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Found 10 papers, 3 papers with code
Subword-based Cross-lingual Transfer of Embeddings from Hindi to Marathi and Nepali
Word embeddings are growing to be a crucial resource in the field of NLP for any language.
Constructing a Lexical Resource of Russian Derivational Morphology
Therefore, we devote this paper to improving one of the methods of constructing such resources and to the application of the method to a Russian lexicon, which results in the creation of the largest lexical resource of Russian derivational relations.
CorefUD 1.0: Coreference Meets Universal Dependencies
Recent advances in standardization for annotated language resources have led to successful large scale efforts, such as the Universal Dependencies (UD) project for multilingual syntactically annotated data.
Do UD Trees Match Mention Spans in Coreference Annotations?
One can find dozens of data resources for various languages in which coreference - a relation between two or more expressions that refer to the same real-world entity - is manually annotated.
Sentence Meaning Representations Across Languages: What Can We Learn from Existing Frameworks?
This article gives an overview of how sentence meaning is represented in eleven deep-syntactic frameworks, ranging from those based on linguistic theories elaborated for decades to rather lightweight NLP-motivated approaches.
Towards Universal Segmentations: UniSegments 1.0
Our work aims at developing a multilingual data resource for morphological segmentation.
Findings of the Shared Task on Multilingual Coreference Resolution
The public edition of CorefUD 1. 0, which contains 13 datasets for 10 languages, was used as the source of training and evaluation data.
The SIGMORPHON 2022 Shared Task on Morpheme Segmentation
The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections.
Ranked #8 on
Morpheme Segmentaiton
on UniMorph 4.0
Unsupervised Lemmatization as Embeddings-Based Word Clustering
We focus on the task of unsupervised lemmatization, i. e. grouping together inflected forms of one word under one label (a lemma) without the use of annotated training data.
