822 results about "Keyword extraction" patented technology

The invention provides a plasticizing product recommendation method and system based on user demands, and relates to the technical field of plasticizing product recommendation, and the method comprises the steps: S1, constructing a plasticizing industry knowledge graph; s2, acquiring user historical data and current user requirements, including historical search data, historical browsing data, historical interaction data and historical transaction data; s3, preprocessing the historical data of the user, analyzing the preprocessed historical data of the user, and extracting historical demand features; s4, performing keyword extraction on the current user demand by utilizing the semantic understanding model, and generating an optimized query condition in combination with the chemical and plastic industry knowledge graph; s5, searching in a plasticizing product database by utilizing the optimized query condition to obtain a first list; and S6, carrying out personalized sorting on the first list by utilizing the chemical and plastic industry knowledge graph, generating a recommendation list from the first k lists, and presenting the recommendation list to the user. According to the invention, the relevance and accuracy of recommendation can be improved, so that the user experience is improved.

The invention provides a prospecting prediction method based on a multi-agent technology. The method specifically comprises the steps that S11, keyword extraction is conducted on mineral problems input by a user; s12, establishing a vector database of the mineral knowledge text and a graph database of the mineral knowledge graph, and optimizing the extracted keywords to generate cue words; s13, constructing a large prospecting model to think the cue words, expanding the cue words, and obtaining a complete statement for professional expression of the user question again; s14, performing multi-task decomposition on the complete statement to obtain a plurality of sub-tasks, and scheduling the plurality of sub-tasks to corresponding agents based on a graph database of the mineral knowledge graph to perform step-by-step prediction; and S15, integrating prediction results of the intelligent agents and feeding back the integrated prediction results to a user. According to the method, prospecting prediction is promoted to be converted from manual driving to intelligent driving, and emerging technical support is provided for prospecting breakthrough.

The invention discloses a traditional Chinese medicine intelligent inquiry method and system based on a knowledge graph and medical case enhancement RAG, and the method comprises the following steps: collecting traditional Chinese medicine related data from a plurality of sources, and carrying out the data cleaning, formatting and standardization processing, and constructing a knowledge graph containing traditional Chinese medicine symptoms, disease causes, prescriptions, and the mutual relation of the symptoms, the disease causes, the prescriptions; expressing a knowledge structure in the graph in a triple form; searching a local sub-graph related to query based on query keyword extraction and generalization by utilizing a Leiden community detection algorithm; a mixed retrieval strategy is adopted, global search and local search are combined, recalled contents are sorted and scored, global and local retrieval results are fused, and a large language model is uniformly output. The system aims at improving the intelligent level and individuation ability of traditional Chinese medicine diagnosis, firstly, a knowledge graph covering entities such as traditional Chinese medicine theories, symptoms, pathogenesis and prescriptions and relationships of the entities is constructed, mass traditional Chinese medicine data are systematically integrated, deep understanding and semantic association of traditional Chinese medicine complex diagnosis and treatment logic are ensured, and the traditional Chinese medicine diagnosis and treatment efficiency is improved. Through deep combination of the structured knowledge base and the generated model, the diagnosis and treatment accuracy is improved, and the model fine tuning and updating cost is reduced.

The invention belongs to the field of artificial intelligence and finance, and relates to a text classification method, which comprises the following steps: preprocessing an original text data set, labeling classification labels to obtain a labeled text data set, inputting a training set into a large language model for keyword extraction to obtain a candidate keyword set, cleaning the candidate keyword set to obtain a classification keyword set, constructing an instruction template based on the classification keyword set, and inputting the training set and the instruction template into a large language model to obtain a prediction classification result; and based on a prediction classification result and a loss function between the classification labels, performing fine tuning on the large language model to obtain a fine tuning model, evaluating that the fine tuning model is qualified to obtain a text classification model, and inputting the to-be-classified text into the text classification model for classification. The invention further provides a text classification device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the tagged text data set can be stored in a block chain. According to the method, the text classification accuracy and stability can be improved.

