Search Results for "pam-python"

Phalanx - Deidentify Safe Harbor Deidentification Mode of Phalanx is an abridged pipeline of NLP annotators culminating in NER annotators which write output of text offsets. It uses the Safe Harbor deidentification method.

A minimalist approach to share text documents and data annotations. Allows a large number of different annotations to be represented. Project files contain: - simple code to hold/read/write data and perform sample processing. - BioC-formatted corpora - BioC tools that work with BioC corpora BioC goals - simplicity - interoperability - broad use - reuse There should be little investment required to learn to use a format or a software module to process that format. We are...

ElixirFM is a high-level implementation of Functional Arabic Morphology. The core of ElixirFM is written in Haskell, while interfaces in Python and Perl support lexicon editing and other interactions. http://github.com/otakar-smrz/elixir-fm

Encode Arabic provides tools for encoding and decoding Arabic in Haskell, Python, Perl, or LaTeX. Interprets the ArabTeX notation to generate original orthography or phonetic transcription. Supports Buckwalter and other romanizations. Converts legacy byte encodings into Unicode. http://github.com/otakar-smrz/encode-arabic

Part-of-speech tagging is the task of assigning symbols from a particular set to words in a natural language text. ACOPOST implements and extends well-known machine learning techniques and provides a uniform environment for testing.

This project concerns the development of human language technology resources, based on the approach to share or recycle resources between closely related language. http://gerhard.pro/closely-related-languages/

The central problem to be addressed in this project concerns a multidisciplinary (linguistics and computational linguistics) investigation into sharing of knowledge and resources between closely-related languages, specifically relating to the automatic processing of compounds. Specifically, we will explore the possibility to create new knowledge about closely-related languages, and efficiently develop additional, more advanced resources for (a) compound segmentation; and (b) the semantic...