Challenge: Animacy is a feature found in nouns such as 'gender', 'number' and 'case' that improves parser accuracy.
Approach: They propose an annotation scheme and parser results for the animacy feature in Russian and Arabic, morphologically rich languages, using the universal dependency framework.
Outcome: The proposed scheme and parser improve on the animacy feature in Russian and Arabic, and the results show that the feature is more accurate than other features found in nouns, namely, 'gender', , and 'number'

Similar Papers

A Framework for Understanding the Role of Morphology in Universal Dependency Parsing (D18-1)

Copied to clipboard

Challenge: a measure of morphological complexity is used to characterize syntactic information in word embeddings.
Approach: They propose a measure of morphological complexity in terms of governor-dependent preferential attachment that explains parsing performance.
Outcome: The proposed framework improves parsing performance on morphologically rich languages using morphology as a syntactic marker.
The Cross-linguistic Role of Animacy in Grammar Structures (2025.acl-long)

Copied to clipboard

Challenge: Animacy is a semantic feature of nominals and follows a hierarchy of personal pronouns . it is argued that soft tendencies may be the cause of animacy constraints . however, there is no empirical evidence for this .
Approach: They propose a method to reliably classify animacy classes of nominals in 11 languages from 5 families using multilingual large language models and word sense disambiguation datasets.
Outcome: The proposed method shows that animacy displays consistent cross-linguistic tendencies in terms of preferred morphosyntactic constructions, but not always in line with received wisdom.
Animacy Denoting German Nouns: Annotation and Classification (2022.lrec-1)

Copied to clipboard

Challenge: Animacy detection is meant to distinguish words which denote humans from words used to denote non-humans.
Approach: They propose a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities.
Outcome: The proposed gold standard comprises almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities.
A New Approach to Animacy Detection (C18-1)

Copied to clipboard

Challenge: Animacy is a property for a referent to be an agent, and prior work has classified words as either animate or inanimate.
Approach: They propose a method that uses supervised machine learning and hand-built rules to classify the animacy of co-reference chains.
Outcome: The proposed method achieves state-of-the-art performance on a 142-text dataset . it leverages word embeddings over referring expressions, parts of speech, and grammatical and semantic roles .
Automatic Animacy Classification for Romanian Nouns (2024.lrec-main)

Copied to clipboard

Challenge: Animacy is a semantic property of nouns that describes the quality of the noun's referent of being alive, sentient or volitional.
Approach: They propose a type-based binary classifier of Romanian nouns into the classes human/non-human using pre-trained word embeddings and animacy information derived from Romanian WordNet.
Outcome: The proposed classifiers perform well on the Romanian language and in a naturalistic setting.
When Language Models Fall in Love: Animacy Processing in Transformer Language Models (2023.emnlp-main)

Copied to clipboard

Challenge: Animacy is not always expressed directly in language, but it manifests indirectly in English . atypically animate entities are easier to remember and prioritized in visual processing .
Approach: They find that LMs behave much like humans when presented with entities whose animacy is typical.
Outcome: The proposed model can learn about animacy even when presented with atypically animate entities.
CoNLL-UL: Universal Morphological Lattices for Universal Dependency Parsing (L18-1)

Copied to clipboard

Challenge: Using the universal dependencies framework, we address the need for a universal representation of morphological analysis that can capture alternative morphology of surface tokens and is compatible with the segmentation and morphologic annotation guidelines prescribed for UD treebanks.
Approach: They propose a new annotation format for word lattices that represent morphological analyses and a resource that obeys this format for a range of typologically different languages.
Outcome: The proposed model can capture alternative morphological analyses of surface tokens and is compatible with the segmentation and morphology guidelines prescribed for UD treebanks.
Evaluating Morphological Plausibility of Subword Tokenization via Statistical Alignment with Morpho-Syntactic Features (2026.findings-eacl)

Copied to clipboard

Challenge: Existing metric for subword tokenization evaluation for morphological plausibility requires unavailable or inconsistent gold segmentation data.
Approach: They propose a morpho-syntactic feature-based metric for subword tokenization evaluation.
Outcome: The proposed metric correlates well with traditional morpheme boundary recall while being more broadly applicable across languages with different morphological systems.
Typological Features for Multilingual Delexicalised Dependency Parsing (N19-1)

Copied to clipboard

Challenge: Existing universal models to describe the syntax of languages are debated for decades . a new study examines the plausibility of universal grammars in dependency parsing .
Approach: They propose to use typological features to describe the syntax of languages to train a multilingual dependency parser.
Outcome: The proposed model can be trained on 40 languages with the help of typological features.
Syntactic Nuclei in Dependency Parsing – A Multilingual Exploration (2021.eacl-main)

Copied to clipboard

Challenge: Existing models for syntactic dependency parsing assume words are elementary units that enter into dependency relations.
Approach: They propose to use composition functions to make a transition-based dependency parser aware of the notion of nucleus.
Outcome: The proposed concept of nucleus gives small but significant improvements in parsing accuracy on 12 languages.

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