KeywordScape: Visual Document Exploration using Contextualized Keyword Embeddings (2022.emnlp-demos)
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| Challenge: | Existing tools for document visualization assume that keywords have static meanings, but contextualized word embeddings are unrealistic. |
| Approach: | They propose a visual exploration tool that visualizes contextualized word embeddings in documents based on keywords. |
| Outcome: | The proposed tool visualizes keywords in terms of their contextualized embeddings in a semantic landscape that keeps keywords with similar context close to each other, allowing for a more precise search and comparison of documents. |
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| Challenge: | Existing benchmarks for phrase-similarity compare phrases alone (without context) and phrases with context (with or without context). |
| Approach: | They propose to use a dataset of 28K noun phrases accompanied by their contextual Wikipedia pages to train machine phrase embeddings. |
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What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition (D19-1)
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| Challenge: | Contextualized word embeddings have boosted many NLP tasks compared with static word embeds. |
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| Outcome: | The proposed framework can explain word meanings given contextualized word embeddings for better interpretation. |
WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations (N19-1)
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| Challenge: | Existing word embeddings cannot model the dynamic nature of words’ semantics, i.e., the property of words to correspond to potentially different meanings. |
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Building Static Embeddings from Contextual Ones: Is It Useful for Building Distributional Thesauri? (2022.lrec-1)
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| Challenge: | contextual language models are dominant in the field of Natural Language Processing, but they are not suitable for all uses. |
| Approach: | They propose a method for building word or type-level embeddings from contextual models . they evaluate a large set of English nouns from the perspective of extracting semantic similarity relations . |
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Contextualized Usage-Based Material Selection (L18-1)
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| Challenge: | Currently, authentic linguistic examples for a given keyword search are organized alphabetically according to context. |
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Query2Prod2Vec: Grounded Word Embeddings for eCommerce (2021.naacl-industry)
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| 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. |
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SensePOLAR: Word sense aware interpretability for pre-trained contextual word embeddings (2022.findings-emnlp)
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| Challenge: | Existing word embedding models lack interpretability for words . |
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Dynamic Contextualized Word Embeddings (2021.acl-long)
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| Challenge: | Static word embeddings that represent words by a single vector cannot capture word meaning in different linguistic and extralinguistic contexts. |
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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 . |
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A Survey on Automatically-Constructed WordNets and their Evaluation: Lexical and Word Embedding-based Approaches (L18-1)
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| Challenge: | WordNets are lexical databases in which groups of synonyms are stored according to the semantic relationships between them. |
| Approach: | This paper describes various approaches to constructing WordNets automatically by leveraging traditional lexical resources and newer trends such as word embeddings. |
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