jWeb1T is an Java tool for efficiently searching n-gram data in the Web 1T 5-gram corpus format. It is based on a binary search algorithm that finds the n-grams and returns their frequency counts in logarithmic time. As the corpus is stored in many files, a simple index is used to retrieve the files containing the n-grams.
Apache OpenNLP is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services.
The Language Detection Library for Java is a Java library to detect the natural languages in which texts are written. This task is also known as "language identification", "language guessing", and "language recognition". It has over 99% precision for more than 40 languages. The supported languages are Afrikaans, Arabic, Bulgarian, Bengali, Czech, German, Greek, English, Spanish, Persian, Finnish, French, Gujarati, Hebrew, Hindi, Croatian, Hungarian, Indonesian, Italian, Japanese, Kannada, Korean, Macedonian, Malayalam, Marathi, Nepali, Dutch, Punjabi, Polish, Portuguese, Romanian, Russian, Slovak, Somali, Albanian, Swedish, Swahili, Tamil, Telugu, Thai, Tagalog, Turkish, Ukrainian, Urdu, Vietnamese, and Simplified/Traditional Chinese.
The Okapi project’s main purpose is to architect a set of building blocks for the creation of larger open source localization and translation tools. But many Okapi components are generic enough to be of interest to the text mining, natural language processing, and text retrieval communities. Okapi’s many text filters (HTML, Properties, XML (ITS XPath-based rules), OpenXML, ODF, Regex etc.) provide a straightforward way to access the text of multiple document formats. Its document events and pipeline can be made to integrate with other frameworks such as UIMA, LingPipe, OpenPipeline, OpenNLP, GATE, and Lucene. The advantage of Okapi’s text filters is that not only is text extracted, but all non-textual formatting is preserved. It is possible to decompose a document into events, process them via the pipeline, and then rebuild the input document without loss. Structural information can be added to Okapi document events so that tables, lists, links, titles etc. are grouped together and treated as a unit. This is useful when context based on a “universal” document structure is needed. The Okapi event model supports user configurable annotations, similar to UIMA, but simpler and more restricted in scope. User can annotate spans of text or add new resources such as translation memory matches, terminology, token types, or part of speech information.