3260 papers • 126 benchmarks • 313 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. Source: Towards Finite-State Morphology of Kurdish
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A deep neural network model for galaxy morphology classification which exploits translational and rotational symmetry is developed in the context of the Galaxy Challenge, an international competition to build the best model for morphology classification based on annotated images from the Galaxy Zoo project.
This work introduces Morse, a recurrent encoder-decoder model that produces morphological analyses of each word in a sentence and shows that generating morphological features individually rather than as a combined tag allows the model to handle rare or unseen tags and outperform whole-tag models.
DEEMD identifies known SARS-CoV-2 inhibitors, such as Remdesivir and Aloxistatin, supporting the validity of the approach, and can be explored for use on other emerging viruses and datasets to rapidly identify candidate antiviral treatments in the future.
A method for weighting a morphological analyzer built using finite state transducers in order to disambiguate its results using a word2vec model that is trained in a completely unsupervised way using raw untagged corpora and is able to capture the semantic meaning of the words.
This work asks how to build a rule-based system that can reason with natural language input but without the manual construction of rules, and proposes MetaQNL, a "Quasi-Natural" language that can express both formal logic and natural language sentences, and MetaInduce, a learning algorithm that inducesMetaQNL rules from training data, with or without intermediate reasoning steps.
A large-scale application of the memory-based approach to part of speech tagging is shown to be feasible, obtaining a tagging accuracy that is on a par with that of known statistical approaches, and with attractive space and time complexity properties when using IGTree, a tree-based formalism for indexing and searching huge case bases.
An evaluation is presented which shows that the transducer has medium-level coverage, between 82% and 87% on two freely available corpora of Kyrgyz, and high precision and recall over a manually verified test set.
The effect of POS tagging and morphological analysis on parsing performance is discussed, and novel ways of improving performance of the components are presented, including the use of morphological features for POS-tagging, and using syntactic information to select good POS sequences from an n-best list.
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