A non-sequential refinement approach to improve word embeddings using GPU-based string matching algorithms
Unlike other word embedding models that learn word vectors for a collection of words sequentially, this paper proposes a non-sequential refinement approach to improve the vectors of particular words non-sequentially using a string matching algorithm to speed up the process. The key idea is to change...
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| Published in: | Cluster computing Vol. 24; no. 4; pp. 3123 - 3134 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Springer US
01.12.2021
Springer Nature B.V |
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| ISSN: | 1386-7857, 1573-7543 |
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| Abstract | Unlike other word embedding models that learn word vectors for a collection of words sequentially, this paper proposes a non-sequential refinement approach to improve the vectors of particular words non-sequentially using a string matching algorithm to speed up the process. The key idea is to change the order of training in the embedding learning model and force it to learn the vector of a particular word completely before skipping to other target words. The learned vector of the given word and its context vectors are then used to train other target words. In this case, later words can be trained by the word vectors that are more accurate. In this study, the effect of training order in the Skip-gram model is investigated and a quantitative and qualitative comparison is made between the learned vectors in the word similarity task. To speed up the process, a GPU based string matching algorithm is used to find the occurrences of the given word in the training corpus. Incorporating the GPU-based string matching algorithm into the Skip-gram model to refine particular word vectors is, to our best knowledge, the first use case in the literature. Additionally, we provide in-depth analysis of GPU parallelization and identification of string matching algorithms that are suitable for integrating into word embedding models. |
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| AbstractList | Unlike other word embedding models that learn word vectors for a collection of words sequentially, this paper proposes a non-sequential refinement approach to improve the vectors of particular words non-sequentially using a string matching algorithm to speed up the process. The key idea is to change the order of training in the embedding learning model and force it to learn the vector of a particular word completely before skipping to other target words. The learned vector of the given word and its context vectors are then used to train other target words. In this case, later words can be trained by the word vectors that are more accurate. In this study, the effect of training order in the Skip-gram model is investigated and a quantitative and qualitative comparison is made between the learned vectors in the word similarity task. To speed up the process, a GPU based string matching algorithm is used to find the occurrences of the given word in the training corpus. Incorporating the GPU-based string matching algorithm into the Skip-gram model to refine particular word vectors is, to our best knowledge, the first use case in the literature. Additionally, we provide in-depth analysis of GPU parallelization and identification of string matching algorithms that are suitable for integrating into word embedding models. |
| Author | Naderalvojoud, Behzad Ozsoy, Adnan |
| Author_xml | – sequence: 1 givenname: Behzad orcidid: 0000-0003-4429-5341 surname: Naderalvojoud fullname: Naderalvojoud, Behzad email: n.behzad@hacettepe.edu.tr organization: Department of Computer Engineering, Hacettepe University – sequence: 2 givenname: Adnan surname: Ozsoy fullname: Ozsoy, Adnan organization: Department of Computer Engineering, Hacettepe University |
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| Cites_doi | 10.1371/journal.pone.0200912 10.1016/j.neucom.2020.03.094 10.1007/s10586-016-0649-7 10.1109/ACCESS.2019.2914071 10.1613/jair.4135 10.1145/361219.361220 10.1109/TPDS.2016.2645222 10.22452/mjcs.vol31no3.3 10.1080/01690969108406936 10.11591/ijeecs.v5.i2.pp462-471 10.1145/2431211.2431212 10.1145/365628.365657 10.1136/jamia.2000.0070378 10.1145/503104.503110 10.1007/978-3-319-99007-1_24 10.18653/v1/N18-1202 10.1109/PCI.2009.47 10.1007/978-981-13-7561-3_21 10.1007/s11227-019-03024-z 10.1145/1390156.1390177 10.1007/978-3-642-19137-4_3 |
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| Title | A non-sequential refinement approach to improve word embeddings using GPU-based string matching algorithms |
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