Social Image Tags as a Source of Word Embeddings: A Task-oriented Evaluation (L18-1)
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| Challenge: | Distributional hypothesis-based word representations lack perceptual and empirical knowledge. |
| Approach: | They evaluate the effectiveness of social image tags in generating word embeddings . they find that generated word embeds exhibit somewhat different behaviors from corpus-originated representations - authors . |
| Outcome: | The generated word embeddings exhibit comparable performance with corpus-originated representations. |
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| Challenge: | Attempts to find a single technique for general-purpose intrinsic evaluation of word embeddings have so far not been successful. |
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| Challenge: | a corpus of medical-themed posts was scrapped from Reddit to train word embeddings on downstream tasks. |
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Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit? (2020.lrec-1)
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| Challenge: | In recent years, vectors derived from neural network training have replaced count-based distributional semantic models as a de facto standard for word representation in NLP. |
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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. |
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Extracting Possessions from Social Media: Images Complement Language (D19-1)
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Inducing Universal Semantic Tag Vectors (2020.lrec-1)
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| Challenge: | Existing semantic tags are useful for syntactically oriented downstream NLP tasks . but their size is limited and many words are out-of-vocabulary words . |
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Querying Word Embeddings for Similarity and Relatedness (N18-1)
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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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Analyzing the Surprising Variability in Word Embedding Stability Across Languages (2021.emnlp-main)
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| Challenge: | Word embeddings are powerful representations that form the foundation of many natural language processing architectures. |
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