https://github.com/ufal/mrpipe-conll2019
공식 코드ÚFAL MRPipe at MRP 2019: UDPipe Goes Semantic in the Meaning Representation Parsing Shared Task
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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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
ÚFAL MRPipe at MRP 2019: UDPipe Goes Semantic in the Meaning Representation Parsing Shared Task
Resource-Size matters: Improving Neural Named Entity Recognition with Optimized Large Corpora
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
Beyond Stemming and Lemmatization: Ultra-stemming to Improve Automatic Text Summarization
Integrated Sequence Tagging for Medieval Latin Using Deep Representation Learning
LemmaTag: Jointly Tagging and Lemmatizing for Morphologically-Rich Languages with BRNNs
Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines
Unsupervised Lemmatization as Embeddings-Based Word Clustering
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.