Progressive collocation for real-time discourse

In an approach to training a corpus link model and generating collocated terms for intra-channel and inter-channel activity, one or more computer processors train a corpus link model based on an analysis of a linguistic corpus and an analysis of one or more author metrics. The one or more computer processors generate one or more collocated terms based on one or more calculations by the trained corpus link model. The one or more computer processors generate a co-occurrence rating for each of the one or more generated collocated terms. The one or more computer processors display the one or more generated collocated terms according to the generated co-occurrence rating of each collocated term.

BACKGROUND

The present invention relates generally to the field of communication, and more particularly to real-time communication applications.

The increasing growth and popularity of modern-day collaborative communication application and suites, within organizations, groups, and companies, has dramatically influenced the need for accurate, reliable, and search term generation and applicability. Modern collaborative communication suites and applications have a plurality of contained chat rooms and channels, each, typically, dedicated to a topic, motif, discipline, subject, or purpose. While solutions exist for determining related terms such as probabilistic searching, adaptive learning searches, and corpus linguistics analysis methods, these solutions are wholly ineffective due to the solution's reliance on generalized probabilistic models utilizing incomplete sentences, phrasing and searches with vague terms leading to ambiguity and irrelevant search results. Furthermore, said solutions do not create specific allowances or assumptions for specific user linguistic tendencies and propensities contained in a plurality of channels or sub-channels within a collaborative communication application.

Collaborative software or groupware are application software designed to help a plurality of individuals, involved in a common task, to achieve one or more cooperative goals. In terms of the level of interaction, collaborative software may be divided into: real-time collaborative editing (RTCE) platforms that allow multiple users to engage in live, simultaneous, and reversible editing of a single file, and version control (e.g., revision control and source control) platforms, which allow separate users to make parallel edits to a file, while preserving every saved edit as multiple files. Collaborative software is a broad concept that overlaps considerably with computer-supported cooperative work (CSCW). Collaborative work systems become a useful analytical tool to understand the behavioral and organizational variables that are associated to the broader concept of CSCW.

A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a sequence allowing the network to exhibit temporal dynamic behavior for a time sequence. Unlike feedforward neural networks, RNNs can use internal states (memory) to process sequences of inputs allowing the RNN to be applicable to tasks such as unsegmented connected handwriting recognition or speech recognition. Long short-term memory (LSTM) units are alternative layer units of a recurrent neural network (RNN). An RNN composed of LSTM units is referred as a LSTM network. A common LSTM unit is composed of a cell, input gate, output gate, and forget gate. The cell remembers values over arbitrary time intervals and the gates regulate the flow of information into and out of the cell. Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks. GRU performance on polyphonic music modeling and speech signal modeling was found to be similar to LSTM. However, GRUs exhibit better performance on smaller datasets.

Collaborative services are ubiquitous in modern organizations, allowing a plurality of individuals and groups the ability to have multiple, concurrent discussions in a real-time. With respect to collaborative services and applications, real-time chat is the predominant method of communication for most organizations. Collaborative chat applications are used by members of a team, organization, and/or company to identify, diagnose, and remediate problems and issues. For example, software development teams or support staff utilize collaboration chat applications to identify sections of faulty code and discuss potential solutions (e.g., alternative code structures, etc.). In this example, different software or support groups may have distinct channels or sub-channels dedicated to a specific issue or problem.

Generic search term generation and production, when applied to dedicated channels or chat rooms within real-time collaboration applications, allow for misapplied, irrelevant, and vague collocated terms that are ineffective, impede others from finding pertinent information relevant to a channel or chat room, and prevent others from communicating and engaging effectively in meaningful discourse. Traditionally, term generation systems are ineffective in enabling a user the ability to be provided an optimal set of linked terms within the construct of a collaborative chat search. Furthermore, ineffective, traditional systems attempt to disambiguate the meaning of analyzed communications using the placements of words, position, and grammar into fixed hierarchical structures and filter search results utilizing the fixed hierarchical structure. Commonly, hierarchical structures are known to perform poorly when generalizing outside an initial communication scope (e.g., different group of people, topic, or channel).

SUMMARY

A first aspect of the present invention discloses a computer-implemented method including one or more computer processors training a corpus link model based on an analysis of a linguistic corpus and an analysis of one or more author metrics. The one or more computer processors generate one or more collocated terms based on one or more calculations by the trained corpus link model. The one or more computer processors generate a co-occurrence rating for each of the one or more generated collocated terms. The one or more computer processors display the one or more generated collocated terms according to the generated co-occurrence rating of each collocated term. Embodiments of the present invention utilize trained models based on specific corpuses to generate generalized collocated terms, allowing the presentation of an optimal set of linked terms within the construct of a collaborative chat search.

A second aspect of the present invention discloses a computer-implemented method including one or more computer processors training a corpus link model based on an analysis of a linguistic corpus and an analysis of one or more author metrics. The one or more computer processors generate one or more collocated terms based on one or more calculations by the trained corpus link model. The one or more computer processors generate a co-occurrence rating for each of the one or more generated collocated terms. The one or more computer processors tag one or more webpages with the one or more generated collocated terms according to the co-occurrence rating of each collocated term. Embodiments of the present invention utilize trained models based on specific corpuses to generate generalized collocated terms, allowing the tagging of an optimal set of linked terms within the construct of a content management system.

