Enriching Word Embeddings with Domain Knowledge for Readability Assessment (C18-1)
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| Challenge: | Existing word embedding models focus on syntactic or semantic relations of words, while ignoring reading difficulty. |
| Approach: | They propose a method which learns the word embedding for readability assessment . they extract the knowledge on word-level difficulty from three perspectives to construct a knowledge graph . |
| Outcome: | The proposed method is effective and potential, the authors show . they use the knowledge-enriched word embedding model on English and Chinese datasets . |
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Evaluation of Domain-specific Word Embeddings using Knowledge Resources (L18-1)
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| Challenge: | Existing word embeddings capture a range of semantic relations relevant to the interpretation of lexical items, but domain-specific terms are difficult to evaluate because of a lack of statistical clues in the underlying corpus. |
| Approach: | They conduct intrinsic and extrinsic evaluations of both general and domain-specific embeddings and adapt embeddment enhancement methods to provide vector representations for infrequent and unseen terms. |
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Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment (2021.acl-long)
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| Challenge: | Existing deep learning models for automatic readability assessment discard linguistic features traditionally used for the task. |
| Approach: | They propose to incorporate linguistic features into machine learning models by learning syntactic dense embeddings based on linguistic feature extraction. |
| Outcome: | Experiments with six data sets of two proficiency levels show that the proposed model can perform better than existing models. |
Interpretable Word Embeddings via Informative Priors (D19-1)
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| Challenge: | Existing word embeddings lack interpretability and are unsupervised . this limitation limits their use within computational social science and digital humanities. |
| Approach: | They propose to use informative priors to create interpretable dimensions for probabilistic word embeddings using a priori model. |
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A Retrofitting Model for Incorporating Semantic Relations into Word Embeddings (2020.coling-main)
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| Challenge: | Existing word embedding models mix semantic similarity with other types of relatedness. |
| Approach: | They propose a model that leverages relational knowledge available in a knowledge resource to improve word embeddings. |
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Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources (2020.acl-srw)
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| Challenge: | In this paper, we present an effective method for semantic specialization of word vector representations. |
| Approach: | They propose a method for semantic specialization of word vector representations using BabelNet. |
| Outcome: | The proposed method improves on word similarity and dialog state tracking tasks. |
Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
| Approach: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and then move to other types of embeddable vectors . |
| Outcome: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and move to other types of embeddable representations . |
Which Evaluations Uncover Sense Representations that Actually Make Sense? (2020.lrec-1)
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| Challenge: | Existing sense representations fail for human-centric tasks like inspecting a language’s sense inventory. |
| Approach: | They propose a coherence evaluation for sense embeddings and a model optimized for finding interpretable sense representations that are more coherent than existing sense embeds. |
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Multifaceted Domain-Specific Document Embeddings (2021.naacl-demos)
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| Challenge: | Current document embeddings require large training corpora but fail to learn high-quality representations when confronted with a small number of domain-specific documents and rare terms. |
| Approach: | They propose a faceted domain encoder that transforms each document into a single embedding vector . they use a Siamese neural network architecture to leverage knowledge graphs to enhance the embeddables . |
| Outcome: | The proposed model achieves the same embedding quality as state-of-the-art models while requiring only a tiny fraction of training data. |
Learning Domain-Sensitive and Sentiment-Aware Word Embeddings (P18-1)
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| Challenge: | Existing word embeddings cannot produce domain-sensitive embeddables due to domain-specific nature of words. |
| Approach: | They propose a method for learning domain-sensitive and sentiment-aware embeddings that captures sentiment semantics and domain sensitivity of individual words. |
| Outcome: | The proposed method can produce domain-common embeddings and domain-specific embedds. |
A Deeper Look into Dependency-Based Word Embeddings (N18-4)
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| Challenge: | Word embeddings trained with dependency contexts excel at different tasks, and enhanced dependencies often improve performance. |
| Approach: | They propose to use dependency-based word embeddings to capture semantic similarity rather than relatedness. |
| Outcome: | The results show that word embeddings trained with Universal and Stanford dependencies excel at different tasks and that enhanced dependencies often improve performance. |