Efficient appointment information extraction from short messages in mobile devices with limited hardware resources

Seon, Choong-Nyoung; Kim, Harksoo; Seo, Jungyun

doi:10.1016/j.patrec.2010.09.029

Cited by 5 publications

(2 citation statements)

References 13 publications

(12 reference statements)

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“…However, the initial implementation for rule-based systems is high and rule-based systems are unfeasible when there are too many rules to manage. To address these limitations, ML-based systems have been implemented that primarily utilize supervised learning models to collect statistical information from a large annotated corpus and determine NE classes based on this information [3][4][5][6][7][8][9][10]. Recently, ML-based systems that implement well-known supervised learning models have been developed to improve the accuracy of NER systems.…”

Section: Previous Workmentioning

confidence: 99%

Low-Cost Implementation of a Named Entity Recognition System for Voice-Activated Human-Appliance Interfaces in a Smart Home

Park

Kim

2018

Sustainability

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When we develop voice-activated human-appliance interface systems in smart homes, named entity recognition (NER) is an essential tool for extracting execution targets from natural language commands. Previous studies on NER systems generally include supervised machine-learning methods that require a substantial amount of human-annotated training corpus. In the smart home environment, categories of named entities should be defined according to voice-activated devices (e.g., food names for refrigerators and song titles for music players). The previous machine-learning methods make it difficult to change categories of named entities because a large amount of the training corpus should be newly constructed by hand. To address this problem, we present a semi-supervised NER system to minimize the time-consuming and labor-intensive task of constructing the training corpus. Our system uses distant supervision methods with two kinds of auto-labeling processes: auto-labeling based on heuristic rules for single-class named entity corpus generation and auto-labeling based on a pre-trained single-class NER model for multi-class named entity corpus generation. Then, our system improves NER accuracy by using a bagging-based active learning method. In our experiments that included a generic domain that featured 11 named entity classes and a context-specific domain about baseball that featured 21 named entity classes, our system demonstrated good performances in both domains, with F1-measures of 0.777 and 0.958, respectively. Since our system was built from a relatively small human-annotated training corpus, we believe it is a viable alternative to current NER systems in smart home environments.

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Section: Previous Workmentioning

confidence: 99%

Low-Cost Implementation of a Named Entity Recognition System for Voice-Activated Human-Appliance Interfaces in a Smart Home

Park

Kim

2018

Sustainability

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“…Many information appliances have limited input and output capabilities, limited bandwidth, limited memory, limited battery capacity, and restricted processing power. In spite of these hardware limitations, lightweight NLP applications (e.g., a tweet clustering system [4], an appointment extraction system [5], a news summarization system (http://summly.com), and a translation assistance system [6]) and NLP-based smart home applications (e.g., a smart home security system [2] and a smart home social networking system [1]) have been successfully developed. To implement these NLP-based applications, words in a sentence should first be tokenized.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Word Spacing Model Based on Short Text Messages for Social Networking in Smart Homes

Song

Kim

2014

International Journal of Distributed Sensor Networks

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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.

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Integration of complex language models in ASR and LU systems

Justo

Torres

2014

Pattern Anal Applic

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Efficient appointment information extraction from short messages in mobile devices with limited hardware resources

Cited by 5 publications

References 13 publications

Low-Cost Implementation of a Named Entity Recognition System for Voice-Activated Human-Appliance Interfaces in a Smart Home

Low-Cost Implementation of a Named Entity Recognition System for Voice-Activated Human-Appliance Interfaces in a Smart Home

Lightweight Word Spacing Model Based on Short Text Messages for Social Networking in Smart Homes

Integration of complex language models in ASR and LU systems

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