The invention relates to the technical field of medical image report error correction, in particular to a medical image report automatic quality control error correction system and method. The method comprises the steps of importing medical image data, analyzing the medical image data by using an open source tool, performing standardization processing and local contrast enhancement on an image, performing automatic segmentation of a target area through a UNet model, and calculating density features of a key area to obtain feature vectors; meanwhile, the report text data is subjected to word segmentation, standardization processing and keyword extraction steps, the word importance is evaluated by utilizing a BERT model, a semantic vector of the report is generated through a text model, finally, the consistency of the image and the report is detected by calculating the similarity between a feature vector and the semantic vector, and an automatic error correction program is started when the image and the report are inconsistent. And error types are analyzed and corrected. According to the method, medical image processing, natural language processing and deep learning technologies are combined, so that human errors are reduced, and the diagnosis efficiency and accuracy are improved.

The invention belongs to the field of question and answer generation, and provides an interactive retrieval enhancement question and answer generation method and system based on a knowledge graph, and the method comprises the steps: carrying out the document partitioning based on an original document set, generating a global block set, carrying out the entity extraction of each text block in the global block set, and obtaining an entity set; performing relation extraction on entity subsets in each text block in the entity set to obtain a global relation set; generating a plurality of sub-knowledge maps based on the global block set, the entity set and the global relationship set, and performing entity fusion and relationship fusion on the sub-knowledge maps to obtain a knowledge map; performing keyword extraction and semantic embedding on the original problem to obtain a dense vector, performing semantic embedding based on the knowledge graph to obtain an embedded vector, and generating a candidate entity set according to the dense vector and the embedded vector; and based on the candidate entity set, utilizing a large language model calling tool to carry out extended search to generate a candidate information set, and utilizing a large language model to obtain an answer to the original question based on the candidate information set.

The invention relates to the technical field of computer vision and human-computer interaction, and discloses an AI digital human intelligent interaction method, system and equipment based on multiple modalities, and the method comprises the steps: pre-awakening a digital human when a human face is detected, and further thoroughly awakening the digital human based on recognized preset voice information or preset gesture information; voice and video information of a user in the interaction process is obtained, a keyword extraction result, a gesture recognition result and an emotional state tag are generated, a pre-constructed knowledge base is utilized to retrieve related information, a big language generation model module is combined to generate an answer text, and the answer text is input into a preset voice synthesis model to generate emotional voice output. And based on the current emotional state label of the user, driving the digital human animation to be output in an emotional manner. According to the method and the system, the digital human for understanding the emotion of the user, generating personalized answers, providing voices with rich emotions and displaying natural expressions and actions can be created, better interaction with the user can be realized, and more humanized and effective services can be provided.

The invention provides a question and answer processing method and device, electronic equipment and a storage medium, and relates to the technical field of data processing.The method comprises the steps that vectorization query and field keyword extraction are conducted on question content, and question query vectors and question field keywords are obtained; performing retrieval recall on the basis of the question query vector and the question field keyword to obtain a recalled document; multi-feature fusion rearrangement is carried out according to the recall documents and the questions, rearranged recall documents are obtained, and the rearranged recall documents are used for the questions to generate answers corresponding to the questions.

The invention discloses a science and technology public text intelligent classification and service method and device based on deep learning. The method comprises the steps that multi-source science and technology public text data are acquired and preprocessed; extracting keywords by adopting a keyword extraction algorithm, and splicing the keywords with the public text title to form enhanced text features; constructing a multi-dimensional public text classification system and performing data annotation; performing feature extraction and fine adjustment by adopting a BERT pre-training model to obtain a classification model; automatically classifying the newly-added public texts and visually presenting the newly-added public texts; and generating a personalized recommendation result based on the user portrait and the public text feature index. The invention further relates to a technical scheme of multi-objective quality diversity optimization, heterogeneous resource allocation and fusion of the LPLC2 neural network and the BERT. The technical problems that a traditional method is limited in complex semantic understanding ability, single in classification dimension and lack of an integrated solution are solved, and the accuracy of science and technology public text classification and the intelligent level of service are improved.