A third aspect of the present invention discloses a computer program product including one or more computer readable storage device and program instructions stored on the one or more computer readable storage device. The stored program instructions include program instructions to train a corpus link model based on an analysis of a linguistic corpus and an analysis of one or more author metrics. The stored program instructions include program instructions to generate one or more collocated terms based on one or more calculations by the trained corpus link model. The stored program instructions include program instructions to generate a co-occurrence rating for each of the one or more generated collocated terms. The stored program instructions include program instructions to display the one or more generated collocated terms according to the generated co-occurrence rating of each collocated term. Embodiments of the present invention utilize trained models based on specific corpuses to generate generalized collocated terms, allowing the presentation of an optimal set of linked terms within the construct of a collaborative chat search.

A fourth aspect of the present invention discloses a computer system including one or more computer processors and one or more computer readable storage device, where the program instructions are stored on the one or more computer readable storage device for execution by at least one of the one or more computer processors. The stored program instructions include program instructions to train a corpus link model based on an analysis of a linguistic corpus and an analysis of one or more author metrics. The stored program instructions include program instructions to generate one or more collocated terms based on one or more calculations by the trained corpus link model. The stored program instructions include program instructions to generate a co-occurrence rating for each of the one or more generated collocated terms. The stored program instructions include program instructions to display the one or more generated collocated terms according to the generated co-occurrence rating of each collocated term. Embodiments of the present invention utilize trained models based on specific corpuses to generate generalized collocated terms, allowing the presentation of an optimal set of linked terms within the construct of a collaborative chat search.

DETAILED DESCRIPTION

Embodiments of the present invention recognize that collaborative environments can introduce confusion and readability issues. Some embodiments of the present invention recognize that providing collocated and colligated terms reduces confusion and increases readability. Embodiments of the present invention improve collaborative systems by training one or models based on specific language and discourse contained within a channel, sub-channel, chat room, group, or application and generating specific collocated and colligated terms utilizing the trained models. Embodiments of the present invention improve collaborative systems by improving training models and the ability of the models to effectively recognize and generate collocated and colligated terms. Embodiments of the present invention allow for generated collocated and colligated terms to be utilized in searches, specifically search term recommendation. Embodiments of the present invention allow generated collocated and colligated terms to be utilized in content, topic, and file tagging. Embodiments of the present invention utilize the generated collocated and colligated terms to create efficient search engine optimization tags and metadata.

Embodiments of the present invention utilize generated collocated and colligated terms (e.g., used to frame search collation for chat discourse) in content management systems, aiding in understanding which terms are most likely search for. Embodiments of the present invention utilize generated collocated and colligated to tag content or formulate titles that would improve searchability and reduce confusion. Embodiments of the present invention model the trending of location from one location to another and utilizes said model to add weight to point in time interactions. Embodiments of the present invention generate predictive progressive collocation group models based on previous conservations of a like group on topical matters. Embodiments of the present invention profile at a group level, allowing the invention to generate terms based on linguistical patterns for like terms and subject matter. Embodiments of the present invention utilizes the progressive collocation group models to predict pattern progression and the evolution of search terms based on group activities (e.g., software support team). Embodiments of the present invention recognize that system efficiency (e.g., reduction in system processing requirements such as memory and central processing unit utilization) can be improved by eliminating the generation of unlikely or improbable collocated terms. No art combines corpus linguistic analysis and author frequency analysis to produce the fidelity of solution that the present invention provides for the problem space. Implementation of embodiments of the invention may take a variety of forms, and exemplary implementation details are discussed subsequently with reference to the Figures.

FIG. 1is a functional block diagram illustrating a computational environment, generally designated100, in accordance with one embodiment of the present invention. The term “computational” as used in this specification describes a computer system that includes multiple, physically, distinct devices that operate together as a single computer system.FIG. 1provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made by those skilled in the art without departing from the scope of the invention as recited by the claims.

Computational environment100includes client computing device110and server computer120, interconnected over network102. Network102can be, for example, a telecommunications network, a local area network (LAN), a wide area network (WAN), such as the Internet, or a combination of the three, and can include wired, wireless, or fiber optic connections. Network102can include one or more wired and/or wireless networks that are capable of receiving and transmitting data, voice, and/or video signals, including multimedia signals that include voice, data, and video information. In general, network102can be any combination of connections and protocols that will support communications between client computing device110, server computer120, and other computing devices (not shown) within computational environment100. In various embodiments, network102operates locally via wired, wireless, or optical connections and can be any combination of connections and protocols (e.g., personal area network (PAN), near field communication (NFC), laser, infrared, ultrasonic, etc.).

Client computing device110may be any electronic device or computing system capable of processing program instructions and receiving and sending data. In some embodiments, client computing device110may be a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), a desktop computer, a personal digital assistant (PDA), a smart phone, or any programmable electronic device capable of communicating with network102. In other embodiments, client computing device110may represent a server computing system utilizing multiple computers as a server system, such as in a cloud computing environment. In general, client computing device110is representative of any electronic device or combination of electronic devices capable of executing machine readable program instructions as described in greater detail with regard toFIG. 4, in accordance with embodiments of the present invention. Client computing device110contains user interface112and application114.

User interface112is a program that provides an interface between a user of client computing device110and a plurality of applications that reside on client computing device110(e.g., application114) and/or may be accessed over network102. A user interface, such as user interface112, refers to the information (e.g., graphic, text, sound) that a program presents to a user and the control sequences the user employs to control the program. A variety of types of user interfaces exist. In one embodiment, user interface112is a graphical user interface. A graphical user interface (GUI) is a type of interface that allows users to interact with peripheral devices (i.e., external computer hardware that provides input and output for a computing device, such as a keyboard and mouse) through graphical icons and visual indicators as opposed to text-based interfaces, typed command labels, or text navigation. The actions in GUIs are often performed through direct manipulation of the graphical elements. In an embodiment, user interface112sends and receives information through application114to user program150.

