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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 176–200 of 351 papers

TitleStatusHype
TreeAnnotator: Versatile Visual Annotation of Hierarchical Text Relations—0
A Morphologically Annotated Corpus of Emirati Arabic—0
Moving TIGER beyond Sentence-Level—0
BioRo: The Biomedical Corpus for the Romanian Language—0
Parser combinators for Tigrinya and Oromo morphology—0
SentiArabic: A Sentiment Analyzer for Standard Arabic—0
Sudachi: a Japanese Tokenizer for BusinessCode0
Automatic Categorization of Tagalog Documents Using Support Vector Machines—0
Build Fast and Accurate Lemmatization for Arabic—0
Adapting the TTL Romanian POS Tagger to the Biomedical Domain—0
Evaluation of Finite State Morphological Analyzers Based on Paradigm Extraction from Wiktionary—0
Fast and Accurate Decision Trees for Natural Language Processing Tasks—0
Automatically Acquired Lexical Knowledge Improves Japanese Joint Morphological and Dependency Analysis—0
bleu2vec: the Painfully Familiar Metric on Continuous Vector Space Steroids—0
An Extensible Multilingual Open Source Lemmatizer—0
Lemmatization of Multi-word Common Noun Phrases and Named Entities in Polish—0
Impact of Feature Selection on Micro-Text Classification—0
KeyXtract Twitter Model - An Essential Keywords Extraction Model for Twitter Designed using NLP Tools—0
Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe—0
Lexical Correction of Polish Twitter Political Data—0
LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network—0
DT\_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output—0
ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification—0
RACAI's Natural Language Processing pipeline for Universal Dependencies—0
QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings—0
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