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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
Universal Morphologies for the Caucasus region—0
BioRo: The Biomedical Corpus for the Romanian Language—0
Coreference Resolution in FreeLing 4.0—0
Very Large-Scale Lexical Resources to Enhance Chinese and Japanese Machine Translation—0
SoMeWeTa: A Part-of-Speech Tagger for German Social Media and Web TextsCode0
SentiArabic: A Sentiment Analyzer for Standard Arabic—0
TreeAnnotator: Versatile Visual Annotation of Hierarchical Text Relations—0
Automatic Categorization of Tagalog Documents Using Support Vector Machines—0
Build Fast and Accurate Lemmatization for Arabic—0
Fast and Accurate Decision Trees for Natural Language Processing Tasks—0
Lemmatization of Multi-word Common Noun Phrases and Named Entities in Polish—0
An Extensible Multilingual Open Source Lemmatizer—0
Adapting the TTL Romanian POS Tagger to the Biomedical Domain—0
Automatically Acquired Lexical Knowledge Improves Japanese Joint Morphological and Dependency Analysis—0
bleu2vec: the Painfully Familiar Metric on Continuous Vector Space Steroids—0
Evaluation of Finite State Morphological Analyzers Based on Paradigm Extraction from Wiktionary—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
Lexical Correction of Polish Twitter Political Data—0
Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe—0
RACAI's Natural Language Processing pipeline for Universal Dependencies—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
QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings—0
Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention—0
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