Natural Language Processing (NLP) Platform

Context:
- It can be provided by a Cloud-based NLP Platform to being an On-Premise-based NLP Platform.
- It can support Text Understanding (NLU) Task.
- It can have Pre-Trained Models.
- It can allow new models training using Training Dataset.
- It can be used to create an Linguistic-based System Platform or a NLP-based Production Systems.
- …
Example(s):
- one used to create a Personal Digital Assistant Service, such as Amazon Lex, Nuance 10 or IBM Watson NLP Platform.
- …
Counter-Example(s):
- an NLP Framework, or an NLP Library.
- a Visual Processing Platform.
See: Semantic Search.

References

(Che et al., 2010) ⇒ Wanxiang Che, Zhenghua Li, and Ting Liu. (2010). “LTP: A Chinese Language Technology Platform.” In: Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations.
- QUOTE: A Chinese natural language processing (NLP) platform always includes lexical analysis (word segmentation, part-of-speech tagging, named entity recognition), syntactic parsing and semantic parsing (word sense disambiguation, semantic role labeling) modules. It is a laborious and time consuming work for researchers to develop a full NLP Platform, especially for Chinese, which has fewer existing NLP tools. Therefore, it should be of particular concern to build an integrated Chinese processing platform. There are some key problems for such a platform: providing high performance language processing modules, integrating these modules smoothly, using processing results conveniently, and showing processing results directly. LTP (Language Technology Platform), a Chinese processing platform, is built to solve the above mentioned problems. It uses XML to transfer data through modules and provides all sorts of high performance Chinese processing modules, some DLL or Web service APIs, visualization tools, and some relevant corpora.