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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

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Showing 276–300 of 351 papers

TitleStatusHype
SAMAR: A System for Subjectivity and Sentiment Analysis of Arabic Social Media—0
SentiArabic: A Sentiment Analyzer for Standard Arabic—0
Services for text simplification and analysis—0
Sharing Cultural Heritage: the Clavius on the Web Project—0
Sigmorphon 2019 Task 2 system description paper: Morphological analysis in context for many languages, with supervision from only a few—0
Simultaneous Word-Morpheme Alignment for Statistical Machine Translation—0
SinaTools: Open Source Toolkit for Arabic Natural Language Processing—0
Social Media Personal Event Notifier Using NLP and Machine Learning—0
Spelling Correction for Morphologically Rich Language: a Case Study of Russian—0
SSA-UO: Unsupervised Sentiment Analysis in Twitter—0
Statistical Parsing of Spanish and Data Driven Lemmatization—0
Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks—0
Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset—0
SU-RUG at the CoNLL-SIGMORPHON 2017 shared task: Morphological Inflection with Attentional Sequence-to-Sequence Models—0
Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity—0
TArC: Tunisian Arabish Corpus First complete release—0
TArC: Tunisian Arabish Corpus, First complete release—0
TartuNLP @ SIGTYP 2024 Shared Task: Adapting XLM-RoBERTa for Ancient and Historical Languages—0
The 2021 Urdu Fake News Detection Task using Supervised Machine Learning and Feature Combinations—0
The annotation of the C-ORAL-BRASIL oral through the implementation of the Palavras Parser—0
The CMU Machine Translation Systems at WMT 2014—0
The COPLE2 corpus: a learner corpus for Portuguese—0
The effect of stemming and lemmatization on Portuguese fake news text classification—0
The Effects of Syntactic Features in Automatic Prediction of Morphology—0
The First Cross-Lingual Challenge on Recognition, Normalization, and Matching of Named Entities in Slavic Languages—0
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