Application114is a set of one of more programs designed to carry out the operations for a specific application to assist a user to perform an activity (e.g., word processing programs, spread sheet programs, media players, web browsers). In the depicted embodiment, application114is a set of one or more programs designed to facilitate in group communications, collaborative problem solving, and coordination activities. In this embodiment, application114contains a plurality of channels, chat rooms, or partitioned communication groups wherein each channel is dedicated to a specific topic or discussion category. In the depicted embodiment, application114resides on client computing device110. In another embodiment, application114resides on server computer120or on another device (not shown) connected over network102.

Server computer120can be a standalone computing device, a management server, a web server, a mobile computing device, or any other electronic device or computing system capable of receiving, sending, and processing data. In other embodiments, server computer120can represent a server computing system utilizing multiple computers as a server system, such as in a cloud computing environment. In another embodiment, server computer120can be a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), a desktop computer, a personal digital assistant (PDA), a smart phone, or any programmable electronic device capable of communicating with client computing device110and other computing devices (not shown) within computational environment100via network102. In another embodiment, server computer120represents a computing system utilizing clustered computers and components (e.g., database server computers, application server computers, etc.) that act as a single pool of seamless resources when accessed within computational environment100. In the depicted embodiment, server computer120includes database122and program150. In other embodiments, server computer120may contain other applications, databases, programs, etc. which have not been depicted in computational environment100. Server computer120may include internal and external hardware components, as depicted and described in further detail with respect toFIG. 4.

Database122is a repository for data used by program150. In the depicted embodiment, database122resides on server computer120. In another embodiment, database122may reside on client computing device110or elsewhere within computational environment100provided program150has access to database122. A database is an organized collection of data. Database122can be implemented with any type of storage device capable of storing data and configuration files that can be accessed and utilized by program150, such as a database server, a hard disk drive, or a flash memory. In an embodiment, database122stores data used by program150, such as linguistic corpus124, described in detail below. In the depicted embodiment, database122contains linguistic corpus124and corpus link model (CLM)126.

Linguistic corpus124is a repository for a plurality of text-based corpora (i.e., natural language representation of auditory speech, speech utterances, text sequences, computer encoded sequences, etc.). In an embodiment, linguistic corpus124contains one or more historical queries, communications, statements, discussions, comments, utterances with one or more authors, individuals, and/or groups. In another embodiment, linguistic corpus124contains historical collocated and colligated terms, associated topics, and associated author metrics (e.g., author-topic mappings, channel frequency, temporal bounds and considerations (e.g., earliest/latest posts, average time of day when posting, etc.), author post frequency, average post length, average post reading level, frequently utilized terms/phrases, etc.). In an embodiment, said historical communications are categorized, organized, and/or structured in relation to the specific author, individual, channel, sub-channel, chat room, or group. For example, all the historical communications contained within a specific channel are structured and partitioned together. In various embodiments, the information contained in linguistic corpus124is temporally structured. For example, said information may be constrained or limited with regards to a time period (e.g., discussions in the last month). In another embodiment, said information is limited to a specific group, author, or topic (e.g., discussion regarding a specific query, topic, genre, problem, issue, solution, etc.).

In an embodiment, linguistic corpus124contains unprocessed communications, discussions, and utterances. In another embodiment, linguistic corpus124may include a series of vectors corresponding to a plurality of determined features including, but not limited to, author, group, topic, identified problem, associated solution, related topic/query sets, technological field (e.g., computer science, mechanical, biology, chemistry, etc.), programmatic conventions (e.g., programming language, programming language category (e.g., strong type, object oriented, procedural, etc.), and temporal events (e.g., subsets constrained by pre-determined intervals (e.g., all communications related to a specific topic or channel made in the last year), software lifecycles (e.g., sunsetting of software, etc.), etc.).

In various embodiments, linguistic corpus124includes collections of queries (e.g., associated topics), collocated and colligated term pairs (e.g., additional search and query terms). Each pair includes a query and a corresponding collocated and colligated term or sequence. A query term or sequence may be a textual term or sequence, in a natural language or a computer-generated representation. For example, the query (e.g., topic) “allergy” is paired with the collocated and colligated terms “acute” and “concerns” and forming the complete query sequence/phrase “acute allergy concerns.” In another embodiment, channel specific statistics such as historical topics, authors, related collocated terms, related colligated terms, and related search terms are included as features. In another embodiment, author metrics are attached to topic terms as features. In a further embodiment, author metrics specific to a channel or communication medium (e.g., chat applications, channels, sub-channels, chatrooms, or environments) are attached to topic terms as features. In various embodiments, a query/collocated term set contains associated communications, discussions, topics, comments, and/or statements either in a structured or unstructured form. In another embodiment, a collocated and colligated term is transformed into a label and attached to one or more associated queries or topic terms. In yet another embodiment, a pre-determined, historical, and/or generated collocated term efficacy rating is attached as a feature, label, or as an expected output to one or more topic sets. In an embodiment, linguistic corpus124may be represented as a graph database, where communications, discourse, and/or discussions are stored in relation to the authors, queries, or topics forming sequences of similar query/topic/communication and channel/author combinations (e.g., collaborative author graph).

Corpus link model (CLM)126, hereinafter CLM126, contains one or more models, containers, documents, sub-documents, matrices, vectors, and associated data, modeling one or more feature sets, such as results from linguistic analysis. In an embodiment, linguistic analysis determines query characterizations and representations, collocated term consolidation, author metrics, and intra-arrival time of post frequency. In an embodiment, CLM126contains one or more generative (e.g., latent Dirichlet allocation (LDA), etc.) or discriminative (e.g., support vector machine (SVM), etc.) statistical models utilized to calculate the conditional probability of an observable X, given a target y, symbolically, P(X|Y=y). In various embodiments, CLM126may train and utilize one or more discriminative models to calculate the conditional probability of the target Y, given an observation x, symbolically, P(Y|X=x).

