Papers with distributional semantic models
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models (D19-61)
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| Challenge: | Word embeddings are an essential component of many natural language processing applications. |
| Approach: | They propose 3 new tasks to obtain higher-quality vectors for word embeddings . they use word forms in training data that are related to word forms themselves . |
| Outcome: | The proposed methods improve the performance of both baseline and advanced models on 4 out of 6 tasks. |
Network Features Based Co-hyponymy Detection (L18-1)
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| Challenge: | Existing methods to detect lexical relations have been used to identify them in both supervised and unsupervised ways. |
| Approach: | They propose to use distributional semantic models to detect co-hyponymy relation with high accuracy and various network measures to perform better or at par with the state-of-the-art models. |
| Outcome: | The proposed model performs better or at par with the state-of-the-art models. |
Why is penguin more similar to polar bear than to sea gull? Analyzing conceptual knowledge in distributional models (2020.acl-srw)
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| 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. |
Challenging distributional models with a conceptual network of philosophical terms (2021.naacl-main)
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| Challenge: | Existing methods for analyzing philosophical data are not accurate enough to support philosophers . comparative research on concepts should follow a conceptual model approach, authors argue . |
| Approach: | They propose a ground truth for evaluation created by philosophy experts and a blueprint for using DS models in a sound methodological setup. |
| Outcome: | The proposed model does not perform well enough to directly support philosophers yet, but it yields promising directions for future work. |
On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings (P19-1)
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| Challenge: | idiomatic phrases have a non-compositional meaning, meanings of which can be derived from constituents and their grammatical relations. |
| Approach: | They propose to combine hierarchical and distributional information to blend hierarchic and distribution-based hierarchies to detect compositionality for noun phrases. |
| Outcome: | The proposed technique achieves significant improvements over state-of-the-art models based on distributional information and a weighted average of the distributional similarity and p-like function. |
AnlamVer: Semantic Model Evaluation Dataset for Turkish - Word Similarity and Relatedness (C18-1)
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| Challenge: | a dataset for semantic model evaluation for Turkish is not available for the language . a similarity and relatedness evaluation resource is needed for higher level tasks . |
| Approach: | They propose a semantic model evaluation dataset for Turkish that evaluates word similarity and word relatedness tasks while discriminating those two relations from each other. |
| Outcome: | The proposed dataset is designed to evaluate word similarity and word relatedness tasks in Turkish. |
A Formidable Ability: Detecting Adjectival Extremeness with DSMs (2021.findings-acl)
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| Challenge: | Existing studies on distributional semantic models capture abstract semantic properties across domains . abstract properties can form the basis for abstract semantic classes . |
| Approach: | They propose to use distributional semantic models to capture cross-domain properties . they use extremeness to model emergence of intensifier meaning in adverbs . |
| Outcome: | The proposed model can capture extremeness and intensifier meaning in adverbs. |
Distributional Term Set Expansion (L18-1)
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| Challenge: | Iterative term set expansion methods for distributional semantic models are used to label terms belonging to a sought after term set. |
| Approach: | They compare iterative term set expansion methods for distributional semantic models to the Simple Margin method, an active learning approach to classification using Support Vector Machines. |
| Outcome: | The proposed methods outperform centrality and classification based methods for distributional semantic models over five different term sets. |
Modeling Affirmative and Negated Action Processing in the Brain with Lexical and Compositional Semantic Models (P19-1)
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| Challenge: | Existing studies have shown that distributional semantic models can be used to decode fMRI patterns associated with specific aspects of semantic composition, such as the negation function. |
| Approach: | They apply lexical and compositional semantic models to decode fMRI patterns associated with negated and affirmative sentences containing hand-action verbs. |
| Outcome: | The proposed models show reduced decoding of sentences where the verb is in the negated context, as compared to the affirmative one, within brain regions implicated in action-semantic processing. |
SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages (L18-1)
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| Challenge: | SemR-11 is a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages. |
| Approach: | This paper describes a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages. |
| Outcome: | The dataset is a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages. |