstract: A frame represents a concept with a set of roles and a set of linguistic rules. If a linguistic rule is satisfied, by a unit of natural language discourse (UNLD), the frame is invoked and a frame instance produced. A frame instance specifies how the UNLD, with particular values drawn from the UNLD, fulfills the roles of the frame. A frame-based search, of target content, is accomplished in response to a frame-based user query. The search result is comprised of records, where each record is a result of a match, of the frame-based query, at a location in the target content. If the frame-based query is implicit, a match requires only that the location of the target content invokes the appropriate frame. If the frame-based query is role-specific, in addition to invoking the appropriate frame, a query search term needs to be found in the value for a role of the frame instance produced.

Abstract: Techniques are presented for producing demographics, in an automated fashion, from a search result of computer-accessible content. While the demographics can be determined for a research product that has been produced by any technique, they are particularly useful when applied to an automated frame-based search approach. Frame-based search engines are presented for technology profiling, healthcare-related search and brand research. Determination of a demographic proceeds at two levels: member and population. At the member level, a demographic characteristic can be determined applicable with either total or partial certainty. Each value assigned by a demographic, to a population member, has a confidence level associated with it and the assignments can be represented by a Confidence Distribution. Summarization of a demographic, at the population level, depends upon whether the certainty assignments, at the member level, are total or partial. Declarant Demographics are presented.

Abstract: Analysis is enabled, of a corpus of statements (such as those from social media), according to each statement's expression of sentiment about some kind of object. Object-specific corpuses are identified, where each object-specific corpus contains statements that refer to a same object. For each statement of an object-specific corpus, the polarity and intensity of sentiment expressed is determined. Net polarity and intensity measures are determined for each object-specific corpus and utilized to graph the corpus in a two-dimensional space. The area of the graphical symbol, representative of an object-specific corpus, can be proportional to the number (absolute or relative) of statements of the object-specific corpus. Brands can be compared, with each brand represented by an object-specific corpus. A single brand can have shown, relative to a temporal dimension, the net polarity, net intensity, or volume of its statements. Net polarity is shown to have a strong correlation with survey-based techniques.

Abstract: A frame represents a concept with a set of roles and a set of linguistic rules. If a linguistic rule is satisfied, by a unit of natural language discourse (UNLD), the frame is invoked and a frame instance produced. A frame instance specifies how the UNLD, with particular values drawn from the UNLD, fulfills the roles of the frame. A frame-based search, of target content, is accomplished in response to a frame-based user query. The search result is comprised of records, where each record is a result of a match, of the frame-based query, at a location in the target content. If the frame-based query is implicit, a match requires only that the location of the target content invokes the appropriate frame. If the frame-based query is role-specific, in addition to invoking the appropriate frame, a query search term needs to be found in the value for a role of the frame instance produced.

Abstract: Occurrence and co-occurrence data can be presented as a graph. A Non-directional Co-occurrence Graph represents occurrences of an item x as a node and co-occurrences, of items x and y, as an edge between the nodes for x and y. The size of a node can depend on the log, or a scaled value, of its occurrences. A Directional Co-occurrence Graph is the same as a Non-directional Co-occurrence Graph, except there can be, between each pair of nodes, up to two directed edges. An edge pointing from x to y can be used to represent the following ratio: number of co-occurrences of x and y divided by the number of occurrences of x. More specifically, the thickness of the edge can be proportional to the ratio. Net sentiment can be determined for an item x and used to modify the graphical representation (e.g., the color) of a node or edge.

Abstract: Pinnacle concepts are not amenable to detection by the use of keywords. A unit of natural language discourse (UNLD) “refers” to a pinnacle concept “C” when that UNLD uses linguistic expressions in such a way that “C” is regarded as expressed, used or invoked by an ordinary reader of “L.” A reference can have a “reference level” value that is proportional to: the “strength” with which the pinnacle concept is referenced, the probability that a pinnacle concept is referenced or both strength and probability. Pinnacle concepts can be divided into Quantifiers and non-Quantifiers. A Quantifier can modify the reference level assigned to a non-Quantifier. A concept “C,” that is determined to be referenced by a UNLD “x,” after application of its Quantifiers, is said to be asserted by “x.” Concept-based classification is the identification of whether a pinnacle concept “C” is asserted by a UNLD. Concept-based classification can be used for concept-based search.

Abstract: The following graphical representations, of frame instance data, are presented: Instance Graph and Instance Plot. An Instance Graph is a kind of directed graph that represents directed relationships between items, as established by frame instances. An example frame is the Preference Frame, as applied to online opinion data. The degree or “influence” of a node can be graphically indicated. Multiple edges, between two nodes, can be represented as a compound edge. Each node can be modeled as having a field, causing it to repel all other nodes, which each edge can be modeled as producing an attractive force. The “net preference” of a node is the difference between its outdegree and indegree. From the “influence” and “net preference” values, for nodes of an Instance Graph, an Instance Plot can be produced. One axis of an instance plot is based on “influence” and another axis is based on “net preference.

