Learning isolated polysemous words: identifying the intended meaning of language learners in informal ubiquitous language learning environments.
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| Title: | Learning isolated polysemous words: identifying the intended meaning of language learners in informal ubiquitous language learning environments. |
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| Authors: | Abou-Khalil, Victoria1 (AUTHOR) v.aboukhalil@gmail.com, Helou, Samar1 (AUTHOR), Flanagan, Brendan2 (AUTHOR), Chen, Mei-Rong Alice2 (AUTHOR), Ogata, Hiroaki2 (AUTHOR) |
| Source: | Smart Learning Environments. 11/13/2019, Vol. 6 Issue 1, pN.PAG-N.PAG. 1p. |
| Subject Terms: | *Classroom environment, *Computer assisted language instruction, Ubiquitous computing, Semantics |
| Abstract: | A growing number of language learners use ubiquitous language learning applications to learn anytime and anywhere. Learners translate and learn isolated words inspired by their activities and surroundings. However, isolated words may have several meanings that change depending on the context. Since learners don't have the opportunity to indicate the meaning they are looking for in an online learning environment, they risk learning translations that do not correspond to their intended meaning. Identifying the intended meaning of the learner is needed to provide them with an appropriate translation. However, isolated words are difficult to disambiguate due to a lack of text around them. To this end, informal ubiquitous learning environments can offer another type of context, one that is formed by the users' past learning logs. In this work, we propose using the learners' past vocabulary to disambiguate their intended meaning when they look up isolated words. Accordingly, we propose and evaluate three methods. The first method considers that the intended meaning of the learner is the one that is the most semantically similar to the learner's past vocabulary. The second method builds on the first method but gives more weight to the vocabulary that the learner logged shortly before the target word. The third method addresses situations where the semantic similarities between the different meanings of the word and the past vocabulary have similar values. In those cases, the method considers that the intended meaning of the learner is the most common meaning in the target language. The three methods were evaluated using 148 logs of SCROLL, a ubiquitous informal language learning environment. The success rates of the three methods were 72.180%, 75.630%, and 83.050% respectively. This work shows that the past activity of language learners in informal ubiquitous language learning environments could be used to identify their intended meaning when learning a new word. [ABSTRACT FROM AUTHOR] |
| Copyright of Smart Learning Environments is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 139655162 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Learning isolated polysemous words: identifying the intended meaning of language learners in informal ubiquitous language learning environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Abou-Khalil%2C+Victoria%22">Abou-Khalil, Victoria</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> v.aboukhalil@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Helou%2C+Samar%22">Helou, Samar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Flanagan%2C+Brendan%22">Flanagan, Brendan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Mei-Rong+Alice%22">Chen, Mei-Rong Alice</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ogata%2C+Hiroaki%22">Ogata, Hiroaki</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Smart+Learning+Environments%22">Smart Learning Environments</searchLink>. 11/13/2019, Vol. 6 Issue 1, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Classroom+environment%22">Classroom environment</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+assisted+language+instruction%22">Computer assisted language instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Ubiquitous+computing%22">Ubiquitous computing</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A growing number of language learners use ubiquitous language learning applications to learn anytime and anywhere. Learners translate and learn isolated words inspired by their activities and surroundings. However, isolated words may have several meanings that change depending on the context. Since learners don't have the opportunity to indicate the meaning they are looking for in an online learning environment, they risk learning translations that do not correspond to their intended meaning. Identifying the intended meaning of the learner is needed to provide them with an appropriate translation. However, isolated words are difficult to disambiguate due to a lack of text around them. To this end, informal ubiquitous learning environments can offer another type of context, one that is formed by the users' past learning logs. In this work, we propose using the learners' past vocabulary to disambiguate their intended meaning when they look up isolated words. Accordingly, we propose and evaluate three methods. The first method considers that the intended meaning of the learner is the one that is the most semantically similar to the learner's past vocabulary. The second method builds on the first method but gives more weight to the vocabulary that the learner logged shortly before the target word. The third method addresses situations where the semantic similarities between the different meanings of the word and the past vocabulary have similar values. In those cases, the method considers that the intended meaning of the learner is the most common meaning in the target language. The three methods were evaluated using 148 logs of SCROLL, a ubiquitous informal language learning environment. The success rates of the three methods were 72.180%, 75.630%, and 83.050% respectively. This work shows that the past activity of language learners in informal ubiquitous language learning environments could be used to identify their intended meaning when learning a new word. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Smart Learning Environments is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s40561-019-0095-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Classroom environment Type: general – SubjectFull: Computer assisted language instruction Type: general – SubjectFull: Ubiquitous computing Type: general – SubjectFull: Semantics Type: general Titles: – TitleFull: Learning isolated polysemous words: identifying the intended meaning of language learners in informal ubiquitous language learning environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Abou-Khalil, Victoria – PersonEntity: Name: NameFull: Helou, Samar – PersonEntity: Name: NameFull: Flanagan, Brendan – PersonEntity: Name: NameFull: Chen, Mei-Rong Alice – PersonEntity: Name: NameFull: Ogata, Hiroaki IsPartOfRelationships: – BibEntity: Dates: – D: 13 M: 11 Text: 11/13/2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 21967091 Numbering: – Type: volume Value: 6 – Type: issue Value: 1 Titles: – TitleFull: Smart Learning Environments Type: main |
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