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A Joint Model for Word Embedding and Word Morphology

arXiv 1606.02601 ↗First posted 8 Jun 2016cs.CLcited by 14

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Abstract (arXiv)

This paper presents a joint model for performing unsupervised morphological analysis on words, and learning a character-level composition function from morphemes to word embeddings. Our model splits individual words into segments, and weights each segment according to its ability to predict context words. Our morphological analysis is comparable to dedicated morphological analyzers at the task of morpheme boundary recovery, and also performs better than word-based embedding models at the task of syntactic analogy answering. Finally, we show that incorporating morphology explicitly into character-level models help them produce embeddings for unseen words which correlate better with human judgments.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2016.

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Citing papers per year: 2021: 8; 2022: 4; 2023: 2.

14 papers.

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  1. 2021You should evaluate your language model on marginal likelihood over tokenisationscited by 29
  2. 2022Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Costcited by 9
  3. 2023What is the best recipe for character-level encoder-only modelling?cited by 6
  4. 2022A Survey of Text Representation Methods and Their Genealogycited by 5
  5. 2021Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflowcited by 4
  6. 2022Whois? Deep Author Name Disambiguation using Bibliographic Datacited by 3
  7. 2021Inducing Meaningful Units from Character Sequences with Dynamic Capacity Slot Attentioncited by 2
  8. 2021A Survey On Neural Word Embeddingscited by 2
  9. 2022A Quantitative and Qualitative Analysis of Suicide Ideation Detection using Deep Learningcited by 2
  10. 2021Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Datacited by 1
  11. 2021Evaluation of Morphological Embeddings for the Russian Languagecited by 0
  12. 2021Evaluation of Morphological Embeddings for English and Russian Languagescited by 0
2 more
  1. 2021Explainable Sentence-Level Sentiment Analysis for Amazon Product Reviewscited by 0
  2. 2023Deep Author Name Disambiguation using DBLP Datacited by 0