{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/confet-an-english-sentence-to-emojis","title":"CoNFET: An English Sentence to Emojis Translation Algorithm","arxiv_id":null,"date":"2021-01-06","proceeding":null,"authors":["Alex Day","Chris Mankos","Soo Kim","Jody Strausser"],"abstract":"Emojis are a collection of emoticons that have been standardized by the Unicode Consortium. Currently, there are\r\nover 3,000 emojis in the Unicode standard. These small pictographs can represent an object as vague as a laughter (🤣)\r\nto something as specific as a passport control (🛂). Due to\r\ntheir high information density and the sheer amount, emojis have become prevalent in common communication media\r\nsuch as SMS and Twitter. There is a need to increase natural language understanding in the emoji domain. To this end,\r\nwe present the CoNFET (Composition of N-grams for Emoji\r\nTranslation) algorithm to translate an English sentence into\r\na sequence of emojis. This translation algorithm consists of\r\nthree main parts: the n-gram sequence generation, the n-gram\r\nto emoji translation, and the translation scoring. First, the input sentence is split into its constituent n-grams either in an\r\nexhaustive manner or using dependency relations. Second,\r\nthe n-grams of the sentence are translated into emojis using\r\nthe nearest neighbor in a vectorized linguistic space. Finally,\r\nthese translations are scored using either a simple average or\r\nan average weighted by the Term Frequency-Inverse Document Frequency (TF-IDF) score of the n-gram. As the result,\r\nthe sequence of emojis with the highest score is selected as\r\nan output of the sentence summarization.","url_abs":"https://www.alexday.me/pdf/emoji.pdf","url_pdf":"https://www.alexday.me/pdf/emoji.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"confet-an-english-sentence-to-emojis","repo_url":"https://github.com/AlexanderDavid/Sentence-to-Emoji-Translation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"confet-an-english-sentence-to-emojis","repo_url":"https://github.com/AlexanderDavid/SentenceToEmojiTranslation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-summarization","task_name":"Sentence Summarization"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}