Morphological Analysis
71 papers with code • 0 benchmarks • 5 datasets
Morphological Analysis is a central task in language processing that can take a word as input and detect the various morphological entities in the word and provide a morphological representation of it.
Benchmarks
These leaderboards are used to track progress in Morphological Analysis
CELEX
Polyglot-NER
Most implemented papers
Rotation-invariant convolutional neural networks for galaxy morphology prediction
Unfortunately, even this approach does not scale well enough to keep up with the increasing availability of galaxy images.
Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Scarcity
In this paper we describe an end to end Neural Model for Named Entity Recognition NER) which is based on Bi-Directional RNN-LSTM.
Morphological analysis using a sequence decoder
Our Morse implementation and the TrMor2018 dataset are available online to support future research\footnote{See \url{https://github. com/ai-ku/Morse. jl} for a Morse implementation in Julia/Knet \cite{knet2016mlsys} and \url{https://github. com/ai-ku/TrMor2018} for the new Turkish dataset.
An Unsupervised Method for Weighting Finite-state Morphological Analyzers
In this paper, we have developed a method for weighting a morphological analyzer built using finite state transducers in order to disambiguate its results.
DEEMD: Drug Efficacy Estimation against SARS-CoV-2 based on cell Morphology with Deep multiple instance learning
DEEMD can be explored for use on other emerging viruses and datasets to rapidly identify candidate antiviral treatments in the future}.
Learning Symbolic Rules for Reasoning in Quasi-Natural Language
In this work, we ask how we can build a rule-based system that can reason with natural language input but without the manual construction of rules.
Neuro-evolutionary evidence for a universal fractal primate brain shape
To demonstrate the importance of this new understanding, we show a scale-dependent effect of ageing on brain morphology.
Lexically Grounded Subword Segmentation
Third, we introduce an efficient segmentation algorithm based on a subword bigram model that can be initialized with the lexically aware segmentation method to avoid using Morfessor and large embedding tables at inference time.
POS-tagging to highlight the skeletal structure of sentences
This study presents the development of a part-of-speech (POS) tagging model to extract the skeletal structure of sentences using transfer learning with the BERT architecture for token classification.
MBT: A Memory-Based Part of Speech Tagger-Generator
In this paper we show that a large-scale application of the memory-based approach is feasible: we obtain a tagging accuracy that is on a par with that of known statistical approaches, and with attractive space and time complexity properties when using {\em IGTree}, a tree-based formalism for indexing and searching huge case bases.}
