Morphological Tagging
23 papers with code • 0 benchmarks • 4 datasets
Morphological tagging is the task of assigning labels to a sequence of tokens that describe them morphologically. As compared to Part-of-speech tagging, morphological tagging also considers morphological features, such as case, gender or the tense of verbs.
Benchmarks
These leaderboards are used to track progress in Morphological Tagging
Datasets
CoNLL
Latest papers with no code
Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling
In this paper we explore the use of multitask learning and adversarial training to address morphological richness and dialectal variations in the context of full morphological tagging.
Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging
Semitic languages can be highly ambiguous, having several interpretations of the same surface forms, and morphologically rich, having many morphemes that realize several morphological features.
On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages
Recent work has validated the importance of subword information for word representation learning.
Multi-Team: A Multi-attention, Multi-decoder Approach to Morphological Analysis.
This paper describes our submission to SIGMORPHON 2019 Task 2: Morphological analysis and lemmatization in context.
Sigmorphon 2019 Task 2 system description paper: Morphological analysis in context for many languages, with supervision from only a few
While our system results are dramatically below the average system submitted for the shared task evaluation campaign, our method is (we suspect) unique in its minimal reliance on labeled training data.
A Simple Joint Model for Improved Contextual Neural Lemmatization
English verbs have multiple forms.
`Indicatements' that character language models learn English morpho-syntactic units and regularities
Character language models have access to surface morphological patterns, but it is not clear whether or \textit{how} they learn abstract morphological regularities.
Neural Morphological Tagging for Estonian
Secondly, we complement these models with the analyses generated by a rule-based Estonian morphological analyser (MA) VABAMORF , thus performing a soft morphological disambiguation.
IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing
This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies.
Turku Neural Parser Pipeline: An End-to-End System for the CoNLL 2018 Shared Task
In this paper we describe the TurkuNLP entry at the CoNLL 2018 Shared Task on Multilingual Parsing from Raw Text to Universal Dependencies.