CLM126assesses a query (e.g., topic) or query sequence by considering different features, available as structured or unstructured data, and applying relative numerical weights. Program150feeds a vectorized, encoded, or computer represented training set of feature data to CLM126. In various embodiments, the data (topic or topic term) is labeled with collocated and/or colligated terms enabling CLM126to “learn” what features (e.g., topic, topic terms, author metrics, group metrics, etc.) are correlated to a specific collocated term or phrase. In various embodiments, the features include metadata (e.g., targeted channels (e.g., channel members, topics, purposes, etc.), similar topics, associated authors, and environmental considerations (e.g., platform, version, device specific variables, etc.) in addition to the topic. In a further embodiment, the training set includes examples of a plurality of features, such as tokenized topic/search term segments, comments, statements, discussions, variables, objects, data structures, etc. CLM126“learns” from the training set of data to distinguish between probable and improbable (e.g., applied against a predetermined efficacy threshold) collocated terms when applied to one or more specific topics or terms (e.g., limited by author, application, or channel/chat room, etc.). Once trained, CLM126can generate one or more collocated colligated terms, associated probabilities, and co-occurrence ratings based on the data aggregated and fed by program150. In an embodiment, CLM126utilizes the following corpus linguistic analysis [termA, collation_left, loglikeihood, collation_right, loglikelihood] and author analysis [userA, distance, post_frequency] as inputs when assessing a query. In this embodiment, CLM126assesses the query utilizing the following structure [stringA, stringL, float, stringR, float]+[userA, distance, post_frequency], representing a model trained utilizing corpus linguistic analysis and author analysis.

In an embodiment, CLM126utilizes deep learning techniques to pair queries and associated topics to probable collocated terms. In various embodiments, CLM126utilizes transferrable neural network algorithms and models (e.g., long short-term memory (LSTM), deep stacking network (DSN), deep belief network (DBN), convolutional neural networks (CNN), compound hierarchical deep models, etc.) that can be trained with supervised or unsupervised methods. In various embodiments, CLM126is a simple neural network. In a further embodiment, CLM126is a single layer feed neural network containing an affine transformation followed by element-wise nonlinearity, y=f(Wx+b), where f is an element-wise non-linearity, such as sigmoid or hyperbolic tangent, and b is a bias vector. Said simple neural network can be extended with multiple hidden layers, where each layer transforms the output of the previous hidden layer to generate output for the next layer, a multi-layered perceptron (MLP).

In the depicted embodiment, CLM126contains one or more recurrent neural networks (RNNs). In this embodiment, the RNNs process sequential data (x1, x2. . . , xt) to calculate subsequent hidden states while the parameters of the models remain the same. RNNs can be “unfolded”, taking the previous hidden state and input at that time step to generate the input for the next series of time steps, allowing information to flow through the network, so that the last cell includes information derived from all the previous cells. In an embodiment, CLM126utilizes gated recurrent units (GRU). GRUB simplify the training process while reducing the amount of necessary computational resources. In another embodiment, CLM126utilizes LSTM. LSTM entails a more robust process, requiring more computational resources but LSTM is more effective when training from large datasets. In various embodiments, the hidden layers of CLM126may be unidirectional or bidirectional. In another embodiment, CLM126maintains one or more stackable RNNs for each channel or chat room contained within one or more applications or communication mediums. In this embodiment, every channel within an application will have one or more dedicated RNNs allowing optimal domain linguistic term matching for said channel. The training and utilization of CLM126is depicted and described in further detail with respect toFIG. 2.

In an embodiment, program150utilizes the models described above, in conjunction with user feedback, back testing, and/or probabilistic modeling to generate a level of co-occurrence (e.g., collocation/colligation rating (e.g., measure of association, co-occurrence confidence level), etc.) of generated terms. In another embodiment, CLM126determines the type of relationship for each generated collocation/colligation term and associated query (e.g., contained topic), such as a syntactic relation, lexical relation, or no linguistically defined relation.

Program150is a program for training a corpus link model and generating collocated search terms for intra-channel and inter-channel activity. In the depicted embodiment, program150is a standalone software program. In various embodiments, program150may implement the following steps. Program150trains a corpus link model based on an analysis of a linguistic corpus and an analysis of one or more author metrics. Program150generates one or more collocated terms based on one or more calculations by the trained corpus link model. Program150generates a co-occurrence rating for each of the one or more generated collocated terms. Program150displays the one or more generated collocated terms according to the generated co-occurrence rating of each collocated term. In another embodiment, the functionality of program150, or any combination programs thereof, may be integrated into a single software program. In some embodiments, program150may be located on separate computing devices (not depicted) but can still communicate over network102. In various embodiments, client versions of program150resides on client computing device110and/or any other computing device (not depicted) within computational environment100. Program150is depicted and described in further detail with respect toFIG. 2.

The present invention may contain various accessible data sources, such as database122, that may include personal storage devices, data, content, or information the user wishes not to be processed. Processing refers to any, automated or unautomated, operation or set of operations such as collection, recording, organization, structuring, storage, adaptation, alteration, retrieval, consultation, use, disclosure by transmission, dissemination, or otherwise making available, combination, restriction, erasure, or destruction performed on personal data. Program150provides informed consent, with notice of the collection of personal data, allowing the user to opt in or opt out of processing personal data. Consent can take several forms. Opt-in consent can impose on the user to take an affirmative action before the personal data is processed. Alternatively, opt-out consent can impose on the user to take an affirmative action to prevent the processing of personal data before the data is processed. Program150enables the authorized and secure processing of user information, such as tracking information, as well as personal data, such as personally identifying information or sensitive personal information. Program150provides information regarding the personal data and the nature (e.g., type, scope, purpose, duration, etc.) of the processing. Program150provides the user with copies of stored personal data. Program150allows the correction or completion of incorrect or incomplete personal data. Program150allows the immediate deletion of personal data.