The invention discloses an information co-processing platform based on a knowledge graph and a potential relationship prediction method. The information co-processing platform comprises a data acquisition processing layer, a knowledge extraction intelligent analysis layer and a knowledge application decision-making layer, the data acquisition processing layer comprises a data acquisition module, a data format conversion module and a knowledge base management module; the knowledge extraction intelligent analysis layer comprises a keyword extraction module, a keyword layering module, a semantic fusion module and a knowledge ontology dynamic evolution module; and the knowledge application decision-making layer comprises an evolution state judgment module, a military strategy graph generation module, an evaluation result acquisition module and a military strategy guidance information updating module. According to the method, a large amount of dispersed and isolated intelligence is integrated from actual military field business data by taking a real-time military strategy as a guide, association of military intelligence information is realized from a semantic level, powerful support is provided for comprehensive analysis, research, judgment and display of the intelligence information, analysis efficiency is improved, and effective cooperation of military strategy and intelligence data processing is realized.

The invention discloses an academic paper retrieval method and device based on large language model driving, and a medium, and the method comprises the steps: obtaining a preliminary academic paper retrieval result through retrieval of a plurality of academic paper databases according to an input retrieval request; combining the preliminary academic paper retrieval results, and performing weighted sorting processing according to correlation, popularity and time; inputting the sorted preliminary academic paper retrieval results into a pre-trained first large language model to obtain article abstracts of academic papers and comparative analysis results of similar academic papers; wherein the training process of the first large language model comprises the steps of constructing diversified task samples including abstract generation, question and answer, keyword extraction, logical reasoning, information extraction and literature review generation, and constructing a total loss function so as to train the first large language model; the total loss function is the weighted sum of abstract generation, question and answer, keyword extraction, logical reasoning, information extraction and literature review loss functions.

The invention relates to the technical field of video detection, in particular to a monitoring video detection and identification method and system based on deep learning, and the method comprises the steps: obtaining a target monitoring video of a monitoring region based on monitoring camera equipment, obtaining a video frame sequence of the target monitoring video, and carrying out the target screening of the video frame sequence, so as to obtain a video frame set; and performing target detection on each video frame in the video frame set based on a trained target detection model to obtain a target detection result set of each video frame in the video frame set. According to the method, through combination of keyword extraction and a large language model, complex monitoring data can be converted into structured event description, the textualized information not only facilitates management personnel to rapidly know the field condition, but also can provide reference for subsequent event processing, and the event description generated in real time is combined, so that the management personnel can conveniently and rapidly know the field condition. And a manager can make a coping strategy more efficiently.

The invention relates to the technical field of human resource management and information, and discloses an employment post recommendation method based on a human resource database, and the method comprises the following steps: S1, data collection; s2, data processing; s3, keyword extraction; s4, performing similarity calculation; s5, post filtering and sorting; s6, post recommendation; and S7, effect evaluation: through automatic data processing and similarity calculation, resumes most matched with post demands can be quickly screened out, the workload of recruiters is reduced, accurate matching degree evaluation is beneficial to realizing optimal configuration of talents and posts, employee loss and recruitment cost waste caused by mismatching are reduced, and the efficiency of recruitment is improved. The recruitment will and the matching degree of the job seekers can be improved through accurate recommendation, so that the recruitment success rate and efficiency are improved, more high-quality job seekers are attracted through efficient post recommendation and accurate matching, and the talent reserve and competitiveness of enterprises are enhanced.

The invention relates to the technical field of image-text recognition, in particular to a big data image-text recognition system and method based on artificial intelligence, and the system comprises an element identification module, a text keyword extraction module, a mapping network construction module, an image-text perception adjustment module, a decision diagram construction module and a format adaptability adjustment module. In the invention, through deep pixel analysis and visual feature matching, accurate analysis of the relationship between the object in the image and the visual label is realized, the accuracy of image-text data identification is improved, the specific association between the image content and the related text data is accurately understood, and the constructed image network is subjected to semantic association calculation, so that the accuracy of image-text data identification is improved. According to the method, the semantic model can dynamically adapt to new data input, the decision path of the image-text content is optimized, the image-text recognition efficiency is effectively improved, the classification decision is more accurate, the image-text recognition quality and speed in the big data environment are optimized, and the processing performance and the application flexibility are remarkably improved.

