The Electronic Health Record (EHR) is an essential part of the modern medical system and impacts healthcare delivery, operations, and research. Unstructured text is attracting much attention despite structured information in the EHRs and has become an exciting research field. The success of the recent neural Natural Language Processing (NLP) method has led to a new direction for processing unstructured clinical notes. In this work, we create a python library for clinical texts, EHRKit. This library contains two main parts: MIMIC-III-specific functions and tasks specific functions. The first part introduces a list of interfaces for accessing MIMIC-III NOTEEVENTS data, including basic search, information retrieval, and information extraction. The second part integrates many third-party libraries for up to 12 off-shelf NLP tasks such as named entity recognition, summarization, machine translation, etc.
We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages. Compared to existing widely used toolkits, Stanza features a language-agnostic fully neural pipeline for text analysis, including tokenization, multi-word token expansion, lemmatization, part-of-speech and morphological feature tagging, dependency parsing, and named entity recognition. We have trained Stanza on a total of 112 datasets, including the Universal Dependencies treebanks and other multilingual corpora, and show that the same neural architecture generalizes well and achieves …
We introduce biomedical and clinical English model packages for the Stanza Python NLP library. These packages offer accurate syntactic analysis and named entity recognition capabilities for biomedical and clinical text, by combining Stanza's fully neural architecture with a wide variety of open datasets as well as large-scale unsupervised biomedical and clinical text data. We show via extensive experiments that our packages achieve syntactic analysis and named entity recognition performance that is on par with or surpasses state-of-the-art results. We further show that these models do not comp…
We present fastHan, an open-source toolkit for four basic tasks in Chinese natural language processing: Chinese word segmentation (CWS), Part-of-Speech (POS) tagging, named entity recognition (NER), and dependency parsing. The backbone of fastHan is a multi-task model based on a pruned BERT, which uses the first 8 layers in BERT. We also provide a 4-layer base model compressed from the 8-layer model. The joint-model is trained and evaluated on 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in dependency parsing and NER, achieving SOTA performance in CWS and POS. Be…
Reinforcement learning (RL) has recently shown impressive performance in complex game AI and robotics tasks. To a large extent, this is thanks to the availability of simulated environments such as OpenAI Gym, Atari Learning Environment, or Malmo which allow agents to learn complex tasks through interaction with virtual environments. While RL is also increasingly applied to natural language processing (NLP), there are no simulated textual environments available for researchers to apply and consistently benchmark RL on NLP tasks. With the work reported here, we therefore release NLPGym, an open-…
pysentimiento: A Python Toolkit for Opinion Mining and Social NLP tasks
Instituto de Ciencias de la Computación, CONICET, Universidad de Buenos Aires（布宜诺斯艾利斯大学 CONICET 计算机科学研究所）；Facultad de Astronomía, Matemática y Física, Universidad Nacional de Córdoba（科尔多瓦国立大学天文、数学与物理学院）；IIIA-CSIC（西班牙国家研究委员会人工智能研究所）
ParsiPy: NLP Toolkit for Historical Persian Texts in Python
Amirkabir University of Technology（阿米尔卡比尔理工大学）；University of Tehran（德黑兰大学）；Open Science Laboratory（开放科学实验室）；University of Southern California（南加州大学）；Institute for Humanities and Cultural Studies（人文与文化研究院）；Qatar Computing Research Institute（卡塔尔计算研究所）；Sharif University of Technology（谢里夫理工大学）
Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python
VA Salt Lake City Health Care System（VA盐湖城医疗系统）；University of Utah（犹他大学）；Veterans Health Administration Office of Analytics and Performance Integration（退伍军人事务管理局分析与绩效整合办公室）
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
