Morphological Analysis
58 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.
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.}
Development of a Hindi Lemmatizer
We live in a translingual society, in order to communicate with people from different parts of the world we need to have an expertise in their respective languages.
