Fine-Grained Modeling of Noun Phrases' Genericity in Natural Language

Kernkonzepte

Modeling Noun Phrases' genericity through a novel annotation framework grounded in linguistic theory and cognitive literature.

Zusammenfassung

Introduction

Language conveys information about individuals and kinds.
Disambiguating between particular and general meanings.

Existing Annotation Frameworks

Multi-class vs. continuous systems for capturing genericity.
Limitations of discrete classifications in modeling genericity nuances.

Annotation Framework

Dual dimensions of inclusiveness and abstractness for fine-grained modeling.
Use of continuous scales for nuanced semantic properties.

Pilot Study

Validation through comparison with existing binary annotations.
Reliability analysis using Intraclass Correlation Coefficient (ICC).

Analysis

Quantitative comparison showing significant differences between GENERIC and NON-GENERIC groups.

Qualitative Comparison

Distribution of INC and ABS ratings compared to binary labels.

Inherent Semantics of Words

Influence on abstractness ratings, especially for concrete vs. abstract nouns.

Statistiken

Through our annotation framework, we propose to model genericity through two different semantic dimensions; continuous evaluations.

Zitate

"Generics allow for exceptions, enabling interpretation based on world knowledge."
"Continuous scales offer advantages over discrete rating systems."

Wichtige Erkenntnisse aus

Specifying Genericity through Inclusiveness and Abstractness Continuous Scales

by Claudia Coll... um arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15278.pdf

Tiefere Fragen

How can the proposed annotation framework be applied to other languages or domains?

The proposed annotation framework for capturing nuances of noun phrases' genericity through continuous scales can be adapted and applied to various languages and domains. To apply this framework to other languages, one would need to translate the dataset of sentences into the target language while ensuring that the semantic nuances related to genericity are preserved. Additionally, annotators proficient in the target language could evaluate inclusiveness and abstractness using continuous sliders similar to those used in the pilot study. This approach allows for a cross-linguistic comparison of genericity phenomena.
In terms of application across different domains, the framework's flexibility lies in its ability to capture fine-grained distinctions in how nouns are perceived as generic or non-generic within specific contexts. By adjusting the dataset with domain-specific vocabulary and concepts, researchers can use this framework to analyze genericity patterns in specialized fields such as medicine, law, or technology. The continuous nature of annotations enables a more nuanced understanding of how different types of entities are conceptualized generically across diverse subject areas.

How might the concept of genericity impact machine learning models beyond NLP applications?

The concept of genericity plays a crucial role in shaping machine learning models beyond just Natural Language Processing (NLP) applications. Understanding how nouns exhibit varying degrees of inclusiveness and abstractness can enhance model performance in tasks requiring commonsense reasoning, knowledge representation, and decision-making processes.

Knowledge Graph Construction: Generic statements often convey general truths about categories or kinds. By incorporating information about generics into knowledge graphs, machine learning models can better represent relationships between entities based on shared properties rather than specific instances.

Commonsense Reasoning: Models trained on data annotated with nuanced levels of genericity can improve their ability to make logical deductions and infer missing information based on general principles associated with different kinds or categories.

Semantic Search & Information Retrieval: Leveraging insights from annotated datasets focused on generics allows search engines and recommendation systems to provide more contextually relevant results by understanding user queries that involve generalized statements about entities.

Ethical AI Development: Considering generics helps AI systems avoid biased assumptions by recognizing when certain attributes apply universally versus being specific cases limited by individual examples.

What are the potential drawbacks or limitations of relying on crowd workers for annotations?

While utilizing crowd workers for annotations offers scalability benefits and cost-effectiveness compared to expert annotators, several drawbacks should be considered:

Quality Control: Crowd workers may vary significantly in their linguistic proficiency and interpretation skills, leading to inconsistencies or inaccuracies in annotations.

Subjectivity Bias: Individual annotators may have personal biases that influence their evaluations regarding inclusiveness and abstractness levels.

3 .Lack Of Expertise: Crowd workers may lack domain-specific knowledge required for accurate labeling within specialized topics or industries.
4 .Annotation Variability: Differences among crowd workers' interpretations could result in noisy data that requires additional processing steps for normalization.
5 .Training Requirements: Training large numbers of crowd workers necessitates time investment upfront before obtaining reliable annotations.