The invention provides an enterprise order purchase management method and system, and relates to the technical field of purchase management. Performance state feedback information corresponding to a purchase order is acquired; performing multi-dimensional alignment analysis on the performance state feedback information, generating performance efficiency boundaries of different purchase material types, and constructing purchase constraint rules of order purchase in each execution stage according to the performance efficiency boundaries and the user sign-in data; performing anomaly detection on a keyword extraction strategy during purchase demand identification to obtain a matching deviation situation of the purchase demand in supply chain matching, performing hierarchical verification on resources of suppliers based on the matching deviation situation and delivery sign-in data, and generating supply recommendation rules of hierarchical suppliers; and generating a target order corresponding to the purchase demand in the supply chain according to the purchase constraint rule and the purchase recommendation rule. According to the application, the performance state can be dynamically monitored and suppliers can be recommended in a graded manner under the supply chain collaboration platform, so that the precision of supply chain matching in the purchasing process is improved.

The invention relates to the technical field of data processing, and provides a question and answer method and system based on paragraph splitting and a knowledge engine platform, and the question and answer method comprises the steps: carrying out directory and title recognition on an uploaded document, and generating a paragraph frame structure with a hierarchical relationship of the document; splitting the text content of the document according to the paragraph frame structure to obtain a document paragraph structure dictionary containing the corresponding text content; according to the document paragraph structure dictionary, text blocks are generated, and a knowledge base is constructed; performing text block vectorization on the text block by adopting a text vectorization model to generate a sparse vector and a dense vector, and storing the sparse vector and the dense vector in a knowledge base; performing keyword extraction and text vectorization processing on the obtained question to obtain a keyword list, a sparse vector and a dense vector of the question; retrieving the knowledge base, and selecting a plurality of text blocks most similar to the problem; and based on the to-be-queried question, a preset cue word and a plurality of text blocks most similar to the question, obtaining a question answer by adopting the large model.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems. A system and method for providing conversation intelligence services may include pre-processing, transcribing, and post-processing. A conversation recording may be pre-processed generating a conversation record (e.g., conversation object). The pre-processed conversation recording may be transcribed into a transcript. The transcript may be post-processed which may include keyword extraction, topic extraction, feature extraction, event generation, trigger action, and / or search indexing. Conversation information may be presented based on the pre-processing, the transcribing, and the post-processing.

The invention relates to the technical field of red tide anomaly detection, in particular to a red tide anomaly detection method and system based on an improved multi-mode Transform. The method comprises the following steps: acquiring a remote sensing image and text data; respectively carrying out data preprocessing according to the obtained remote sensing image and text data; performing visual positioning and text selection based on the preprocessed data; performing cross-modal feature learning on the basis of a hierarchical Transform of a multi-modal capsule mechanism; guiding an attention mechanism based on a semantic path to carry out image-semantic feature alignment optimization; and carrying out multi-modal knowledge distillation on the optimized features. According to the method, an image and text preprocessing module, a visual positioning module, a keyword extraction module and other modules are combined, multi-angle accurate perception of a complex red tide scene is achieved, and the bottleneck that a red tide area is difficult to accurately recognize under the condition that data are single and information dimensions are limited in a traditional method is broken through.

The invention relates to the technical field of artificial intelligence, and particularly provides a knowledge graph-based interview simulation method, system and device and a storage medium, and the method comprises the steps: obtaining a resume document, and extracting key information from the resume document through a keyword extraction technology; retrieving a local graph from a pre-constructed knowledge graph based on the key information; generating a problem based on entities and relationships in the local atlas; receiving answer data for the question, and extracting entities and relationships from the answer data; and matching the extracted entities and relationships with the entities and relationships corresponding to the questions, and generating evaluation data based on a matching result. According to the method, the personalized resume of the interviewer can be deeply analyzed, the specific demand of a target post can be accurately met, and a series of interview problems which are in accordance with reality and are rich in pertinence can be intelligently generated. And the answer quality of the interviewer can be evaluated instantly and objectively.

