SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–250 of 351 papers

TitleStatusHype
Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention—0
Context Sensitive Lemmatization Using Two Successive Bidirectional Gated Recurrent Networks—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
Services for text simplification and analysis—0
Multilingwis ^2 -- Explore Your Parallel Corpus—0
Exploring Properties of Intralingual and Interlingual Association Measures Visually—0
An Automated Text Categorization Framework based on Hyperparameter OptimizationCode0
Gender Profiling for Slovene Twitter communication: the Influence of Gender Marking, Content and Style—0
A data-driven approach to verbal multiword expression detection. PARSEME Shared Task system description paper—0
The First Cross-Lingual Challenge on Recognition, Normalization, and Matching of Named Entities in Slavic Languages—0
Adapting a State-of-the-Art Tagger for South Slavic Languages to Non-Standard Text—0
Spelling Correction for Morphologically Rich Language: a Case Study of Russian—0
Distributional regularities of verbs and verbal adjectives: Treebank evidence and broader implications—0
Acquisition of semantic relations between terms: how far can we get with standard NLP tools?—0
YAMAMA: Yet Another Multi-Dialect Arabic Morphological Analyzer—0
Improving the Morphological Analysis of Classical Sanskrit—0
Improving Neural Translation Models with Linguistic Factors—0
The Power of Language Music: Arabic Lemmatization through Patterns—0
ENIAM: Categorial Syntactic-Semantic Parser for Polish—0
The impact of simple feature engineering in multilingual medical NER—0
Automatic Translation of English Text to Indian Sign Language Synthetic Animations—0
LAMB: A Good Shepherd of Morphologically Rich Languages—0
Towards error annotation in a learner corpus of Portuguese—0
Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks—0
Authorship Attribution Based on Life-Like Network Automata—0
An Analysis of Lemmatization on Topic Models of Morphologically Rich Language—0
An NLP Pipeline for Coptic—0
NRC Russian-English Machine Translation System for WMT 2016—0
Leveraging Inflection Tables for Stemming and Lemmatization.—0
Morphological Reinflection via Discriminative String Transduction—0
Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time—0
English-French Document Alignment Based on Keywords and Statistical Translation—0
Dealing with word-internal modification and spelling variation in data-driven lemmatization—0
UdS-(retrain|distributional|surface): Improving POS Tagging for OOV Words in German CMC and Web Data—0
SoMaJo: State-of-the-art tokenization for German web and social media textsCode0
Using longest common subsequence and character models to predict word forms—0
The Kyoto University Cross-Lingual Pronoun Translation System—0
The GW/UMD CLPsych 2016 Shared Task System—0
Leveraging Data-Driven Methods in Word-Level Language Identification for a Multilingual Alpine Heritage Corpus—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
Unsupervised Compound Splitting With Distributional Semantics Rivals Supervised MethodsCode0
Weighting Finite-State Transductions With Neural Context—0
HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity—0
The IPR-cleared Corpus of Contemporary Written and Spoken Romanian Language—0
Merging Data Resources for Inflectional and Derivational Morphology in Czech—0
Analyzing Pre-processing Settings for Urdu Single-document Extractive SummarizationCode0
Rule-based Automatic Multi-word Term Extraction and Lemmatization—0
Urdu Summary CorpusCode0
The COPLE2 corpus: a learner corpus for Portuguese—0
Show:102550
← PrevPage 5 of 8Next →

No leaderboard results yet.