FIG. 2is a flowchart depicting operational steps of program150for training a corpus link model and generating collocated terms for intra-channel and inter-channel activity, in accordance with an embodiment of the present invention.

Program150retrieves historical communications (step202). In an embodiment, program150retrieves all historical communications including, but limited to, queries, messages, conversations, discussions, utterances, and/or statements associated with a specified channel (e.g., chat room, sub-channel, moderated group, etc.), application (e.g., application114), author (e.g., user), sets of authors, topics, and associated search terms (e.g., collocations and colligations). In another embodiment, program150can process the retrieved historical communications (e.g., queries and messages) into multiple sets (e.g., by author, channel, application, topic, etc.). In yet another embodiment, program150partitions historical communications into discrete sets containing differently processed versions of the same discussion. In various embodiments, program150defines the bounds of a by utilizing predetermined response intervals. For example, if the user does not respond to a message, topic, or contribute to a discussion for more than a week, then program150determines that any communications sent after the time threshold is a new, distinct conversation, implying that the topic of the discussion may have shifted. In this embodiment, program150partitions the historical communications into one or more sets defined by temporal constraints as described above. In another embodiment, the retrieved historical communications contain information or metadata regarding user behavior such as frequently utilized search terms and associated topics.

Program150then utilizes natural language processing (NLP) techniques and corpus linguistic analysis techniques (e.g., syntactic analysis, etc.) to identify parts of speech and syntactic relations between various portions of a communication (i.e., query). Program150utilizes corpus linguistic analysis techniques, such as part-of-speech tagging, statistical evaluations, optimization of rule-bases, and knowledge discovery methods, to parse, identify, and evaluate portions of a communication. In an embodiment, program150utilizes part-of-speech tagging to identify the particular part of speech of one or more words in a communication based on its relationship with adjacent and related words. For example, program150utilizes the aforementioned techniques to identity the nouns, adjectives, adverbs, and verbs in the example sentence: “Henry, I believe this link will solve your issue”. In this example, program150identifies “Henry”, “link”, and “issue” as nouns, “solve” and “believe” as verbs. In another embodiment, program150utilizes term frequency-inverse document frequency (tf-idf) techniques to calculate how important a term is to the communication, sentence, conversation, document, or historical chat corpus. In another embodiment, program150utilizes tf-idf to calculate a series of numerical weights for the words extracted from historical communications. In a further embodiment, program150utilizes said calculations to identify and weigh frequently used terms. For example, program150increases the weight of a word proportionally to the frequency the word appears in the conversation offset by the frequency of documents (e.g., communications, discussions, etc.), in linguistic corpus124, that contain the word. In an embodiment, program150utilizes the weights calculated from tf-idf to initialize one or more instances of CLM126.

Program150may utilize one or more models (e.g., instances of CLM126or a plurality of models contained in CLM126), such as biterm topic modeling and LDA, to identify topics and themes within conversations, messages, discussions, queries, etc. In an embodiment, program150utilizes biterm topic modeling to model the generation of co-occurrence patterns (e.g., biterms) in a document. Program150utilizes biterm topic modeling to model the word co-occurrence (e.g., collocation and colligations) patterns thus enhancing the identification of topics. In various embodiments, program150utilizes aggregated patterns in a corpus to identify topics based on co-occurrence patterns (e.g., optimal search terms) at the channel-level and program150may calculate co-occurrence patterns to an application level (e.g., organizational, team, corporation, etc.). In another embodiment, program150utilizes biterm topic modeling to calculate the probability that a series of words are representative of a specified topic. In another embodiment, program150may utilize latent semantic analysis to decompose a matrix of documents and terms (e.g., multiple comments, conservations, etc.) into multiple sub-matrices, such as channel-topic matrices, author-topic, or topic-search term matrices. In an embodiment, program150utilizes probabilistic latent semantic analysis to calculate a probabilistic model that may be utilized to generate one or more probabilistic matrices such as the sub-matrices listed above.

In various embodiments, program150utilizes latent Dirichlet allocation (LDA) to identify one or more topics that may be contained within a communication or query. LDA allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words (e.g., terms) collected into documents, LDA posits that each document is a mixture of a small number of topics and the presence of each word is attributable to one of the topics of the document. Program150utilizes LDA to decompose a document (e.g., communications, queries, discussions, collection of comments, etc.) as a mixture of various topics. For example, an LDA model might have topics that can be classified as ALLERGY_related and MEDICAL_related. The LDA model contains the probabilities of topic associations of various words, such as sneezing, pollen, and antihistamine, which can be classified and interpreted by as ALLERGY_related. The MEDICAL_related topic, likewise, has probabilities of being associated with the terms: hospital, platelets, and bone. Words without special relevance, such as “the”, will have a split probability between classes or, dependent on a similarity threshold, be considered a novel topic.

In an embodiment, topics are identified based on automatic detection of the likelihood of term co-occurrence. A lexical term may occur in several topics with a different probability, however, with a different typical set of neighboring words in each topic. In an embodiment, program150associates the topics and linguistic tendencies of the historical discussions identified above with authors creating author-topic mappings. Program150utilizes the aforementioned NLP techniques to create and monitor a plurality of author-based metrics (e.g., author-topic mappings, channel or chatroom frequency, temporal bounds and considerations (e.g., earliest/latest posts, average time of day when posting, etc.), author post frequency, average post length, average post reading level, frequently utilized terms/phrases, etc.) In an embodiment, the author metrics are categorized, organized, and/or structured in relation to the specific author, individual, channel, chatroom, or group.

