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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 151–200 of 351 papers

TitleStatusHype
Attention-free encoder decoder for morphological processing—0
NLP-Cube: End-to-End Raw Text Processing With Neural NetworksCode0
UZH@SMM4H: System Descriptions—0
Building a Lemmatizer and a Spell-checker for Sorani Kurdish—0
Towards JointUD: Part-of-speech Tagging and Lemmatization using Recurrent Neural NetworksCode0
Imitation Learning for Neural Morphological String TransductionCode0
LemmaTag: Jointly Tagging and Lemmatizing for Morphologically-Rich Languages with BRNNsCode0
Local String Transduction as Sequence Labeling—0
An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing—0
From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical ResourcesCode0
Neural Transition-based String Transduction for Limited-Resource Setting in MorphologyCode0
Resource-Size matters: Improving Neural Named Entity Recognition with Optimized Large CorporaCode0
Character-level Supervision for Low-resource POS Tagging—0
Fast Query Expansion on an Accounting Corpus using Sub-Word Embeddings—0
IUCM at SemEval-2018 Task 11: Similar-Topic Texts as a Comprehension Knowledge SourceCode0
Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification—0
Context Sensitive Neural Lemmatization with Lematus—0
Robustness of sentence length measures in written texts—0
The Use of Text Alignment in Semi-Automatic Error Analysis: Use Case in the Development of the Corpus of the Latvian Language Learners—0
SoMeWeTa: A Part-of-Speech Tagger for German Social Media and Web TextsCode0
Developing New Linguistic Resources and Tools for the Galician Language—0
Very Large-Scale Lexical Resources to Enhance Chinese and Japanese Machine Translation—0
Universal Morphologies for the Caucasus region—0
Generating a Gold Standard for a Swedish Sentiment Lexicon—0
Coreference Resolution in FreeLing 4.0—0
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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