Abstract: At least some embodiments are directed to a computer-implemented method that comprises receiving original input text that includes a term, comparing a definition of the term to definitions of multiple candidate replacement terms to generate a set of candidate replacement terms, and substituting each of the candidate replacement terms in the set for the term in the original input text to produce a plurality of modified input texts. The method also comprises determining the grammatical accuracy of each of the plurality of modified input texts, comparing meanings of the modified input texts to a meaning of the original input text, and modifying the set of candidate replacement terms based on the determinations of grammatical accuracy and the comparisons of the meanings. The method still further comprises ranking the modified set of candidate replacement terms using one or more criteria, and displaying the ranking on a display.

Abstract: Disclosed aspects relate to analytics engine selection management. A set of reference data may be ingested by a first analytics engine to compile a first set of characteristic data. The set of reference data may be ingested by a second analytics engine to compile a second set of characteristic data. The first set of characteristic data may be compiled for the first analytics engine. The second set of characteristic data may be compiled for the second analytics engine. A set of distinct attributes related to the first and second analytics engines may be determined based on the first and second sets of characteristic data. An analytics engine selection operation may be executed.

Abstract: Embodiments of the present invention relate to performing entity recognition on a stream while providing ongoing training or supplementation of an entity dictionary. In one embodiment, a method of and computer program product for stream based named entity recognition is provided. A first portion of a textual input is received. A plurality of patterns is applied to the first portion to determine that a predetermined type is present in the first portion. Approval is requested of the presence of the predetermined type. An indication of approval or disapproval of the predetermined type is received. A dictionary is supplemented according to the indication. A second portion of the textual input is received. The plurality of patterns is applied to the second portion.

Abstract: One embodiment provides generating a similarity matrix corresponding to an input collection including initializing, by a processor, a working set as a collection of a multiple items. Until the similarity matrix converges: receiving a seed for similarity for at least one pair of items of the multiple items, and obtaining a similarity value for all other item pairs using a Naive Triangle Inequality process. The similarity is generated with obtained similarity values.

Abstract: A method, system and computer program product for disambiguating meaning of terms. A natural language processing system obtains a first corpora of words used in a first sense and a second corpora of the same words that are used in a second sense. Each of these corpora of words may be associated with different linguistic domains. The natural language processing system generates a first and a second set of patterns using both the first and second corpora of words, respectively. A question passage is then received by the natural language processing system. The natural language processing system examines a word of interest in the question passage. The user is then notified that the word of interest is being used in the first sense or the second sense in response to identifying the word of interest in only the first set of patterns or the second set of patterns, respectively.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement an automated lexicon expansion for an identified corpus. For a selected corpus in a set of corpora, the mechanism determines an estimated number of new terms in the selected corpus that are not in the lexicon based on a frequency count known terms in the selected corpus. Responsive to the estimated number of new terms in the selected corpus being greater than a threshold, the mechanism performs lexicon expansion using the selected corpus to form an expanded lexicon. Responsive to the estimated number of new terms in the selected corpus not being greater than the threshold, the mechanism halts lexicon expansion.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement an automated lexicon expansion for an identified corpus. For a selected corpus in a set of corpora, the mechanism determines an estimated number of new terms in the selected corpus that are not in the lexicon based on a frequency count known terms in the selected corpus. Responsive to the estimated number of new terms in the selected corpus being greater than a threshold, the mechanism performs lexicon expansion using the selected corpus to form an expanded lexicon. Responsive to the estimated number of new terms in the selected corpus not being greater than the threshold, the mechanism halts lexicon expansion.

Abstract: Influencers (individuals or groups) over a selected audience (observers or recipients of information, objects and/or events) on a given topic are measured based on influence features, which include a sentiment flipping influence feature indicative of ability of an audience member to influence other audience members to change their sentiment on the selected topic. Other influence features include the ability to influence others: to change followership; to express interest in a topic associated with a hashtag pioneered by the influencer, based on the effectiveness and phrasing of language used. The output of the influence engine can be a score representing the relative influence of audience members over the audience on the topic of interest. Influencers may be ranked according to their total influence score over the audience on the topic.

Abstract: Influencers (individuals or groups) over a selected audience (observers or recipients of information, objects and/or events) on a given topic are measured based on influence features, which include a sentiment flipping influence feature indicative of ability of an audience member to influence other audience members to change their sentiment on the selected topic. Other influence features include the ability to influence others: to change followership; to express interest in a topic associated with a hashtag pioneered by the influencer, based on the effectiveness and phrasing of language used. The output of the influence engine can be a score representing the relative influence of audience members over the audience on the topic of interest. Influencers may be ranked according to their total influence score over the audience on the topic.

Abstract: A method, system and computer program product for disambiguating meaning of terms. A natural language processing system obtains a first corpora of words used in a first sense and a second corpora of the same words that are used in a second sense. Each of these corpora of words may be associated with different linguistic domains. The natural language processing system generates a first and a second set of patterns using both the first and second corpora of words, respectively. A question passage is then received by the natural language processing system. The natural language processing system examines a word of interest in the question passage. The user is then notified that the word of interest is being used in the first sense or the second sense in response to identifying the word of interest in only the first set of patterns or the second set of patterns, respectively.

