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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 201–250 of 351 papers

TitleStatusHype
ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification—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
Exploring Properties of Intralingual and Interlingual Association Measures Visually—0
Services for text simplification and analysis—0
Multilingwis ^2 -- Explore Your Parallel Corpus—0
An Automated Text Categorization Framework based on Hyperparameter OptimizationCode0
Adapting a State-of-the-Art Tagger for South Slavic Languages to Non-Standard Text—0
The First Cross-Lingual Challenge on Recognition, Normalization, and Matching of Named Entities in Slavic Languages—0
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
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
Improving Neural Translation Models with Linguistic Factors—0
ENIAM: Categorial Syntactic-Semantic Parser for Polish—0
YAMAMA: Yet Another Multi-Dialect Arabic Morphological Analyzer—0
Automatic Translation of English Text to Indian Sign Language Synthetic Animations—0
Acquisition of semantic relations between terms: how far can we get with standard NLP tools?—0
The Power of Language Music: Arabic Lemmatization through Patterns—0
The impact of simple feature engineering in multilingual medical NER—0
Improving the Morphological Analysis of Classical Sanskrit—0
Towards error annotation in a learner corpus of Portuguese—0
LAMB: A Good Shepherd of Morphologically Rich Languages—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
Morphological Reinflection via Discriminative String Transduction—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
NRC Russian-English Machine Translation System for WMT 2016—0
An NLP Pipeline for Coptic—0
Using longest common subsequence and character models to predict word forms—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
The Kyoto University Cross-Lingual Pronoun Translation System—0
Leveraging Inflection Tables for Stemming and Lemmatization.—0
Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time—0
Leveraging Data-Driven Methods in Word-Level Language Identification for a Multilingual Alpine Heritage Corpus—0
The GW/UMD CLPsych 2016 Shared Task System—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity—0
Weighting Finite-State Transductions With Neural Context—0
Unsupervised Compound Splitting With Distributional Semantics Rivals Supervised MethodsCode0
Urdu Summary CorpusCode0
UDPipe: Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization, Morphological Analysis, POS Tagging and Parsing—0
Analyzing Pre-processing Settings for Urdu Single-document Extractive SummarizationCode0
FOLK-Gold ― A Gold Standard for Part-of-Speech-Tagging of Spoken German—0
CEPLEXicon ― A Lexicon of Child European Portuguese—0
Lemmatization and Morphological Tagging in German and Latin: A Comparison and a Survey of the State-of-the-art—0
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