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Stanza v1.1.1 Release Notes

Release Date: 2020-08-13 // over 3 years ago

  • Overview

    🛠 This release features support for extending the capability of the Stanza pipeline with customized processors, a new sentiment analysis tool, improvements to the CoreNLPClient functionality, new models for a few languages (including Thai, which is supported for the first time in Stanza), new biomedical and clinical English packages, alternative servers for downloading resource files, and various improvements and bugfixes.

    🆕 New Features and Enhancements

    🆕 New Sentiment Analysis Models for English, German, Chinese : The default Stanza pipelines for English, German and Chinese now include sentiment analysis models. The released models are based on a convolutional neural network architecture, and predict three-way sentiment labels (negative/neutral/positive). For more information and details on the datasets used to train these models and their performance, please visit the Stanza website.

    🆕 New Biomedical and Clinical English Model Packages : Stanza now features syntactic analysis and named entity recognition functionality for English biomedical literature text and clinical notes. These newly introduced packages include: 2 individual biomedical syntactic analysis pipelines, 8 biomedical NER models, 1 clinical syntactic pipelines and 2 clinical NER models. For detailed information on how to download and use these pipelines, please visit Stanza's biomedical models page.

    👌 Support for Adding User Customized Processors via Python Decorators : Stanza now supports adding customized processors or processor variants (i.e., an alternative of existing processors) into existing pipelines. The name and implementation of the added customized processors or processor variants can be specified via @register_processor or @register_processor_variant decorators. See Stanza website for more information and examples (see custom Processors and Processor variants). (PR #322)

    👌 Support for Editable Properties For Data Objects : We have made it easier to extend the functionality of the Stanza neural pipeline by adding new annotations to Stanza's data objects (e.g., Document, Sentence, Token, etc). Aside from the annotation they already support, additional annotation can be easily attached through data_object.add_property(). See our documentation for more information and examples. (PR #323)

    👌 Support for Automated CoreNLP Installation and CoreNLP Model Download : CoreNLP can now be easily downloaded in Stanza with stanza.install_corenlp(dir='path/to/corenlp/installation'); CoreNLP models can now be downloaded with stanza.download_corenlp_models(model='english', version='4.1.0', dir='path/to/corenlp/installation'). For more details please see the Stanza website. (PR #363)

    👍 Japanese Pipeline Supports SudachiPy as External Tokenizer : You can now use the SudachiPy library as tokenizer in a Stanza Japanese pipeline. Turn on this when building a pipeline with nlp = stanza.Pipeline('ja', processors={'tokenize': 'sudachipy'}. Note that this will require a separate installation of the SudachiPy library via pip. (PR #365)

    🆕 New Alternative Server for Stable Download of Resource Files : Users in certain areas of the world that do not have stable access to GitHub servers can now download models from alternative Stanford server by specifying a new resources_url argument. For example, stanza.download(lang='en', resources_url='stanford') will now download the resource file and English pipeline from Stanford servers. (Issue #331, PR #356)

    👍 CoreNLPClient Supports New Multiprocessing-friendly Mechanism to Start the CoreNLP Server : The CoreNLPClient now supports a new Enum values with better semantics for its start_server argument for finer-grained control over how the server is launched, including a new option called StartServer.TRY_START that launches the CoreNLP Server if one isn't running already, but doesn't fail if one has already been launched. This option makes it easier for CoreNLPClient to be used in a multiprocessing environment. Boolean values are still supported for backward compatibility, but we recommend StartServer.FORCE_START and StartSerer.DONT_START for better readability. (PR #302)

    🆕 New Semgrex Interface in CoreNLP Client for Dependency Parses of Arbitrary Languages : Stanford CoreNLP has a module which allows searches over dependency graphs using a regex-like language. Previously, this was only usable for languages which CoreNLP already supported dependency trees. This release expands it to dependency graphs for any language. (Issue #399, PR #392)

    🆕 New Tokenizer for Thai Language : The available UD data for Thai is quite small. The authors of pythainlp helped provide us two tokenization datasets, Orchid and Inter-BEST. Future work will include POS, NER, and Sentiment. (Issue #148)

    👌 Support for Serialization of Document Objects : Now you can serialize and deserialize the entire document by running serialized_string = doc.to_serialized() and doc = Document.from_serialized(serialized_string). The serialized string can be decoded into Python objects by running objs = pickle.loads(serialized_string). (Issue #361, PR #366)

    👌 Improved Tokenization Speed : Previously, the tokenizer was the slowest member of the neural pipeline, several times slower than any of the other processors. This release brings it in line with the others. The speedup is from improving the text processing before the data is passed to the GPU. (Relevant commits: 546ed13, 8e2076c, 7f5be82, etc.)

