NLP Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining Tools (2023)

Abstract: NLP Workbench is a web-based platform for text mining that allows non-expert users to obtain semantic understanding of large-scale corpora using state-of-the-art text mining models. The platform is built upon latest pre-trained models and open source systems from academia that provide semantic analysis functionalities, including but not limited to entity linking, sentiment analysis, semantic parsing, and relation extraction. Its extensible design enables researchers and developers to smoothly replace an existing model or integrate a new one. To improve efficiency, we employ a microservice architecture that facilitates allocation of acceleration hardware and parallelization of computation. This paper presents the architecture of NLP Workbench and discusses the challenges we faced in designing it. We also discuss diverse use cases of NLP Work- bench and the benefits of using it over other approaches. The platform is under active devel- opment, with its source code released under the MIT license. A website and a short video demonstrating our platform are also available.

## components nlp workbench already includes a variety of tools and models for text mining. most of the components come from state-of-the-art research in the respective subfields. others are baseline implementations to demonstrate nlp workbench's extensibility, showing that developers can straightforwardly incorporate new tools and build pipelines from existing ones. one benefit of the flexible and modular design as described in §3 is that all built-in tools and models can easily be replaced or upgraded. existing tools and models in nlp workbench include: named entity recognition the task, known as ner, is to identify mentions to entities such as people, organizations, and locations. we incorporated the ner model from pure @xcite , which achieved good performance by simply fine-tuning bert @xcite . coreference resolution to determine which entity a pronoun refers to, we adopted the heuristic algorithm by @xcite that is based on recency and type agreement. entity linking mentions to entities in the text are disambiguated and linked to wikidata (vrandečić and krötzsch, 2014) entities. candidate entities are generated by a fuzzy match on name. in addition to name similarity, the ranking of candidates utilizes the cosine similarity between the sentence embeddings @xcite of the context and the descriptions of the candidate entity from wikipedia and wikidata. ## use cases text mining has been proven useful in a variety of domains, such as corporate finance, patent research, life sciences, and many others @xcite . nlp workbench, as a fullfledged text mining platform, has been or has the potential to be applied in many of these domains. some of the use cases are described in this section. digital humanities accessible and reliable nlp tools are useful in digital humanities projects such as linked infrastructure for networked cultural scholarship (lincs) 16 . a major component of the lincs project is to generate linked data from natural language cultural heritage texts. the key steps are ner, coreference resolution, entity linking, and relation extraction, all of which are made available in nlp workbench. through the simple user interface, digital humanists and their students can choose the available models that suit their data, and easily connect these steps into a custom workflow. the centre for artificial intelligence, data, and conflict (caidac) 17 houses human right scholars interested in how armed groups and extremists use social media to promote their agendas. nlp workbench offers such researchers convenient tools for collecting tweets foot_13 for analysis. posts can be subjected to the nlp tools of interest, and the social network underlying the corpus can be visualized, explored, and analyzed. all of these tasks are done through an accessible interface, requiring no programming from the users, enabling them to perform more and larger studies in a fraction of the time otherwise required. business analytics business analysts ask questions like "are recent news reports about apple inc. positive or negative?". these type of questions can easily be answered by nlp workbench. after performing ner and entity linking on the news articles, the analyst can conduct a semantic search to find the articles that are related to apple inc. rather than apple the fruit. then, the analyst can use the sentiment analysis tool and visualize the distribution of sentiment polarity scores with kibana lens, as shown in the bottom right screenshot in figure 1 . ## roadmap nlp workbench is still in its early stages of development, and we are actively working on improving the system. besides usability, stability, and security updates, we plan to work on the following major features in the near future: human-in-the-loop nlp adding annotation support to the web interface will allow users to provide feedback to the outputs of models. this will help researchers to collect domain-specific labelled data and improve the performance of the models in a human-in-the-loop fashion @xcite . improved corpus management managing document collections is a crucial aspect of text mining @xcite . currently, corpora are manually imported into elasticsearch or created by crawling social media. we hope to improve the way users access document collections. this can be done by connecting nlp workbench to datasets @xcite and datalab @xcite where popular text datasets are already available. in addition to crawling from social media, we also plan to support creating a new corpus by doing web search on a search engine. more text mining tools the extensible design of nlp workbench allows us to keep existing tools and models up to date by replacing them when better models are released, and integrate emerging text mining tools to the system. for example, we hope to add claim extraction models to facilitate fact checking tasks @xcite . ## conclusion we introduced nlp workbench, a platform that caters to all three major aspects of text mining systems: corpus management, text mining tools, and user interface. we explained what design features make nlp workbench efficient and extensible, and how it can be used in a variety of applications.