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Lemmatization

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

Papers

Showing 201225 of 351 papers

TitleStatusHype
RACAI's Natural Language Processing pipeline for Universal Dependencies0
Realignment from Finer-grained Alignment to Coarser-grained Alignment to Enhance Mongolian-Chinese SMT0
Recent advancements in computational morphology : A comprehensive survey0
Recycling and Comparing Morphological Annotation Models for Armenian Diachronic-Variational Corpus Processing0
Robustness of sentence length measures in written texts0
ROMBAC: The Romanian Balanced Annotated Corpus0
Rule-based Automatic Multi-word Term Extraction and Lemmatization0
SAMAR: A System for Subjectivity and Sentiment Analysis of Arabic Social Media0
SentiArabic: A Sentiment Analyzer for Standard Arabic0
Services for text simplification and analysis0
Sharing Cultural Heritage: the Clavius on the Web Project0
Sigmorphon 2019 Task 2 system description paper: Morphological analysis in context for many languages, with supervision from only a few0
Simultaneous Word-Morpheme Alignment for Statistical Machine Translation0
SinaTools: Open Source Toolkit for Arabic Natural Language Processing0
Social Media Personal Event Notifier Using NLP and Machine Learning0
Spelling Correction for Morphologically Rich Language: a Case Study of Russian0
SSA-UO: Unsupervised Sentiment Analysis in Twitter0
Statistical Parsing of Spanish and Data Driven Lemmatization0
Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks0
Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset0
SU-RUG at the CoNLL-SIGMORPHON 2017 shared task: Morphological Inflection with Attentional Sequence-to-Sequence Models0
Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity0
TArC: Tunisian Arabish Corpus First complete release0
TArC: Tunisian Arabish Corpus, First complete release0
TartuNLP @ SIGTYP 2024 Shared Task: Adapting XLM-RoBERTa for Ancient and Historical Languages0
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