| Challenge: | a large corpus of newspaper archives has been generated, but historians have struggled to analyze it manually for decades. |
| Approach: | They propose to train word embeddings from three large Lebanese news archives, which collectively consist of 609,386 scanned newspaper images and span 151 years. |
| Outcome: | The embeddings are trained using a Google Tesseract 4.0 OCR engine and a benchmark of analogy tasks to evaluate their accuracy. |
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| Challenge: | Word embeddings are powerful representations that form the foundation of many natural language processing architectures. |
| Approach: | They explore word embedding stability in a wide range of languages to gain insight into their stability. |
| Outcome: | The proposed results provide insights into word embedding stability in English and other languages. |
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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Domain-Specific Word Embeddings with Structure Prediction (2023.tacl-1)
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| Challenge: | Current word embedding methods do not provide a way to use or predict information on structure between sub-corpora, time or domain. |
| Approach: | They propose a word embedding method that provides general word representations for the whole corpus, domain-specific representations and embeddable alignment simultaneously. |
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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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Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity (2020.acl-main)
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| Challenge: | Existing word embeddings combine complementary strengths of their components to achieve unsupervised semantic similarity (STS). |
| Approach: | They propose to ensemble pre-trained sentence encoders into sentence meta-embeddings to achieve unsupervised Semantic Textual Similarity (STS) they adapt dimensionality reduction, generalized Canonical Correlation Analysis and cross-view auto-encoders to their work. |
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Exploring Word Usage Change with Continuously Evolving Embeddings (2021.acl-demo)
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| Challenge: | a new method to track word usage changes is proposed for text datasets that are collected over a longer period of time. |
| Approach: | They propose a way to track word usage changes via continuously evolving embeddings . they demonstrate an interactive web app that can explore semantic shifts with interactive plots a text . |
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Relational Word Embeddings (P19-1)
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| Challenge: | Existing approaches to learn word embeddings rely on external knowledge bases . however, they are limited by the amount of available relational knowledge . |
| Approach: | They propose to encode relational knowledge in a separate word embedding . this is complementary to a standard word embedded from co-occurrence statistics . |
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Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment (D18-1)
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| Challenge: | Existing approaches to network embeddings focus on one-hot representations of vertices, which are not able to capture relationships between verti- ces. |
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Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings (2021.emnlp-main)
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| Challenge: | Semantic sentence embeddings are usually supervisedly built minimizing distances between pairs of embeddable sentences labelled as semantically similar by annotators. |
| Approach: | They propose a language-independent approach to build large datasets of pairs of informal texts weakly similar, without manual human effort, exploiting Twitter’s powerful signals of relatedness: replies and quotes of tweets. |
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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. |