Program150then processes each partitioned set based on one or more feature sets. For example, a feature set that only contains specific topics such as system environmental parameters (e.g., platform, versions, device specific variables, etc.). In another example, the feature set contains information regarding a specific author in a specific channel. Program150then may transform each term into a corresponding stem/root equivalent, eliminating redundant punctuation, participles, grammatical tenses, etc. In yet another embodiment, program150non-deterministically divides the processed sets into training sets and into test sets. In a further embodiment, program150attaches the corresponding intended collocated term to each topic term as a label.

Program150then vectorizes the partitioned topic/collocated term sets, along with associated discussion and author data. In an embodiment, program150utilizes one-hot encoding techniques to vectorize categorical or string-based feature sets. For example, when vectorizing feature sets of individual words, program150creates a one-hot vector comprising a 1×N matrix, where N symbolizes the number of distinguishable words. In another embodiment, program150utilizes one-of-c coding to recode categorical data into a vectorized form. For example, when vectorizing an example categorical feature set consisting of [allergy, sneeze, cough], program150encodes the corresponding feature set into [[1,0,0], [0,1,0], [0,0,1]]. In another embodiment, program150utilizes featuring scaling techniques (e.g., rescaling, mean normalization, etc.) to vectorize and normalize numerical feature sets. In various, program150utilizes lda2vec (e.g., word embedding) to convert the aforementioned LDA and biterm topic results, documents, and matrices into vectorized representations.

Program150trains corpus link model (step204). Program150trains one or more models contained in CLM126. In an embodiment, program150initializes CLM126with randomly generated weights. In an alternative embodiment, program150initializes CLM126with weights calculated from the analysis described above (e.g., tf-idf, etc.). In an alternative embodiment, program150initializes CLM126with weights inherited from a historical model. In yet another embodiment, program150performs supervised training with the labeled vectorized data, as described in step202. For example, program150feeds query/collocated term pairs into CLM126, allowing program150to make inferences between the query term data and collocated term data (i.e., label). In an embodiment, program150trains CLM126with a plurality of feature vectors originating from data extracted from related queries, topics, communications, or author specific discussions or queries located in linguistic corpus124, as detailed above. In an embodiment, program150retrieves all historical messages, conversations, discussions, and queries related to a specific query, topic, environment, channel, chatroom, application, recipient, group, author, and/or user. In another embodiment, program150retrieves a subset of all historical communication, queries, messages, conversations, and discussions between members of a channel, group, and/or chat application.

In various embodiments, program150utilizes supervised training to determine the difference between a prediction and a target (i.e., the error), and back-propagates the difference through the layers such that CLM126“learns.” In an embodiment, program150utilizes stochastic gradient algorithms to implement backpropagation. In another embodiment, program150utilizes cosine similarity as a loss function. Program150may adjust the learning rate to adjust cross-entropy cost, allowing program150to increase or decrease the adaptability of related cells and layers. In an embodiment, program150determines whether a sufficient precision is obtained by utilizing test or held-out sets. If the calculated precision is insufficient, then program150continues with supervised training of CLM126. If the calculated precision is sufficient, then program150ends the training process and continues to step206.

Accordingly, in this embodiment, program150trains one or more models based on unique and distinct historical communications contained in one or more channels, groups, and collaborative applications (e.g., corpus). In some instances, program150trains the models according to individual groups or specific topics. Thus, this embodiment is used to create a plurality of models trained and designed to facilitate the generation of collocated terms specific to a specified channel or group corpus.

Program150monitors communications (step206). In various embodiments, communications include, but are not limited to, the detection, entry, and/or transmission of one or more user utterances, comments, sentences, search queries, and/or search terms. In an embodiment, program150prevents transmission of one or more search terms contained in a query (e.g., search query) until a collocation rating is calculated and/or one or more collocated terms are generated. In various embodiments, program150replaces and/or substitutes a graphical transmission icon within a user interface (e.g., user interface112). For example, chat applications (e.g., application114) have graphical icons that when activated transmit a search. In this example, program150replaces one or more icons, along with respective triggers, to intercept and retain the search before transmission. In yet another embodiment, program150may analyze outbound traffic of client computing device110to detect the transmission of a search (e.g., query). In this embodiment, program150may retain the search until the search (e.g., query, search terms, etc.) is analyzed, collocated terms are generated, and approved by the user. In yet another embodiment, program150detects a message by detecting the user entering words in an application (e.g., application114) element, such as a search bar or text entry area. In a further embodiment, program150may trigger analysis in response to every word or sentence the user inputs. For example, as a user types a search query, program150concurrently detects the query, extracts associated topic information, and generates one or more collocated terms specific to the communication medium (e.g., channel or chatroom). In an alternative embodiment, program150may trigger query analysis after the user stops or ceases inputting or typing text/characters or manipulating user interface112for a specified duration. For example, the user begins to input a query and after 5 seconds of non-input, program150determines the message is ready to be transmitted and thus analyzes the message.

Program150utilizes one or more models contained in CLM126to identify and/or match one or more queries and contained topics (e.g., categories, targeted channels, etc.) or topic terms (e.g., terms associated with or that described a topic) of a discussion set (e.g., messages or discussions contained in an application (e.g., chat, specialized channel, etc.)) with collocated terms (e.g., adjacent search terms) based on historical queries and contained topics, associated terms, phrases and data. In another embodiment, CLM126generates one or more probabilities (e.g., probability distributions) denoting the likelihood that a given query and topic terms is related to another topic, term, or phrase.