Abstract: Concept-based ranking is applied to the results of a search process. Concept-based ranking relies on the input query, that produced the search results, having pinnacle concept properties specified for some of its keyword-based search terms. For each record “r” of a search result, it can be assigned a concept-based ranking score as follows. For each query term “QT,” of the input query, the following can be performed. The set of occurrences of “QT,” in the record, can be determined. Based upon the pinnacle concept of “QT,” and certain characteristics of the set of occurrences, a value can be assigned to “QT.” Characteristics, of the set of occurrences, can include the type of pinnacle concept asserted by at least one sub-unit of the record in which there is an occurrence. Once each “QT” has been assigned a value, such values can be combined to produce a score for ranking the record.

Abstract: A frame represents a concept with a set of roles and a set of linguistic rules. If a linguistic rule is satisfied, by a unit of natural language discourse (UNLD), the frame is invoked and a frame instance produced. A frame instance specifies how the UNLD, with particular values drawn from the UNLD, fulfills the roles of the frame. A frame-based search, of target content, is accomplished in response to a frame-based user query. The search result is comprised of records, where each record is a result of a match, of the frame-based query, at a location in the target content. If the frame-based query is implicit, a match requires only that the location of the target content invokes the appropriate frame. If the frame-based query is role-specific, in addition to invoking the appropriate frame, a query search term needs to be found in the value for a role of the frame instance produced.

Abstract: Techniques are presented for producing demographics, in an automated fashion, from a search result of computer-accessible content. While the demographics can be determined for a research product that has been produced by any technique, they are particularly useful when applied to an automated frame-based search approach. Frame-based search engines are presented for technology profiling, healthcare-related search and brand research. Determination of a demographic proceeds at two levels: member and population. At the member level, a demographic characteristic can be determined applicable with either total or partial certainty. Each value assigned by a demographic, to a population member, has a confidence level associated with it and the assignments can be represented by a Confidence Distribution. Summarization of a demographic, at the population level, depends upon whether the certainty assignments, at the member level, are total or partial. Declarant Demographics are presented.

Abstract: Analysis is enabled, of a corpus of statements (such as those from social media), according to each statement's expression of sentiment about some kind of object. Object-specific corpuses are identified, where each object-specific corpus contains statements that refer to a same object. For each statement of an object-specific corpus, the polarity and intensity of sentiment expressed is determined. Net polarity and intensity measures are determined for each object-specific corpus and utilized to graph the corpus in a two-dimensional space. The area of the graphical symbol, representative of an object-specific corpus, can be proportional to the number (absolute or relative) of statements of the object-specific corpus. Brands can be compared, with each brand represented by an object-specific corpus. A single brand can have shown, relative to a temporal dimension, the net polarity, net intensity, or volume of its statements. Net polarity is shown to have a strong correlation with survey-based techniques.

Abstract: A frame represents a concept with a set of roles and a set of linguistic rules. If a linguistic rule is satisfied, by a unit of natural language discourse (UNLD), the frame is invoked and a frame instance produced. A frame instance specifies how the UNLD, with particular values drawn from the UNLD, fulfills the roles of the frame. A frame-based search, of target content, can produce a search result comprised of records and corresponding frame instances. The values of such frame instances can be presented to the user as a role-value oriented search result. Multiple values of a role-value oriented search result, sufficiently similar in meaning, can be merged. Merged values can be represented, in a role-value oriented search result, by a single value. Selection of a value, of a role-value oriented search result, can cause the records, for which the value occurs in the corresponding instance, to be displayed to the user.

Abstract: Pinnacle concepts are not amenable to detection by the use of keywords. A unit of natural language discourse (UNLD) “refers” to a pinnacle concept “C” when that UNLD uses linguistic expressions in such a way that “C” is regarded as expressed, used or invoked by an ordinary reader of “L.” A reference can have a “reference level” value that is proportional to: the “strength” with which the pinnacle concept is referenced, the probability that a pinnacle concept is referenced or both strength and probability. Pinnacle concepts can be divided into Quantifiers and non-Quantifiers. A Quantifier can modify the reference level assigned to a non-Quantifier. A concept “C,” that is determined to be referenced by a UNLD “x,” after application of its Quantifiers, is said to be asserted by “x.” Concept-based classification is the identification of whether a pinnacle concept “C” is asserted by a UNLD. Concept-based classification can be used for concept-based search.

Abstract: Searching computer-accessible content can be described as the utilization of an automated process to determine occurrences of where a sought-for concept is referred to in natural language discourse. Concept-based search refers to the reliable identification, within computer-accessible content that represents natural language discourse, of instances in which a particular pinnacle concept “C” is referenced. References to pinnacle concepts are not amenable to detection by the use of keywords. For each pinnacle concept “C,” whose reference is to be determined, a set of linguistic features can be compiled that is referred to herein as a “concept feature set.” In general, it is desirable for a concept feature set to be “complete.” A definition of completeness is presented. Concept-based search can be used in conjunction with keyword-based search.