    👉 User provided Ukrainian NER model : We now have a model built from the lang-uk NER dataset, provided by a user for redistribution.

    💥 Breaking Interface Changes

    Token.id is Tuple and Word.id is Integer : The id attribute for a token will now return a tuple of integers to represent the indices of the token (or a singleton tuple in the case of a single-word token), and the id for a word will now return an integer to represent the word index. Previously both attributes are encoded as strings and requires manual conversion for downstream processing. This change brings more convenient handling of these attributes. (Issue: #211, PR: #357)

    🔄 Changed Default Pipeline Packages for Several Languages for Improved Robustness : Languages that have changed default packages include: Polish (default is now PDB model, from previous LFG, #220), Korean (default is now GSD, from previous Kaist, #276), Lithuanian (default is now ALKSNIS, from previous HSE, #415).

    CoreNLP 4.1.0 is required : CoreNLPClient requires CoreNLP 4.1.0 or a later version. The client expects recent modifications that were made to the CoreNLP server.

    🚚 Properties Cache removed from CoreNLP client : The properties_cache has been removed from CoreNLPClient and the CoreNLPClient's annotate() method no longer has a properties_key argument. Python dictionaries with custom request properties should be directly supplied to annotate() via the properties argument.

    🛠 Bugfixes and Other Improvements

    🛠 Fixed Logging Behavior : This is mainly for fixing the issue that Stanza will override the global logging setting in Python and influence downstream logging behaviors. (Issue #278, PR #290)

    Compatibility Fix for PyTorch v1.6.0 : We've updated several processors to adapt to new API changes in PyTorch v1.6.0. (Issues #412 #417, PR #406)

    👌 Improved Batching for Long Sentences in Dependency Parser : This is mainly for fixing an issue where long sentences will cause an out of GPU memory issue in the dependency parser. (Issue #387)

    👌 Improved neural tokenizer robustness to whitespaces : the neural tokenizer is now more robust to the presence of multiple consecutive whitespace characters (PR #380)

    Resolved properties issue when switching languages with requests to CoreNLP server : An issue with default properties has been resolved. Users can now switch between CoreNLP supported languages with and get expected properties for each language by default.


Previous changes from v1.0.1

  • Overview

    🚀 This is a maintenance release of Stanza. It features new support for jieba as Chinese tokenizer, faster lemmatizer implementation, improved compatibility with CoreNLP v4.0.0, and many more!

    ✨ Enhancements

    👌 Supporting jieba library as Chinese tokenizer. The Stanza (simplified and traditional) Chinese pipelines now support using the jieba Chinese word segmentation library as tokenizer. Turn on this feature in a pipeline with: nlp = stanza.Pipeline('zh', processors={'tokenize': 'jieba'}, or by specifying argument tokenize_with_jieba=True.

    Setting resource directory with environment variable. You can now override the default model location $HOME/stanza_resources by setting an environmental variable STANZA_RESOURCES_DIR (#227). The new directory will then be used to store and look up model files. Thanks to @dhpollack for implementing this feature.

    Faster lemmatizer implementation. The lemmatizer implementation has been improved to be about 3x faster on CPU and 5x faster on GPU (#249). Thanks to @mahdiman for identifying the original issue.

    👌 Improved compatibility with CoreNLP 4.0.0. The client is now fully compatible with the latest v4.0.0 release of the CoreNLP package.

    🛠 Bugfixes

    Correct character offsets in NER outputs from pre-tokenized text. We fixed an issue where the NER outputs from pre-tokenized text may be off-by-one (#229). Thanks to @RyanElliott10 for reporting the issue.

    Correct Vietnamese tokenization on sentences beginning with punctuation. We fixed an issue where the Vietnamese tokenizer may throw an AssertionError on sentences that begin with a punctuation (#217). Thanks to @aryamccarthy for reporting this issue.

    Correct pytorch version requirement. Stanza is now asking for pytorch>=1.3.0 to avoid a runtime error raised by pytorch ((#231)). Thanks to @Vodkazy for reporting this.

    Known Model Issues & Solutions

    0️⃣ Default Korean Kaist tokenizer failing on punctuation. The default Korean Kaist model is reported to have issues with separating punctuations during tokenization (#276). Switching to the Korean GSD model may solve this issue.

    💅 Default Polish LFG POS tagger incorrectly labeling last word in sentence as PUNCT. The default Polish model trained on the LFG treebank may incorrectly tag the last word in a sentence as PUNCT (#220). This issue may be solved by switching to the Polish PDB model.