Program150generates collocated terms (step208). Responsive to program150monitoring and processing communications (e.g., search queries, find commands, tagging requests, etc.) within one or more chat applications, program150extracts, analyzes, and decomposes the information contained in said communications, as discussed in step202. Program150processes the weights and probabilities calculated from CLM126utilizing various components of the channel, chatroom, and application specification historical communications, queries, messages, comments, topic terms, common collocated terms, linguistic tendencies, and associated author metrics to generate one or more sets of collocated terms and determine the likelihood (e.g., percentage) of the respective terms. In an embodiment, program150utilizes any combination of biterm topic modeling, LDA, and trained cognitive models (e.g., RNN) to identify topics and relevant collocated term sets within queries, conversations, messages, discussions, as detailed in step202, in one or more chat applications, channels, sub-channels, chatrooms, or environments. In various embodiments, program150utilizes transfer learning and transference layers to utilize pretrained, related models to assist, speed up, or supplement any other related model. For example, program150trains a model utilizing the communications contained in a tech support channel of a larger collaborative application. In this example, program150may utilize transfer learning to generalize the tech support channel model to other related channels.

In an embodiment, program150detects, processes, vectorizes, and feeds the identified communications and any contained queries, topics and topic terms into a plurality of trained models within CLM126. In this embodiment, CLM126outputs one or more sets of collocated and colligated terms and associated probabilities based on the one or more fed topic terms. In an embodiment, every model within CLM126is pre-trained with training sets specific to a channel, multiple channels, user, group of users, and/or application. Collocation and colligation terms can be generated according to a plurality of organizational schemas such as channel, group, chat room, application, topic, or author specific schemas. In an additional embodiment, CLM126is trained and fed with the author metrics of an associated author or group of authors (e.g., members of a channel, sub-channel, etc.). In another embodiment, program150utilizes the output generated from CLM126to generate a co-occurrence score (e.g., rating) representing the degree of co-occurrence (e.g., likelihood that the generated term is collocated and/or colligated) between collocated terms and the topic of the monitored communications and historical communications contained with linguistic corpus124. In this embodiment, program150inputs a vector containing a sequence of query terms into one or more models contained in CLM126and CLM126outputs one or more co-occurrence scores. In another embodiment, program150utilizes one or more probabilities generated from one or more models, such as the results of the biterm modeling or the LDA output to adjust the weights each of the aforementioned results in proportion to the degree of co-occurrence confidence of each model.

In an embodiment, utilizing the output of CLM126, program150determines whether the probability associated with the generated collocation term is sufficient for an identified topic in a query. In various embodiments, program150utilizes a predefined co-occurrence threshold. In another embodiment, if program150determines that an output (e.g., generated collocated term) is less than the co-occurrence (e.g., probability, etc.) threshold, then program150removes the generated terms and reduces one or more weights, within CLM126, associated with the generated terms. In an example scenario, a group of developers are troubleshooting a buffer overflow bug and discussing potential solutions in a real-time chat application while program150monitors and analyzes the discussion, retrains an associated model, and generates collocation terms in response to a query or topic.

Accordingly, in this embodiment, program150utilizes the trained models, as detailed in step204, to generate one or more collocated terms based on an identified query. In some embodiments, program150calculates a co-occurrence score representing a probability that a generated term can be found in proximity to a query term. Thus, this embodiment is used to create effective collocated terms based on the corpus of associated trained models. In an instance, program150utilizes a model trained utilizing a corpus based on a distinct channel but related topic, generalizing said model to other problem sets (e.g., other collaborative applications, channels, groups, topics, etc.)

Program150presents collocated terms (step210). Based on one or more generated collocation terms, as described in step208, program150may generate, adjust, and present the generated collocation terms dependent on the capabilities of the associated application (e.g., chat application, etc.). Responsive an associated co-occurrence rating, program150generates, displays, modifies, or presents one or more generated collocation terms comments distinguishably (e.g., distinctively, etc.) from the original query. For example, program150may present the example generated collocation term “therapy”, when presented with a query containing the topic “radiation”, forming the modified query “radiation therapy”. In various embodiments, program150may display the associated co-occurrence rating, as a numerical score, rating, or probability, of a collocated term. In this embodiment, program150displays the rating in proximity to the corresponding term. In an embodiment, program150retrieves, queries, prompts, or determines user preferences or settings detailing user preferred presentation settings such as level of transparency and text color preferences. In another embodiment, program150modifies, transforms, or adjusts one or more stylistic elements including, but not limited to, font, font size, character style, font color, background color, capitalizations, general transparency, and relative transparency, of a display or one or more displayed terms

In an embodiment, if the co-occurrence rating does not meet or exceed a predetermined co-occurrence rating threshold, e.g., detailing a lower boundary, then program150may delete, remove, hide, or otherwise obscure the associated collocated term and/or related collocated terms. In an embodiment, where program150has multiple probable collocated terms (e.g., terms that have associated co-occurrence scores that meet or exceed a threshold), program150ranks the terms based on associated generated co-occurrence ratings. For example, as program150displays the ranked list of probable collocated terms, program150may decrease the font size of displayed terms as the co-occurrence rating of said terms decreases. In this embodiment, program150may display all probable collocated terms, allowing the user to select a term, rank one or more terms, and/or provide feedback to the terms.

