Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

Abstract

This paper presents NLP Lean Programming framework (NLPf), a new framework for creating custom natural language processing (NLP) models and pipelines by utilizing common software development build systems. This approach allows developers to train and integrate domain-specific NLP pipelines into their applications seamlessly. Additionally, NLPf provides an annotation tool which improves the annotation process significantly by providing a well-designed GUI and sophisticated way of using input devices. Due to NLPf's properties developers and domain experts are able to build domain-specific NLP applications more efficiently. NLPf is Opensource software and available at https:// gitlab.com/schrieveslaach/NLPf. IntroductionNowadays more and more business models rely on the processing of natural language data, e. g. companies extract relevant eCommerce data from domain-specific documents. The required eCommerce data could be related to various domains, e. g. life-science, public utilities, or social media, depending on the companies' business models.Furthermore, the World Wide Web (WWW) provides a huge amount of natural language data that provides a wide variety of knowledge to human readers. This amount of knowledge is unmanageable for humans and applications try to make this knowledge more accessible to humans, e. g. Treude and Robillard (2016) make natural language text about software programming more accessible through a natural language processing (NLP) application.All these approaches have in common that they require domain-specific NLP models that have been trained on a domain-specific and annotated corpus. These models will be trained by using different NLP frameworks and these models have to be evaluated for every annotation layer. For example, named entity recognition (NER) of Stanford CoreNLP (Manning et al., 2014) might work better than NER of OpenNLP (Reese, 2015, Chapter 1); the chosen segmentation tool, e. g. UDPipe (Straka and Straková, 2017), might work better than Stanford CoreNLP's segmentation tool, and so on. Existing studies show that domain specific training and evaluation is a common approach in the NLP community to determine the best-performing NLP pipeline (Buyko et al., 2006; Giesbrecht and Evert, 2009; Neunerdt et al., 2013; Omran and Treude, 2017).Developers of NLP applications are forced to create domain-specific corpora to determine the best-performing NLP pipeline among many NLP frameworks. During this process they face various obstacles:• The training and evaluation of different NLP frameworks requires a lot of effort of scripting or programming because of incompatible APIs.• Domain experts who annotate domainspecific documents with a GUI tool struggle with an insufficient user experience.• There are too many combinations how developers can combine these NLP tools into NLP pipelines.• The generated NLP models as a build artifact have to be integrated manually into the application code.NLP Lean Programming framework (NLPf) addresses these issues. NLPf provides a standardized p...