Cooking recipe display system (100) is provided with database (11), extraction unit (21a), emphasis unit (21b), and output unit (23). Database (11) stores a plurality of cooking recipes each being expressed in natural language sentences. Extraction unit (21a) extracts one or more recipe terms from the natural language sentences constituting one cooking recipe selected from the plurality of cooking recipes. Emphasis unit (21b) determines an emphasis method for the one or more recipe terms. Output unit (23) outputs the one cooking recipe with the one or more recipe terms emphasized according to the emphasis method determined by emphasis unit (21b).

Methods and systems for improved categorization of queries are disclosed. In one aspect, first queries having query results limited to a particular category are identified. Second queries including a first query are also identified. For identified second queries, a pattern is generated based on the second query and the included first query, and a rank of the first query's particular category in results for the second query and a percentage of the second query's results having a category equivalent to the included first query's particular category are determined. The ranks and percentages are aggregated for matching patterns, and second patterns with aggregated ranks and percentages meeting a criterion are determined. Results for a third query are limited to a category equivalent to a particular category for a first query included in the third query, and then transmitted over a computer network to a client device.

Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

A technology trend prediction method and system are provided. The method comprises acquiring paper data, and further comprises following steps: processing the paper data to generate a candidate technology lexicon; screening the candidate technology lexicon based on mutual information; calculating an independent word forming probability of an OOV word; extracting missed words in a title using a bidirectional long short-term memory network and a conditional random field (BI-LSTM+CRF) model; predicting a technology trend. The technology trend prediction method and system provided analyzes relationship of technology changes in a high-dimensional space, and predicts a development of technology trend based on time by extracting technical features of papers through natural language processing and time sequence algorithms.

A technique for arranging nodes on a landscape based on a viewpoint desired by a user is provided. One aspect of the present disclosure relates to a node processing apparatus for synthesizing and extracting feature quantities that meet the needs of a user analysis from a plurality of types of feature quantities assigned for each node of a node set, and the node processing apparatus includes a receiving unit configured to receive, from a user, a designation related to an arrangement of nodes selected from the node set on an analysis axis assumed by the user, and a node processing unit configured to synthesize and extract feature quantities based on the arrangement of the received designation.

A completely unsupervised solution for generating and maintaining a list of lexically similar terms for an e-commerce system is provided. Given a particular electronic collection of items in an e-commerce system, each term in a first item listing is initially paired with each term in a second item listing to form a set of token pairs. The token pairs represent possible candidates for being synonyms. For a respective token pair, an attempt is made to match the shortest token of the token pair to the longest token of the token pair, character by character. If a match is successful, the terms in the token pair are automatically labeled as synonyms for the particular electronic collection of items. Some implementations automatically filter out false positives and/or token pairs that are unrelated and not likely synonyms. The solution can be performed at the granularity of a product, category, vertical, or entire catalog.

An approach is provided for using parsing rules to automatically identify attributes and attribute values from documents and generate metadata that maps attribute values to display labels that may be searched, filtered, and sorted upon within an external storage service. A document processing system maintains parsing rules that define how to identify field labels, which represent attributes, and corresponding field values, which represent attribute values, and metadata mappings that map associations between field values and display labels. The display labels are used within the graphical user interface of the external storage service. The system receives a batch of multiple documents and uses the parsing rules to identify field labels and field values. The system generates metadata using the defined metadata mappings and associates the metadata to the documents processed. The system then sends the documents and their associated metadata to the external storage service for storage.

A document analysis device that includes a memory operable to store a machine learning model configured to receive a sentence as an input and to output a classification identifier that is associated with a sentence type for the received sentence. The device further includes an artificial intelligence (AI) processing engine configured to receive a document comprising text, to sentences within the document, and to classify the sentences using the machine learning model. The AI processing engine is further configured to identify tagging rules for the document and to annotate one or more sentences from the document with a sentence type that matches a sentence type that is identified by the tagging rules for the document.

A centralized system may collect and aggregate assessments from multiple websites. An aggregate score may be calculated for the resource that cumulatively considers assessments from a plurality of different websites from which assessments are received from users. Text descriptions associated with each of the assessments may be provided to a machine-learning system that uses a trained model to assign identifiers to the assessments as they are received. These identifiers may include common words or text that are descriptive of different facets of user experiences related to receiving and using the resource. After selecting one or more identifiers, assessments associated with that identifier may be included or excluded from the display. Additionally, the overall aggregate score for the resource may be recalculated by removing components of that score that are based on assessments with identifiers that have been selected for exclusion.

This invention pertains to a system and method for a recommendation engine that helps customers find the answers and services. The engine understands the context of what the user is looking at and recommends products and services to meet the needs of the user. The engine is operated through a search index that gives access to more data and better scalability to provide answers and optimal solutions to the user. The recommendation engine functions as a search tool, whereby the user can search by fields, including title, abstract, claims, inventor, specification or assignee. The recommendation engine can also recommend products and services provided by platform partners or third party providers, and implement a tracking system for referrals to third party providers for revenue generation.