In an embodiment, program150provides generated collocated terms to a content management system (CMS). In this embodiment, the CMS utilizes the collocated terms as metadata and metatags commonly utilized in search engine optimization (SEO). SEO assists in understanding which terms are most likely searched for and then used to tag content or formulate tags. Program150can utilize the generated collocation terms when generalized to a similar domain or related topic category such as optimizing a website by editing content, modifying HTML, and associated code to both increase relevance to specific keywords (e.g., topic terms) and allowing efficient indexing by search engines. Program150can increase the quality and quantity of web traffic utilizing SEO and generated collocation terms thus increasing the visibility of a web page to users of a search engine. In an embodiment, program150utilizes the generated collocated terms to tag one or more webpages to provide keywords and metadata for a plurality of web spiders, web crawlers, and associated search engines. In this embodiment, program150utilizes the calculate co-occurrence rating to modify, rank, or remove one or more tagged collocated terms.

Program150may create one or more predictive progressive collocation group models based on historical conservations of related or similar groups of an on-going topical matters. The predictive progressive collocation group models allow for frequently communicating groups to be profiled at a group level based on linguistic patterns for similar and related terms and subject matter. In an embodiment, program150is implemented within a software support system providing sets of generated collocated terms to a plurality of support teams. In this embodiment, program150allows for effective queries by utilizing through group corpus profiling to predict pattern progressions and evolution of topic terms and associated collocated terms (e.g., queries) based on historical software support activities. Program150can utilize CLM126to calculate and identify collocation trends over a time period. In an embodiment, program150models the collocation trends between a plurality of channels within the same chat application, group, or organization. In various embodiments, program150utilizes the described models to adjusts a plurality of weights incorporating point in time interactions and group linguistic patterns.

Accordingly, in this embodiment, program150presents the generated terms to one or more users. In an instance, program150modifies one or more stylistic elements of the presented terms based on the associated co-occurrence score. In an instance, program150utilizes the generated terms as metadata and tags commonly utilized in SEO. In another instance, program150utilizes the generated terms to create a predictive progressive collocation group, allowing program150to profile linguistic patterns of one or more groups. Thus, this embodiment provides a user with an optimal set of linked terms within the construct of a collaborative chat search or content management system.

Program150logs relevant communications and remodels the corpus link model (step212). In one embodiment, program150logs relevant conservations, comments, discussions, and associated data into linguistic corpus124. In another embodiment, program150may receive user feedback through a graphical user interface (e.g., user interface112) on client computing device110. For example, after program150analyzes a query and associated collocated terms, the user can provide feedback for the query and the rated term on a graphical user interface (e.g., user interface112) of client computing device110. In an embodiment, feedback may include a simple positive or negative response. In another embodiment, feedback may include a user confirmation of the provided collocated terms. For example, if program150generated a low co-occurrence valued collocated term or provided the term in the wrong position in relation to a topic term, the user can provide negative feedback and correctly identity the correct collocated term, relative position, and related communications. In an embodiment, program150feeds the user feedback and the corrected term into CLM126, allowing program150to adjust the model accordingly. In another embodiment, program150may use one or more techniques of NLP to log whether the response of the user is positive or negative. Program150logs relevant conservations, comments, discussions, and associated data into linguistic corpus124and retrains CLM126utilizing the adjusted corpus and associated training and testing sets.

FIG. 3Adepicts example300illustrative of a collaborative real-time communication session, an example discussion of a problem-solving session between a plurality of users. Example300includes chat application302, a real-time communication application, query304, an inputted search query, search bar308, a text input area, focused channel310, an active and viewable channel, and channels312, a list of the available channels. Example300utilizes corpus linguistic analysis and author analysis as inputs when assessing a query304.

FIG. 3Bdepicts example350illustrative of a collaborative real-time communication session, an example discussion of a problem-solving session between a plurality of users. Example300includes chat application302, a real-time communication application, query304, an inputted search query, generated collocated term306, a generated collocated search term, search bar308, a text input area, focused channel310, an active and viewable channel, and channels312, a list of the available channels.

FIG. 4depicts a block diagram of components of client computing device110and server computer120in accordance with an illustrative embodiment of the present invention. It should be appreciated thatFIG. 4provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made.

Server computer120includes communications fabric404, which provides communications between cache403, memory402, persistent storage405, communications unit407, and input/output (I/O) interface(s)406. Communications fabric404can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications, and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric404can be implemented with one or more buses or a crossbar switch.

Memory402and persistent storage405are computer readable storage media. In this embodiment, memory402includes random access memory (RAM). In general, memory402can include any suitable volatile or non-volatile computer readable storage media. Cache403is a fast memory that enhances the performance of computer processor(s)401by holding recently accessed data, and data near accessed data, from memory402.

Program150may be stored in persistent storage405and in memory402for execution by one or more of the respective computer processor(s)401via cache403. In an embodiment, persistent storage405includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage405can include a solid-state hard drive, a semiconductor storage device, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information.

The media used by persistent storage405may also be removable. For example, a removable hard drive may be used for persistent storage405. Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage405. Software and data412can be stored in persistent storage405for access and/or execution by one or more of the respective processors401via cache403.

Communications unit407, in these examples, provides for communications with other data processing systems or devices. In these examples, communications unit407includes one or more network interface cards. Communications unit407may provide communications through the use of either or both physical and wireless communications links. Program150may be downloaded to persistent storage405through communications unit407.

I/O interface(s)406allows for input and output of data with other devices that may be connected to client computing device110. For example, I/O interface(s)406may provide a connection to external device(s)408, such as a keyboard, a keypad, a touch screen, and/or some other suitable input device. External devices408can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, e.g., program150, can be stored on such portable computer readable storage media and can be loaded onto persistent storage405via I/O interface(s)406. I/O interface(s)406also connect to a display409.