Lightweight Word Spacing Model Based on Short Text Messages for Social Networking in Smart Homes
In smart homes, information appliances interact with residents via social network services. To capture residents' intentions, the information appliances should analyze short text messages entered typically through small mobile devices. However, most information appliances have hardware constrai...
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| Format: | Article |
| Language: | English |
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Wiley
2014-02-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/532759 |
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| _version_ | 1832547865655771136 |
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| author | Yeongkil Song Harksoo Kim |
| author_facet | Yeongkil Song Harksoo Kim |
| author_sort | Yeongkil Song |
| collection | DOAJ |
| description | In smart homes, information appliances interact with residents via social network services. To capture residents' intentions, the information appliances should analyze short text messages entered typically through small mobile devices. However, most information appliances have hardware constraints such as small memory, limited battery capacity, and restricted processing power. Therefore, it is not easy to embed intelligent applications based on natural language processing (NLP) techniques, which traditionally require large memory and high-end processing power, into information appliances. To overcome this problem, lightweight NLP modules should be implemented. We propose an automatic word spacing system, the first step module of NLP for many languages with their own word spacing rules, which is designed for information appliances with limited hardware resources. The proposed system consists of a word spacing dictionary and a pattern-matching module. When a sentence is entered, the pattern-matching module inserts spaces by simply looking up the word spacing dictionary in a back-off manner. In comparative experiments with previous models, the proposed method showed low memory usage (0.79 MB) and high character-unit accuracy (0.9460) without requiring complex arithmetical computations. On the basis of these experiments, we conclude that the proposed system is suitable for information appliances with many hardware limitations. |
| format | Article |
| id | doaj-art-b195b8d99d0b4cf1bda0ee709258343f |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-b195b8d99d0b4cf1bda0ee709258343f2025-02-03T06:43:09ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-02-011010.1155/2014/532759532759Lightweight Word Spacing Model Based on Short Text Messages for Social Networking in Smart HomesYeongkil SongHarksoo KimIn smart homes, information appliances interact with residents via social network services. To capture residents' intentions, the information appliances should analyze short text messages entered typically through small mobile devices. However, most information appliances have hardware constraints such as small memory, limited battery capacity, and restricted processing power. Therefore, it is not easy to embed intelligent applications based on natural language processing (NLP) techniques, which traditionally require large memory and high-end processing power, into information appliances. To overcome this problem, lightweight NLP modules should be implemented. We propose an automatic word spacing system, the first step module of NLP for many languages with their own word spacing rules, which is designed for information appliances with limited hardware resources. The proposed system consists of a word spacing dictionary and a pattern-matching module. When a sentence is entered, the pattern-matching module inserts spaces by simply looking up the word spacing dictionary in a back-off manner. In comparative experiments with previous models, the proposed method showed low memory usage (0.79 MB) and high character-unit accuracy (0.9460) without requiring complex arithmetical computations. On the basis of these experiments, we conclude that the proposed system is suitable for information appliances with many hardware limitations.https://doi.org/10.1155/2014/532759 |
| spellingShingle | Yeongkil Song Harksoo Kim Lightweight Word Spacing Model Based on Short Text Messages for Social Networking in Smart Homes International Journal of Distributed Sensor Networks |
| title | Lightweight Word Spacing Model Based on Short Text Messages for Social Networking in Smart Homes |
| title_full | Lightweight Word Spacing Model Based on Short Text Messages for Social Networking in Smart Homes |
| title_fullStr | Lightweight Word Spacing Model Based on Short Text Messages for Social Networking in Smart Homes |
| title_full_unstemmed | Lightweight Word Spacing Model Based on Short Text Messages for Social Networking in Smart Homes |
| title_short | Lightweight Word Spacing Model Based on Short Text Messages for Social Networking in Smart Homes |
| title_sort | lightweight word spacing model based on short text messages for social networking in smart homes |
| url | https://doi.org/10.1155/2014/532759 |
| work_keys_str_mv | AT yeongkilsong lightweightwordspacingmodelbasedonshorttextmessagesforsocialnetworkinginsmarthomes AT harksookim lightweightwordspacingmodelbasedonshorttextmessagesforsocialnetworkinginsmarthomes |