18 skills found
zhongzhuoyao / HCCR GoogLeNetThis project is about directional feature extraction and HCCR-GoogLeNet CNN architecture definition for Caffe platform.
adobe-research / Deft CorpusThe Definition Extraction From Text corpus and relevant formatting scripts
jgc128 / DefVectorsA tool for semantic relation extraction. The program finds pairs of semantically related words based on the text definitions coming from the Wikipedia articles (other texts may be also used). The extraction method implemented in this system is based on three similarity measures (cosine, gloss overlap, and Karaulov's measure) between texts and two nearest-neighbor algorithms (KNN and Mutual KNN). The tool is a cross-platform console application.
microsoft / Vscode Ts File Path SupportAn extension for VS Code that improves editor support for relative file paths in typescript/javascript sources, including auto-completion, renaming, go-to-definition, inlining and extraction.
tencent-ailab / ZEDThis is the repository for EMNLP 2022 paper "Efficient Zero-shot Event Extraction with Context-Definition Alignment"
YipingNUS / DefMinerMining Scientific Terms and their Definitions: A Study of the ACL Anthology (EMNLP 2013). [code+data]
amirveyseh / Definition ExtractionNo description available
DFKI-NLP / Defx[SemEval 2020] Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction
YiHan708 / EAU CNN Expression Recognition AlgorithmFacial expression recognition is one of the methods to obtain the change of human's inner emotion. The existing methods mostly extract the global facial features, but ignore the local features. According to the definition of psychologists' facial behavior coding system, different expressions have corresponding muscle motion units. Therefore, this paper proposes an expression recognition algorithm EAU-CNN based on extracting local representation of muscle motion units. For local representation, this paper calculates the generating regions of muscle motion units with different expressions, divides the face into 43 regions on the basis of 68 feature points of the face, and splices the regions into 8 local images according to the generation regions of facial organs and motion units. In order to extract the image features evenly, EAU-CNN uses 8 input channels to extract features, and stitches 4096 dimension full connection layer according to the proportion of the composition image area. The splicing full connection layer multiplies different expressions by different weight values to highlight the proportion of local composition image features in different expressions. After subsequent feature extraction and softmax function, the expressions are divided into seven categories: neutral, angry and disgusting , surprise, joy, sadness, fear. After verification, the average accuracy of the algorithm in CK + and Jaffe datasets reaches 99.85% and 96.61%, with the highest improvement of 16.09% and 25.42%, and the average accuracy of custom large fed data set reaches 98.6%, with the highest improvement of 19.68%. The algorithm verifies the importance of local representation for expression recognition.
ferrycode / AI Driven Social Media DashboardAI-Driven Social Media Dashboard monitors and ingests specified tweets using stream processing and leverages a serverless architecture and ML services (Amazon Translate and Amazon Comprehend) to translate and extract insights from those tweets. The diagram below presents the architecture you can build using the example code on GitHub. AI-Driven Social Media Dashboard architecture AI-Driven Social Media Dashboard deploys an Amazon Elastic Compute Cloud (Amazon EC2) instance running in an Amazon Virtual Private Cloud (Amazon VPC) that ingests tweets from Twitter. An Amazon Kinesis Data Firehose delivery stream loads the streaming tweets into the raw prefix in the solution's Amazon Simple Storage Service (Amazon S3) bucket. Amazon S3 invokes an AWS Lambda function to analyze the raw tweets using Amazon Translate to translate non-English tweets into English, and Amazon Comprehend to use natural-language-processing (NLP) to perform entity extraction and sentiment analysis. A second Kinesis Data Firehose delivery stream loads the translated tweets and sentiment values into the sentiment prefix in the Amazon S3 bucket. A third delivery stream loads entities in the entities prefix using in the Amazon S3 bucket. The Guidance also deploys a data lake that includes AWS Glue for data transformation, Amazon Athena for data analysis, and Amazon QuickSight for data visualization. AWS Glue Data Catalog contains a logical database which is used to organize the tables for the data on Amazon S3. Athena uses these table definitions to query the data stored on Amazon S3 and return the information to an Amazon QuickSight dashboard.
Media-Bias-Group / SciDefRepository for the paper "SciDef: Automating Definition Extraction from Academic Literature with Large Language Models"
Res-Tan / Hypernym Extractionhypernym extraction from definitions by recurrent neural networks using the part of speech information
jeffhj / CDMThe code and data for "Understanding Jargon: Combining Extraction and Generation for Definition Modeling" (EMNLP '22)
claudio-db / DefIEDefIE: Open information extraction from textual definitions (http://lcl.uniroma1.it/defie)
TobiasLee / DeftEval2020DeftEval 2020: Definition extraction Challenge
CENMetaLex / Metalex AnnotatorMetaLex concept extraction and definition recognition toolkit
zerfl / StaticRaidExtractionHero definition data extraction for Raid: Shadow Legends
dsciitism / SemEval 2020 Task 6DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition Extraction
