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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 191200 of 351 papers

TitleStatusHype
Evaluation of Finite State Morphological Analyzers Based on Paradigm Extraction from Wiktionary0
Impact of Feature Selection on Micro-Text Classification0
KeyXtract Twitter Model - An Essential Keywords Extraction Model for Twitter Designed using NLP Tools0
Lexical Correction of Polish Twitter Political Data0
Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe0
RACAI's Natural Language Processing pipeline for Universal Dependencies0
LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network0
DT\_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output0
QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings0
Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention0
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