Categories
analysis of language Backbone Corpora for Content and Language Integrated Learning corpus linguistics Java

BACKBONE Annotator 3.8 featured on Softpedia

BACKBONE Annotator 3.8 featured on Softpedia

Categories
analysis of language Backbone Corpora for Content and Language Integrated Learning corpus linguistics Java

BACKBONE Annotator 3.8 featured on Softpedia

BACKBONE Annotator 3.8 featured on Softpedia

Categories
analysis of language Java software

LingPipe

LingPipe is a suite of Java libraries for the linguistic analysis of human language.

Feature Overview

LingPipe’s information extraction and data mining tools:
track mentions of entities (e.g. people or proteins);
link entity mentions to database entries;
uncover relations between entities and actions;
classify text passages by language, character encoding, genre, topic, or sentiment;
correct spelling with respect to a text collection;
cluster documents by implicit topic and discover significant trends over time; and
provide part-of-speech tagging and phrase chunking.

Architecture

LingPipe’s architecture is designed to be efficient, scalable, reusable, and robust. Highlights include:
Java API with source code and unit tests;
multi-lingual, multi-domain, multi-genre models;
training with new data for new tasks;
n-best output with statistical confidence estimates;
online training (learn-a-little, tag-a-little);
thread-safe models and decoders for concurrent-read exclusive-write (CREW) synchronization; and
character encoding-sensitive I/O

Categories
analysis of language Java software

LingPipe

LingPipe is a suite of Java libraries for the linguistic analysis of human language.

Feature Overview

LingPipe’s information extraction and data mining tools:
track mentions of entities (e.g. people or proteins);
link entity mentions to database entries;
uncover relations between entities and actions;
classify text passages by language, character encoding, genre, topic, or sentiment;
correct spelling with respect to a text collection;
cluster documents by implicit topic and discover significant trends over time; and
provide part-of-speech tagging and phrase chunking.

Architecture

LingPipe’s architecture is designed to be efficient, scalable, reusable, and robust. Highlights include:
Java API with source code and unit tests;
multi-lingual, multi-domain, multi-genre models;
training with new data for new tasks;
n-best output with statistical confidence estimates;
online training (learn-a-little, tag-a-little);
thread-safe models and decoders for concurrent-read exclusive-write (CREW) synchronization; and
character encoding-sensitive I/O