anukat2015,anukat2015,github

seco-lexicalanalysis-1

Text Analytics -> SeCo lexical analysis tools

secos

Kognetics -> Text Analytics -> SECOS is an unsupervised compound splitter that uses information from a distributional thesaurus (DT). Details about its working can be found in our paper.

seeder

?2013/4 project, a NodeJS/Javascript webapp that can be used to create data rich knowledge graphs

seek

BigLnq -> Analytics Research Platform -> For finding, sharing and exchanging Data, Models, Simulations and Processes in Science.

? Sefarad (needs LMF) is an application developed to explore data by making SPARQL queries to the endpoint you choose without writing more code. You can also create your own cores if you have a big collection of data (LMF required). To view your data you can customize your own widgets and visualize it through them.

segrada

BigLnq -> Semantic Graph -> Segrada - Semantic Graph Database

semafor

BigLnq -> TExt Analytics - Frame Semantic Parser -> SEMAFOR is a frame-semantic parser developed by Dipanjan Das, Sam Thomson, Meghana Kshirsagar, André F. T. Martins, Nathan Schneider, Desai Chen, and Noah Smith. Open-source software developed for research purposes, SEMAFOR automatically processes English sentences according to the form of semantic analysis in Berkeley FrameNe

semafor-semantic-parser

Ruby BigLinq -> Automatically exported from code.google.com/p/semafor-semantic-parser

semagrow

Federated Sparql Query -> The main Semagrow repository where the core system is developed

semantic-integration

Semantic Matching -> Semantic Web project which aims to integrate heterogeneous databases by matching properties and features obtained from products descriptions given in a natural language. It uses Java and the ANTLR parser

semantic-ui

Semantic UI -> Semantic is a UI component framework based around useful principles from natural language.

semanticizer

Text Analytics -> Entity Linking for the masses

semantika

Kognetics - Biglnq -> Q&A -> Semantika source code for ontology-based information system

sematch

Kognetics -> Knowledge Graph -> semantic entity search framework

semcps

Integrating Industry 4.0 related standards, AutomationML by means of Probabilistic Soft Logic (PSL) and Ontologies

sempre

Question & Answer -> Semantic Parser with Execution -> Converting NL Questions to Logically Mapped sub clauses to write SPARQL queries

semweb2nl

Kognetics -> Semantic Web related concepts converted to Natural language

senna.js

:seedling: A blazing-fast Single Page Application engine

senpy

Sentiment Analytics -> A sentiment and emotion analysis server in Python

senpy-full

Sentiment Analytics -> Dockerfile and submodules to deploy senpy and all the modules

sense2vec

Biglnq -> Use spaCy to go beyond vanilla word2vec

sentence-boundary-detection-nn

Kognetics -> Sentence Boundary Detection using Deep Neural Networks.

sentence-compression

Text ANalytics -> Sentence-level compressions via deletion. It is a modified implementation of the ILP model described in Clarke and Lapata, 2008, "Global Inference for Sentence Compression: An Integer Linear Programming Approach".

senticnet

Kognetics -> BigLnq - > SenticNet JSON -> JSON conversions of sentic.net data; http://sentic.net/senticnet-2.pdf

senticnetapi

BigLnq -> Sentiment Analytics -> A Graph-Based Approach to Commonsense Concept Extraction and Semantic Similarity Detection

sentime

Biglnq -> Ensemble Process-> Text Analytics - Sentiment - Tweet Sentiment Analysis

sentiment

BigLnq -> Sentiment Analytics -> AFINN-based sentiment analysis for Node.js.

sentiment-analysis

Sentiment analysis framework developed by CERTH.

sentiment-analysis-movie-reviews

Sentiment Analytics -> A iPython notebook that tests Graphify's feature extraction and selection algorithm as a logistic regression classifier

sentinel

BigLnq -> Text Sentiment Analytics -> Named Entity Sentiment Analysis -> SentiNEL system is developed for sentiment analysis of tweets based on SemEval2015 Task10-Subtask A: Contextual Polarity Disambiguation. The purpose of SentiNEL is that given a message containing a marked instance of a word or a phrase, determines whether that instance is positive, negative or neutral in that context. SentiNEL is inspired by the IOA system. The main differences are that SentiNEL extracts more features (e.g. Char 3, 4, 5 grams, Hashtag, longer Word2Vec dimension, more lexicons etc.) for training. Besides, SentiNEL trains L2-regularized logistic regression SVM classifier with C value 0.5. The code is based on Webis system.

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