Challenge: Existing tools for learning the embeddings of words and entities from Wikipedia are not yet available.
Approach: They propose a Python-based tool for learning Wikipedia embeddings from Wikipedia . they use a Wikipedia dump file as an argument to issue a single command .
Outcome: The proposed tool achieves state-of-the-art results on the KORE entity relatedness dataset and competitive results on benchmark datasets.

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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.
Outcome: The proposed method provides better performance than baselines on a dataset of science and philosophy articles.
HG2Vec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph (2022.coling-1)

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Challenge: Existing models that learn word embeddings rely on a large corpus of data . however, these models require massive time and space for data pre-processing and training .
Approach: They propose a model that learns word embeddings utilizing only dictionaries and thesauri . they exploit a new context-focused loss model that models transitive relationships between word pairs .
Outcome: The proposed model reaches the state-of-art on multiple word similarity and relatedness benchmarks.
Linking, Searching, and Visualizing Entities in Wikipedia (L18-1)

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Challenge: Existing systems to extract, index, search, and visualize entities in Wikipedia are not strings, but unique identifiers from Wikidata.
Approach: They propose a system to extract, index, search, and visualize entities in Wikipedia . they use a document model to store linguistic annotations and a string matching engine .
Outcome: The proposed system achieves CEAFm scores of 70.0 on English, 64.4 on Chinese, and 66.5 on Spanish.
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 .
Word Mover’s Embedding: From Word2Vec to Document Embedding (D18-1)

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Challenge: Recent work has demonstrated that Word Mover’s Distance (WMD) that aligns semantically similar words yields unprecedented KNN classification accuracy.
Approach: They propose a Word Mover’s Distance (WMD) method that aligns semantically similar words to generate unsupervised sentences or documents embeddings.
Outcome: The proposed method consistently outperforms state-of-the-art techniques on 9 benchmark text classification datasets and 22 textual similarity tasks.
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.
Outcome: The proposed model is more accurate than existing methods from the literature . it is also more efficient than existing embedding methods in the context of high-traffic websites.
Glyph2Vec: Learning Chinese Out-of-Vocabulary Word Embedding from Glyphs (2020.acl-main)

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Challenge: Chinese NLP applications that rely on large text often contain huge amounts of vocabulary which are sparse in corpus.
Approach: They propose a multi-modal model that extracts visual features from Chinese word glyphs to expand current word embedding space without accessing any corpus.
Outcome: The proposed model can embed words in Chinese without accessing corpus without a corpus.
Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings (N18-2)

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Challenge: Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases.
Approach: They propose an unsupervised technique that leverages phrase embeddings for ranking keyphrases extracted from scientific articles using theme-weighted PageRank.
Outcome: The proposed method performs better on benchmark datasets than other methods and is of high quality.
Deconstructing word embedding algorithms (2020.emnlp-main)

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Challenge: Word embeddings are reliable feature representations of words used in many NLP tasks today.
Approach: They propose to deconstruct Word2vec, GloVe and others into a common form . they propose to generalize several word embedding algorithms into . a low rank embedder framework is proposed to generalise the algorithms into one common form.
Outcome: The proposed framework can be used to make word embeddings more performant.
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT (2020.findings-emnlp)

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Challenge: Existing methods to enhance BERT with factual knowledge about entities require no additional pretraining and no changes to the encoder itself.
Approach: They propose a way to inject factual knowledge into the pretrained BERT model by aligning Wikipedia2Vec entity vectors with BERT's native wordpiece vector space and feeding the aligned entity vector into BERT as if they were wordpieces.
Outcome: The proposed version outperforms baseline models on unsupervised question answering, supervised relation classification and entity linking tasks.

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