Feature2Vec: Distributional semantic modelling of human property knowledge (D19-1)
Copied to clipboard
| Challenge: | Existing distributional semantic models of word meaning are limited in size due to constraints associated with exhaustively listing properties for large numbers of words. |
| Approach: | They propose a method for mapping human property knowledge onto a distributional semantic space and adapt it to the task of modelling concept features. |
| Outcome: | The proposed model performs better on evaluation tasks and improves on other evaluation tasks. |
Similar Papers
Why is penguin more similar to polar bear than to sea gull? Analyzing conceptual knowledge in distributional models (2020.acl-srw)
Copied to clipboard
| Challenge: | Several analysis methods have been shown to be limited and are not well understood . thesis aims to understand distributional semantic representations based on linguistic data . |
| Approach: | They propose a framework for investigating the information encoded in distributional semantic models . they combine observations made on corpora with insights obtained from data manipulation experiments . |
| Outcome: | The proposed framework pairs observations made on corpora with insights obtained from data manipulation experiments. |
Semantic Specialization of Distributional Word Vectors (D19-2)
Copied to clipboard
| Challenge: | Distributional word vectors conflate various paradigmatic and syntagmatic lexico-semantic relations. |
| Approach: | This tutorial provides an overview of specialization methods for distributional word vectors . a common solution is to include external lexico-semantic knowledge in a reshaped vector space . |
| Outcome: | This paper provides an overview of specialization methods for distributional word vectors . the most recent developments include a new method for asymmetric relations in Euclidean . |
Aff2Vec: Affect–Enriched Distributional Word Representations (C18-1)
Copied to clipboard
| Challenge: | Affective word distributions are not well understood in literature. |
| Approach: | They propose a model that embeds affective word interpretations into enriched word embeddings. |
| Outcome: | The proposed model outperforms the state-of-the-art in word-similarity tasks and in emotion analysis, personality detection, and frustration prediction tasks. |
Investigating Word-Class Distributions in Word Vector Spaces (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies have been successful in representing the meaning of a word with a vector in a continuous vector space, but little attention has been paid to the distribution of words belonging to a certain word class in . word vector spaces are useful for a range of natural language processing tasks, including selectional preference acquisition and entity set expansion. |
| Approach: | They investigated the distribution of word vectors belonging to a certain word class in a pre-trained word vector space and compared their models to validate their assumptions. |
| Outcome: | The proposed model fails to estimate how likely a word in the vector space is a member of a given word class, and the geometry of the distribution and existence of subgroups will have limited impact. |
Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings (2022.acl-long)
Copied to clipboard
Shib Dasgupta, Michael Boratko, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Li, Andrew McCallum
| Challenge: | Word2Box provides a set-theoretic training objective for learning word representations . word representation is not natural, all senses and contexts, levels of abstraction, variants and modifications which the word may represent are forced to be captured by mat t is nunc. |
| Approach: | They propose a fuzzy-set interpretation of box embeddings and learn box representations of words using a set-theoretic training objective. |
| Outcome: | The proposed model improves word similarity tasks on less common words. |
A Distributional Perspective on Word Learning in Neural Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Language models are increasingly being studied as models of human language learners. |
| Approach: | They propose a distributional approach to word learning that captures distributional knowledge and gradient preferences for the word’s appropriateness. |
| Outcome: | The proposed signatures capture knowledge of where the target word can and cannot occur as well as gradient preferences about the word’s appropriateness. |
WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations (N19-1)
Copied to clipboard
| Challenge: | Existing word embeddings cannot model the dynamic nature of words’ semantics, i.e., the property of words to correspond to potentially different meanings. |
| Approach: | They propose a large-scale Word in Context dataset, called WiC, which is curated by experts and can be used to evaluate context-sensitive representations. |
| Outcome: | The proposed models outperform the standard evaluation dataset for the purpose and highlight their shortcomings. |
Query2Prod2Vec: Grounded Word Embeddings for eCommerce (2021.naacl-industry)
Copied to clipboard
| Challenge: | Query2Prod2Vec is a model that grounds lexical representations for product search in product embeddings. |
| Approach: | They propose a model that grounds lexical representations for product search in product embeddings. |
| Outcome: | The proposed model is more accurate than existing methods from the literature . it is also more efficient than existing embedding methods in the context of high-traffic websites. |
Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
Copied to clipboard
| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. authors propose a cutting-edge, full-day tutorial for all stakeholders in the AI community. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
From Brain Space to Distributional Space: The Perilous Journeys of fMRI Decoding (P19-2)
Copied to clipboard
| Challenge: | Recent work in cognitive neuroscience has introduced models for predicting distributional word meaning representations from brain imaging data. |
| Approach: | They propose to use several alternative measures to evaluate the predicted distributional space against a corpus-derived distributional spatial space. |
| Outcome: | The proposed model performs poorly on the most common metrics, while still delivering promising results. |