The invention provides a method for generating a measurement index of thesis innovation quality based on a big language model, and relates to the technical field of thesis innovation quality measurement, and the method comprises the steps: firstly obtaining to-be-tested thesis data; the to-be-tested paper data is input into a preset science and technology paper innovation quality measurement model, the science and technology paper innovation quality measurement model comprises a novelty measurement module, a usefulness score calculation module and a measurement index calculation module, and the novelty measurement module carries out keyword extraction on the to-be-tested paper data through a constructed large language model; calculating to obtain a novelty score according to the keyword; the usefulness score calculation module calculates a usefulness score according to the to-be-tested paper data; and inputting the novelty score and the usefulness score into a measurement index calculation module, wherein a measurement index is used for measuring the innovation quality of the to-be-measured paper. According to the method, the distinction degree of innovative measurement of the paper is effectively improved, the comprehensiveness of prediction dimensions is ensured, measurement index generation is performed on the paper from an objective angle, and the reliability of a measurement result is ensured.

The embodiment of the invention belongs to the field of artificial intelligence and big data, and relates to a user clue mining method, which comprises the following steps of: obtaining a user call text, performing data cleaning on the user call text, and inputting a preset tag processing big model to perform text keyword extraction processing to obtain a first user data tag, carrying out data recall processing on the potential user call text through the first user data label to obtain weak label data, taking the weak label data as a clue training data set, training the general model to obtain a clue model, inputting the potential user call text into the clue model to carry out clue search processing, generating a second user data label, and uploading the first user data tag and the second user data tag to a tag information base, and carrying out classification processing on the transaction keyword and the clue keyword to obtain user clue information. According to the invention, a closed-loop system for clue mining is established, so that the clue mining efficiency and the clue amount mined from stock customers can be improved.

The invention discloses a question and answer method, device and equipment based on a graph database and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining a target question of a user, and performing question similarity verification on the target question and a question knowledge base; under the condition that it is recognized that the problem similarity verification is not passed, keyword extraction is conducted on the target problem, and a target keyword of the target problem is obtained; based on the natural language large model, performing semantic analysis on the target problem to obtain target semantic information; and based on the general language large model, according to the target keyword and the target semantic information, determining a graph database query statement, and according to the graph database query statement, determining a target answer of the target question from the target graph database. According to the technical scheme, after similarity verification, keyword extraction and semantic analysis are carried out on the problems, the optimized graph database query statement is generated through the large model, and the efficiency and accuracy of graph database query can be effectively improved.

A dialogue system for cognitively impaired adults based on CST principles is provided. The dialogue system includes an encoder module, a decoder module, and an external knowledge module. The encoder module is configured to classify utterance and execute a progressive mask context training part and a text classification part. The progressive mask context training part performs a sentiment analysis function and a keyword extraction. The text classification part performs text classification tasks comprising emotion, strategy, and CST classifications. The decoder module is in electrical communication with the encoder module for response generation according to predicted values derived from attention weights in response to outcomes of the encoder module from the utterance. The external knowledge module, the encoder module, and the decoder module interact with each other to form a dialogue framework embedded with CST principles through multi-source knowledge fusion and to guide the dialogue framework to generate responses.

The invention discloses an LLM-based document structuring automatic processing method and system, and relates to the technical field of document processing, and the method comprises the following steps: S1, document input and preprocessing: removing special characters in a disordered text, unifying a coding format, and adjusting the semantic density; s2, redundancy removal and keyword extraction: removing repeated information in the disordered text, and extracting a first keyword; s3, semantic extraction and structuring: parsing the disordered text sentence by sentence according to the first keyword, and extracting main meaning and related semantic information corresponding to each sentence to obtain an extraction result which comprises a second keyword and sentence meaning of the same sentence; s4, constructing and updating a semantic stack: constructing and updating the semantic stack according to an extraction result, and storing a semantic unit; one semantic unit corresponds to the same extraction result; and S5, structured output: outputting the data in the semantic stack in a preset format. The method is suitable for structured processing of complex disordered documents in multiple fields and multiple types.

The invention discloses an online medical inquiry method based on language reasoning analysis of a large language model. The method comprises the following steps: acquiring doctor seeing information of a patient; constructing a medical knowledge base and a drug database; carrying out keyword extraction on the doctor seeing information of the patient based on the medical concept to construct an inference tuple database containing symptom elements, diagnosis elements, complication elements and treatment elements; and constructing a corresponding semantic network by taking each element in the reasoning tuple database as a node and taking a medical reasoning direction as a relation edge, and forming a reasoning model by the semantic network and a pre-trained large language model. The invention further provides an online medical inquiry system. According to the method provided by the invention, the medical concept can be fused into the man-machine interaction process to optimize the logicality of the inquiry result, so that more reliable medical suggestions are provided for the user.

