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
DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew
We present DictaBERT, a new state-of-the-art pre-trained BERT model for modern Hebrew, outperforming existing models on most benchmarks.
LatinCy: Synthetic Trained Pipelines for Latin NLP
This paper introduces LatinCy, a set of trained general purpose Latin-language "core" pipelines for use with the spaCy natural language processing framework.
Post-hoc analysis of Arabic transformer models
Given the success of pre-trained language models, many transformer models trained on Arabic and its dialects have surfaced.
Benchmarking zero-shot and few-shot approaches for tokenization, tagging, and dependency parsing of Tagalog text
The grammatical analysis of texts in any written language typically involves a number of basic processing tasks, such as tokenization, morphological tagging, and dependency parsing.
Interpreting Arabic Transformer Models
Arabic is a Semitic language which is widely spoken with many dialects.
On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions
However, it remains unclear in which situations these dataset embeddings are most effective, because they are used in a large variety of settings, languages and tasks.
EstBERT: A Pretrained Language-Specific BERT for Estonian
This paper presents EstBERT, a large pretrained transformer-based language-specific BERT model for Estonian.
Evaluating Multilingual BERT for Estonian
Recently, large pre-trained language models, such as BERT, have reached state-of-the-art performance in many natural language processing tasks, but for many languages, including Estonian, BERT models are not yet available.
Morphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods
In this paper we present the first full morphological analysis and disambiguation system for Gulf Arabic.
Reproducing a Morphosyntactic Tagger with a Meta-BiLSTM Model over Context Sensitive Token Encodings
Furthermore, even where we improve on earlier models, we fail to match the F1-scores reported for the meta-BiLSTM model.