Abstract: A method, system and computer program product for disambiguating meaning of terms. A natural language processing system obtains a first corpora of words used in a first sense and a second corpora of the same words that are used in a second sense. Each of these corpora of words may be associated with different linguistic domains. The natural language processing system generates a first and a second set of patterns using both the first and second corpora of words, respectively. A question passage is then received by the natural language processing system. The natural language processing system examines a word of interest in the question passage. The user is then notified that the word of interest is being used in the first sense or the second sense in response to identifying the word of interest in only the first set of patterns or the second set of patterns, respectively.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for optimizing data analysis. A set of analytics are received and a plurality of subsets of the set of analytics is generated. An optimization goal(s) is defined. The plurality of subsets of analytics are applied to a set of data. The output of applied analytics are recorded on a data storage device by the computer. Performance values of the computer applying the plurality of subsets of analytics are recorded and displayed. An optimal subset of the plurality of subsets of analytics is calculated, using recorded performance values and outputs of the applied plurality of subsets of analytics, being based on the optimization goal. A set of applied analytics are displayed, where the set of applied analytics includes a number of subsets of analytics of the plurality of analytics that meet the criteria of the optimal subset.

Abstract: A computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to receive a document having multiple section headers, segment the document into at least first and second sections based on the section headers, segment items in the first section into fragments and identify a section type for each of the fragments, determine that the identified section type for at least one of the fragments better matches a type of the second section than it matches a type of the first section, and re-locate the at least one of the fragments to the second section.

Abstract: A method comprising receiving an item via a computer input device, comparing the item to a plurality of lexicons using a processor, and determining, using the processor and based upon the comparison, that the item is part of a fragment matching at least one of the plurality of lexicons. The method also comprises storing, using the processor, the fragment in a section of a document determined based on the at least one of the plurality of lexicons, and receiving at least one other item via the computer input device after the fragment is stored in the section.

Abstract: A method comprising receiving a document having multiple sections of different types using a processor. The method also comprises obtaining a plurality of lexicons using the processor, each of the lexicons for interpreting fragments in one or more of the section types. The method further comprises interpreting fragments in a first section of the multiple sections using the processor and one or more lexicons. The method still further comprises determining, based upon the interpretation and using the processor, that a fragment in the first section is misplaced. The method still further comprises re-locating, using the processor, the misplaced fragment to a second section of the multiple sections in the document to generate a re-organized document. The method additionally includes storing the re-organized document in a hardware storage system using the processor.

Abstract: A system, comprising an input device configured to receive a first item and a second item, and a processor communicably coupled to the input device and configured to determine that the first item is a fragment matching a lexicon, and place the fragment in a section of a document, the section selected based on the matching lexicon, wherein the processor is configured to perform the determination and the placement after it receives the first item but before it receives the second item.

Abstract: At least some embodiments are directed to a computer-implemented method that comprises receiving original input text that includes a term, comparing a definition of the term to definitions of multiple candidate replacement terms to generate a set of candidate replacement terms, and substituting each of the candidate replacement terms in the set for the term in the original input text to produce a plurality of modified input texts. The method also comprises determining the grammatical accuracy of each of the plurality of modified input texts, comparing meanings of the modified input texts to a meaning of the original input text, and modifying the set of candidate replacement terms based on the determinations of grammatical accuracy and the comparisons of the meanings. The method still further comprises ranking the modified set of candidate replacement terms using one or more criteria, and displaying the ranking on a display.

Abstract: One embodiment provides generating a similarity matrix corresponding to an input collection including initializing, by a processor, a working set as a collection of a multiple items. Until the similarity matrix converges: receiving a seed for similarity for at least one pair of items of the multiple items, and obtaining a similarity value for all other item pairs using a Naive Triangle Inequality process. The similarity is generated with obtained similarity values.

Abstract: Disclosed aspects relate to analytics engine selection management. A set of reference data may be ingested by a first analytics engine to compile a first set of characteristic data. The set of reference data may be ingested by a second analytics engine to compile a second set of characteristic data. The first set of characteristic data may be compiled for the first analytics engine. The second set of characteristic data may be compiled for the second analytics engine. A set of distinct attributes related to the first and second analytics engines may be determined based on the first and second sets of characteristic data. An analytics engine selection operation may be executed.

Abstract: Disclosed aspects relate to analytics engine selection management. A set of reference data may be ingested by a first analytics engine to compile a first set of characteristic data. The set of reference data may be ingested by a second analytics engine to compile a second set of characteristic data. The first set of characteristic data may be compiled for the first analytics engine. The second set of characteristic data may be compiled for the second analytics engine. A set of distinct attributes related to the first and second analytics engines may be determined based on the first and second sets of characteristic data. An analytics engine selection operation may be executed.