The invention relates to the technical field of electronic shopping, in particular to a big data analysis method based on an e-commerce platform and an AI system, and the method comprises the following steps: collecting a click rate, a search frequency and a purchase record of a user on the e-commerce platform, carrying out the subdivision of the click rate, the search frequency and the purchase record, and creating a data set; according to the invention, by integrating the equipment fingerprint technology and the social media data, the analysis precision of the user behavior data is improved, and the capabilities of user identity recognition and behavior pattern analysis are further optimized. In-depth analysis is carried out on user interaction data such as click rate, search frequency and purchase records, consumer behavior patterns and preference prediction are refined through clustering and decision tree algorithms, and sentiment analysis and keyword extraction are carried out on social media posting data. Therefore, the enterprise can monitor the market dynamic state in real time and quickly adjust the marketing strategy. And the accuracy of market prediction and the pertinence of marketing activities are improved.

The invention relates to the technical field of financial risk control, and discloses a financial risk control system based on a block chain and public opinions, and the system comprises a public opinion data collection module which is used for collecting financial-related public opinion data from multiple channels; the public opinion analysis and processing module is used for performing sentiment analysis, keyword extraction and trend prediction on the collected public opinion data; the block chain storage module is used for storing public opinion analysis results and risk related data, and is responsible for the non-tampering and transparency of the data; the intelligent contract execution module is used for dynamically triggering risk control measures according to the public opinion analysis result; the risk assessment and credit scoring module is used for performing dynamic risk assessment on the enterprise and generating a credit score; and the risk early warning and control module is used for generating risk early warning information and automatically executing a risk control strategy. According to the invention, through the distributed account book and the consensus mechanism of the block chain, the system ensures that the whole-process records of the public opinion analysis result, the risk score and the risk control strategy cannot be tampered.

The invention discloses a message content extraction method and device, computer equipment and a storage medium, and relates to the technical field of big data. The method comprises the following steps: firstly, converting a to-be-audited message into a keyword vector through keyword extraction and vector conversion; and then, performing field classification on the keyword vector by utilizing a pre-trained field classification model, and determining a business field to which the message belongs. And selecting a matched target business domain knowledge graph according to a domain classification result, and generating a vector representation of the matched target business domain knowledge graph. Then, combining the vector representation, and applying a preset long-short term memory network model to carry out semantic coding on the message to generate a semantic representation vector; and finally, based on the semantic representation vector, accurately identifying key entities, attributes and relationships in the message through a conditional random field model. The invention further relates to the technical field of block chains, and the to-be-audited message is stored on the block chain node. According to the method and the device, the accuracy and the efficiency of message content extraction are improved, and key entities, attributes and relationships can be automatically identified.

A chatbot system detects commercial intent during conversations with users and provides targeted advertisements. The chatbot system extracts keyword candidates from a conversation and assigns relevance scores based on the meaning of the conversation and commercial scores based on a machine learning model trained to detect commercially-related keywords. The chatbot system selects keywords based on a combination of the scores and transmits the selected keywords to advertising content servers that provide advertising content including advertisements selected using the keywords. The chatbot system displays the advertisements to the user during the conversation. The chatbot system may integrate the advertisements into the chatbot's responses, select advertisements based on a predicted click-through rate for the user, train the machine learning model using translations of labeled examples, access a long-term memory of the user's conversations and user profile data to provide context for the advertisements, and analyze messages from multiple users in group chats.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems. A system and method for providing conversation intelligence services may include pre-processing, transcribing, and post-processing. A conversation recording may be pre-processed generating a conversation record (e.g., conversation object). The pre-processed conversation recording may be transcribed into a transcript. The transcript may be post-processed which may include keyword extraction, topic extraction, feature extraction, event generation, trigger action, and / or search indexing. Conversation information may be presented based on the pre-processing, the transcribing, and the post-processing.