Morphological Analysis
62 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
Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks
We introduce a constituency parser based on a bi-LSTM encoder adapted from recent work (Cross and Huang, 2016b; Kiperwasser and Goldberg, 2016), which can incorporate a lower level character biLSTM (Ballesteros et al., 2015; Plank et al., 2016).
Building a Word Segmenter for Sanskrit Overnight
There is an abundance of digitised texts available in Sanskrit.
Deep Affix Features Improve Neural Named Entity Recognizers
We propose a practical model for named entity recognition (NER) that combines word and character-level information with a specific learned representation of the prefixes and suffixes of the word.
Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings
Experiments on word similarity, syntactic analogy and text classification are conducted to validate the feasibility of our models.
A Characterwise Windowed Approach to Hebrew Morphological Segmentation
This paper presents a novel approach to the segmentation of orthographic word forms in contemporary Hebrew, focusing purely on splitting without carrying out morphological analysis or disambiguation.
Tree-Stack LSTM in Transition Based Dependency Parsing
We introduce tree-stack LSTM to model state of a transition based parser with recurrent neural networks.