Centre for Quantitative Medicine, Duke-NUS Medical School（杜克-新加坡国立大学医学院定量医学中心）；Department of Biomedical Informatics, Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院生物医学信息学系）；Department of Linguistics, Northwestern University（西北大学语言学系）；Department of Computer Science, Yale University（耶鲁大学计算机科学系）；Yale School of Public Health, Yale University（耶鲁大学公共卫生学院）；Yale New Haven Hospital, Yale School of Medicine, Yale University（耶鲁大学医学院耶鲁纽黑文医院）；Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health（美国国立卫生研究院国家眼科研究所流行病学与临床应用部）；National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；Section of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University（耶鲁大学医学院生物医学信息学与数据科学科）；Information Technology Center, University of Tokyo（东京大学信息技术中心）
Institute for Natural Language Processing（自然语言处理研究所）；University of Stuttgart（斯图加特大学）
In this paper, we describe Function Assistant, a lightweight Python-based toolkit for querying and exploring source code repositories using natural language. The toolkit is designed to help end-users of a target API quickly find information about functions through high-level natural language queries and descriptions. For a given text query and background API, the tool finds candidate functions by performing a translation from the text to known representations in the API using the semantic parsing approach of Richardson and Kuhn (2017). Translations are automatically learned from example text-c…
University of Yamanashi（山梨大学）；Tsukuba University of Technology（筑波技术大学）；University of Tsukuba（筑波大学）
This paper describes the ExKaldi-RT online automatic speech recognition (ASR) toolkit that is implemented based on the Kaldi ASR toolkit and Python language. ExKaldi-RT provides tools for building online recognition pipelines. While similar tools are available built on Kaldi, a key feature of ExKaldi-RT that it works on Python, which has an easy-to-use interface that allows online ASR system developers to develop original research, such as by applying neural network-based signal processing and by decoding model trained with deep learning frameworks. We performed benchmark experiments on the mi…
SDialog: A Python Toolkit for End-to-End Agent Building, User Simulation, Dialog Generation, and Evaluation
Idiap Research Institute（Idiap研究 institute）；Université de Toulon（里昂大学）；Aix Marseille Univ（马赛大学）；LIS（LIS实验室）；Avignon University（阿维尼翁大学）；Zenidoc University of Zurich（苏黎世Zenidoc大学）；Stenograf；Solventum CNRS & ILLS（Solventum CNRS与ILLs）；Johns Hopkins University（约翰霍普金斯大学）
We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP). The open-source toolkit offers a detailed hierarchy of modules and classes implementing all stages of a pipeline for converting sentences to string diagrams, tensor networks, and quantum circuits ready to be used on a quantum computer. lambeq supports syntactic parsing, rewriting and simplification of string diagrams, ansatz creation and manipulation, as well as a number of compositional models for preparing quantum-friendly representations of sentences, employing various degrees of syntax se…
We present a new tool for training neural network language models (NNLMs), scoring sentences, and generating text. The tool has been written using Python library Theano, which allows researcher to easily extend it and tune any aspect of the training process. Regardless of the flexibility, Theano is able to generate extremely fast native code that can utilize a GPU or multiple CPU cores in order to parallelize the heavy numerical computations. The tool has been evaluated in difficult Finnish and English conversational speech recognition tasks, and significant improvement was obtained over our b…
Pre-trained language models and other generative models have revolutionized NLP and beyond. However, these models tend to reproduce undesirable biases present in their training data. Also, they may overlook patterns that are important but challenging to capture. To address these limitations, researchers have introduced distributional control techniques. These techniques, not limited to language, allow controlling the prevalence (i.e., expectations) of any features of interest in the model's outputs. Despite their potential, the widespread adoption of these techniques has been hindered by the d…
NeMo (Neural Modules) is a Python framework-agnostic toolkit for creating AI applications through re-usability, abstraction, and composition. NeMo is built around neural modules, conceptual blocks of neural networks that take typed inputs and produce typed outputs. Such modules typically represent data layers, encoders, decoders, language models, loss functions, or methods of combining activations. NeMo makes it easy to combine and re-use these building blocks while providing a level of semantic correctness checking via its neural type system. The toolkit comes with extendable collections of p…
