natural-language-processing-4
Lemmatization
3260 papers • 126 benchmarks • 313 datasets
Lemmatization is a process of determining a base or dictionary form (lemma) for a given surface form. Especially for languages with rich morphology it is important to be able to normalize words into their base forms to better support for example search engines and linguistic studies. Main difficulties in Lemmatization arise from encountering previously unseen words during inference time as well as disambiguating ambiguous surface forms which can be inflected variants of several different base forms depending on the context. Source: Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks
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Most implemented papers
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
Christopher D. Manning, Yuhui Zhang, Yuhao Zhang, Peng Qi, Jason Bolton•Sun Mar 15 2020
This work introduces Stanza, an open-source Python natural language processing toolkit supporting 66 human languages that 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.