On September 22nd, we are holding a tutorial on Distributional Semantics and JoBimText. This tutorial will also be held during the KONVENS 2016 conference in Bochum.
Participants can use the Tutorial Page for information and instructions.
On June 11th, we are holding a tutorial on Distributional Semantics and JoBimText. This tutorial will also be held during the NLDB 2015 conference in Passau.
We have released a new JoBimText pipeline. The main addition in this version is an API access via the JoBimViz interface. Users can start developing with JoBimText, e.g. using lexical expansion from a JoBimText model.
To demonstrate this capabilities, we held a Tutorial where the functionality is demonstrated in an example project.
Other improvements include:
You can download the pipeline in your desired version:
Today, we have released the Wikipedia Stanford model for English in JoBimViz demonstrator (formerly “web demo”). It was computed from an English Wikipedia dump from 2014 and features Sense Clustering and a Feature DT.
This model is a high accuracy model on a large corpus, that required much computation time. You can use it via the API for lexical expansion.
This release includes optimizations to the new PattaMaika component:
This release brings some minor improvements and new Holing Operations:
You can download the new release from Sourceforge:
Today, have released new JoBimText models for German news. They are the first released models based on the new JoBimText 0.1.0 pipeline. The provided models feature sense clusterings in different granularities:
The models are free for any use. We also provide them in the JoBimText web demo. The demo is now capable to parse German sentences.
We are proud to announce the next release of the JoBimText pipeline. The main addition of version 0.1.0 is the pattern matching engine PattaMaika, that can run locally and on Hadoop. The pattern matching engine is able to extract hierarchical relations between terms and is very flexible. It utilizes the Apache UIMA Ruta annotation engine to tag patterns. For more information on the pattern engine, consult the PattaMaika project page.
Other improvements include the re-organization of thirdparty models. Since their number grows with the increasing number of components (segmenters, taggers, parsers) they are now structure. Additionally, the build scripts have been updated.
You can download the new release from Sourceforge: JoBimText pipeline 0.1.0.
We are happy to announce a new JoBimText version release! The most significant change is the ability to run JoBimText Holing Operations on Hadoop using UIMA pipelines. Here are the updates:
You can download the latest version of JoBimText from Sourceforge.
Today, we have released the Wikipedia Trigram model for English in the web demo. It was computed from an English Wikipedia dump from 2013 and is one of the first models that contains a Bim DT (Feature DT).
The Bim DT is a “reversed Distributional Thesaurus”; users can find similar context features when they need more contexts to reduce sparsity issues. The Bim DT was computed using words as “Bims” and features as “Jos”. This demonstrates the general JoBimText approach, where users can define any type of Jos and Bims for their tasks.
Currently, we are using the Wikipedia trigram model to develop in-text contextualization, that is able to assign the induced word senses to words in text. Due to sparsity of contexts, the Bim DT helps in this application.