Challenge: Compound words provide a multifaceted challenge for diachronic models of semantic change . novel sense-targeting approach targets both noun compounds and their constituent parts .
Approach: They propose a dataset of relatedness judgements of noun compounds in English and german . they use contrasting vector representations to evaluate their ability to cluster example sentence pairs .
Outcome: The proposed approach captures diachronic meaning changes for multi-word expressions without condensing individual senses into an aggregate value.

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Modeling the Evolution of English Noun Compounds with Feature-Rich Diachronic Compositionality Prediction (2025.acl-long)

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Challenge: Empirical research directly addressing these issues is limited to a small number of studies suggesting that compounding is a highly productive process.
Approach: They represent English noun compounds as vectors of time-specific values and implement a set of features to classify them for present-day compositionality and assess the informativeness of the corresponding linguistic patterns.
Outcome: The proposed method captures relevant and complementary information across approaches and shows that low-compositional meanings are reflected by a parallel drop in compositionality and sustained semantic change.
What Can Diachronic Contexts and Topics Tell Us about the Present-Day Compositionality of English Noun Compounds? (2024.lrec-main)

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Challenge: Existing methods to determine the semantic relatedness between compounds and constituents have applied a synchronic perspective, but this study examines what diachronic changes in contexts and semantic topics reveal about the compounds’ present-day compositionality.
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Outcome: The proposed model performs on par with co-occurrence space and captures similar information.
Can Large Language Models Interpret Noun-Noun Compounds? A Linguistically-Motivated Study on Lexicalized and Novel Compounds (2024.acl-long)

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Challenge: Noun-noun compounds represent an important challenge for Natural Language Understanding . correct interpretation of noun-nomin compounds is essential for many applications .
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Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change (N18-2)

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Challenge: Existing frameworks for evaluating lexical semantic change are limited . evaluation of lexicals is a major obstacle in the field of semantic change detection .
Approach: They propose a framework that extends synchronic polysemy annotation to diachronic changes in lexical meaning to counteract lack of resources for evaluating computational models of lexiconal semantic change.
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Diachronic word embeddings and semantic shifts: a survey (C18-1)

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Challenge: Existing methods for tracing time-related semantic shifts with word embedding models lack the cohesion, common terminology and shared practices of more established areas of natural language processing.
Approach: They propose several axes along which these methods can be compared and propose a framework for comparison.
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Towards a Standardized Dataset for Noun Compound Interpretation (L18-1)

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Challenge: Noun compounds are interesting constructs in Natural Language Processing . lack of standardized set of relation inventories and annotated datasets hinders interpretation .
Approach: They propose a dataset that uses FrameNet as its semantic relation inventory to examine noun compounds.
Outcome: The proposed dataset is linguistically grounded and uses FrameNet as its semantic relation inventory.
Variants of Vector Space Reductions for Predicting the Compositionality of English Noun Compounds (2020.lrec-1)

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Challenge: Existing approaches to predict the degree of compositionality of noun compounds are based on comparing compounds and their constituents within a vector space and using distributional similarity as a proxy to predict their degree of semantic relatedness.
Approach: They propose to use distributional similarity as a proxy to predict the semantic relatedness between the compounds and their constituents as the compound’s degree of compositionality.
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Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation (D18-1)

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Challenge: In computational linguistics, nounnoun compound interpretation is approached as an automatic classification problem.
Approach: They empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task.
Outcome: The proposed methods improve the accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations.
Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View (P19-1)

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Challenge: Existing word embeddings only assign one vector to a word for a time period, thus they face the meaning conflation deficiency.
Approach: They propose a sense representation and tracking framework based on deep contextualized embeddings that can be used to answer what and when the word meaning changes.
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A Systematic Search for Compound Semantics in Pretrained BERT Architectures (2023.eacl-main)

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Challenge: Existing models for noun compounds have been less successful in predicting compositionality than transformers . authors: suboptimal use of encoded information may be a contributing factor . performance of transformer-based models is poor, authors say .
Approach: They propose to use semantic knowledge derived from pretrained BERT to predict compositionality . they find distinct linguistic roles of heads and modifiers are reflected by differences in BERT representations .
Outcome: The proposed model improves on unsupervised implementations of pretrained BERT . empirical properties such as frequency, productivity, and ambiguity affect performance .

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