The Persian Dependency Treebank Made Universal (2022.lrec-1)

Copied to clipboard

Challenge: Existing universal dependency treebanks are lacking sufficient annotated data.
Approach: They propose a method for converting Persian Dependency Treebank to Universal Dependencies using an automatic method.
Outcome: The proposed method is more compatible with Universal Dependencies than the Uppsala Persian Universal Dependency Treebank.

Similar Papers

Informal Persian Universal Dependency Treebank (2022.lrec-1)

Copied to clipboard

Challenge: phonological, morphological, and syntactic distinctions between formal and informal Persian are important . formal Persian is not a universally recognized form of language, but is a dialect of informal Persian .
Approach: They develop an open-source treebank for informal Persian to train dependency parsers . they then train dependency lexicographers on existing treebanks and evaluate them on out-of-domain data .
Outcome: The proposed treebanks show that they perform poorly when training on formal and informal Persians.
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection (2020.lrec-1)

Copied to clipboard

Challenge: Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages.
Approach: They describe version 2 of the universal guidelines and discuss major changes from UD v1 to UD 2 . they propose a morphological layer, a syntactic layer and a word segmentation layer .
Outcome: The proposed treebanks are available for 90 languages and have been updated to meet the needs of multilingual parsers and researchers.
Development of a Multilingual CCG Treebank via Universal Dependencies Conversion (2022.lrec-1)

Copied to clipboard

Challenge: Combinatory Categorial Grammar (CCG) is a lexicalized grammar formalism that can capture both syntactic and semantic information.
Approach: They propose an algorithm to convert UD treebanks to CCG treebank and propose future extensions.
Outcome: The proposed algorithm performs lexical, sentential, and syntactic rule coverage analysis, as well as CCG parsing experiments.
Parser Training with Heterogeneous Treebanks (P18-2)

Copied to clipboard

Challenge: In the 2017 CoNLL Shared Task on Universal Dependency Parsing, 25 languages have more than one treebank . many teams did not take advantage of the multiple treebanks, however, and trained one model per treebank instead of one model for each language.
Approach: They propose a method to make the most of heterogeneous treebanks when training a monolingual parser.
Outcome: The proposed method improves on training with multiple treebanks for a single language.
A Universal Dependencies Treebank of Ancient Hebrew (2022.lrec-1)

Copied to clipboard

Challenge: Using a rule-based parser, we construct a treebank with morphological annotations of Ancient Hebrew . the Hebrew Scriptures are a collection of 39 books written in the first millennium BC in Ancient Hebrew.
Approach: They propose to use a Universal Dependencies treebank with morphological annotations of Ancient Hebrew for comparative study with ancient translations and analysis of Hebrew syntax.
Outcome: The proposed treebank can be used in comparative study with ancient translations and analysis of Hebrew syntax.
Treebank Embedding Vectors for Out-of-Domain Dependency Parsing (2020.acl-main)

Copied to clipboard

Challenge: a recent advance in monolingual dependency parsing is the idea of a treebank embedding vector . this allows the model to prefer training data from one treebank over another at test time .
Approach: They propose a method to predict a treebank vector for sentences that do not come from a particular treebank . they also explore what happens when they move away from predefined treebank embedding vectors .
Outcome: The proposed method can predict treebank vectors for sentences that do not come from a treebank used in training with sufficient accuracy for nine out of ten languages.
Some Languages Seem Easier to Parse Because Their Treebanks Leak (2020.emnlp-main)

Copied to clipboard

Challenge: Cross-language differences in (universal) dependency parsing performance are mostly attributed to treebank size, average sentence length, average dependency length, morphological complexity, and domain differences.
Approach: They compute graph isomorphisms and find that treebank size is a factor that influences parsing performance.
Outcome: The results show that the overlap between training and test graphs explain more of the observed variation than standard explanations such as the above.
Dependency Parsing for Urdu: Resources, Conversions and Learning (2020.lrec-1)

Copied to clipboard

Challenge: Existing treebanks for Urdu are under-resourced due to lack of resources.
Approach: They propose to convert existing treebanks into a common format that is based on Universal Dependencies.
Outcome: The proposed format outperforms the MaltParser and a transition-based BiLSTM parser with word embeddings and significantly improves parsing accuracy.
Universal Dependencies for Punjabi (2022.lrec-1)

Copied to clipboard

Challenge: UD is a community project that maintains a standard scheme for the annotation of grammar in a cross-lingually consistent manner.
Approach: They propose a Universal Dependencies treebank for Punjabi written in the Gurmukhi script and discuss corpus design and linguistic phenomena encountered in annotation.
Outcome: The proposed treebank covers a variety of genres and has been annotated for POS tags, dependency relations, and graph-based Enhanced Dependencies.
Constructing a Dependency Treebank for Second Language Learners of Korean (2024.lrec-main)

Copied to clipboard

Challenge: a manually annotated syntactic treebank is available for second language learners . the dataset includes 7,530 sentences (66,982 words; 129,333 morphemes)
Approach: They propose to manually annotate syntactic treebanks based on Universal Dependencies from Korean written data.
Outcome: The proposed dataset includes 7,530 sentences and 129,333 morphemes from Korean learners.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations