Research ArticleLightweight Morphological Analysis Model for Smart HomeApplications Based on Natural Language Interfaces
Sangwoo Kang,1 Harksoo Kim,2 Hyun-Kyu Kang,3 and Jungyun Seo1
1 Department of Computer Science and Engineering, Sogang University, Seoul 121-742, Republic of Korea2 Program of Computer and Communications Engineering, Kangwon National University, Chuncheon-si,Gangwon-do 200-701, Republic of Korea
3 Department of Computer Engineering, Konkuk University, Chungju-si, Chungcheongbuk-do 380-701, Republic of Korea
Correspondence should be addressed to Harksoo Kim; [email protected]
Received 10 December 2013; Revised 19 February 2014; Accepted 8 March 2014; Published 23 June 2014
With the rapid evolution of the smart home environment, the demand for natural language processing (NLP) applications oninformation appliances is increasing. However, it is not easy to embed NLP-based applications in information appliances becausemost information appliances have hardware constraints such as small memory, limited battery capacity, and restricted processingpower. In this paper, we propose a lightweight morphological analysis model, which provides the first step module of NLP formany languages. To overcome hardware constraints, the proposed model modifies a well-known left-longest-match-preference(LLMP) model and simplifies a conventional hidden Markov model (HMM). In the experiments, the proposed model exhibitedgood performance (a response time of 0.0195 sec per sentence, a memory usage of 1.85MB, a precision of 92%, and a recall rateof 90%) in terms of the various evaluation measures. On the basis of these experiments, we conclude that the proposed model issuitable for natural language interfaces of information appliances with many hardware limitations because it requires less memoryand consumes less battery power.
1. Introduction
A smart home is a home in which all systems worktogether to make residents’ lives better with more control.In smart homes, household appliances are being rapidlyevolved into information appliances (e.g., smartphones andpersonal digital assistants (PDAs)), which are usable for thepurposes of computing, telecommunicating, reproducing,and presenting encoded information in myriad forms andapplications. The information appliances will play importantroles in the improvement of the quality of life, safety, andsecurity as well as the communication possibilities with theoutside world [1]. Therefore, future information applianceswill interact with residents via social networking services(SNS) such as Twitter (http://www.twitter.com/), Facebook(http://www.facebook.com/), and Line (http://line.me/en/)[2, 3], as shown in Figure 1.
To implement such interactions via social networking,information appliances need to be enabled to aWeb server ora gateway. Recent approaches have shownmethods to embedWeb servers directly in resource-constrained devices [2]. Asshown in Figure 1, some information appliances in whichembedded Web servers will be registered as users’ friends.Then, the registered information appliances will executevarious commands that are received from users via socialnetworking services. To realize such smart homes, informa-tion appliances should understand users’ natural languagecommands which are in the form of short text messages (e.g.,tweets and recognized speech inputs) [2]. Natural languageprocessing (NLP) techniques can be used to convert a naturallanguage into a formal language that information appliancescan understand [4], as shown in Figure 2.
As shown in Figure 2, a morphological analyzer segmentsan input sentence into a sequence of words and annotates
Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2014, Article ID 570634, 9 pageshttp://dx.doi.org/10.1155/2014/570634
2 International Journal of Distributed Sensor Networks
User: current temperature.Air conditioner: 30∘C.User: set 25∘C in 2 hours.Air conditioner: oK.
Air conditioner: [alert]some smoke and motionare detected!
User: turn on CCTV andtrace the motion.
NL commands
NL commands
NL responses
NL responses
Motion info.
Smoke info.
Motion sensor
Temperature info.
Temperature sensorSmoke sensor· · ·
∗NL: natural language NLP: natural language processing
Social network services
Web server (+NLP module for command routing)
CCTV (+gateway and NLP module)
Air conditioner (+gateway and NLP module)
Figure 1: Scenarios of smart home services via social networking.
in/preposition 2/number
Temp: 25∘CTime: 14:30
hour+s/plural-noun
SNS: sending a sentence Web server: receiving and routing a sentence Information appliance: analyzing and executing a sentence
NLP module
Morphological analyzer
Named entity recognizer
Semantic and speech act analyzer
Set 25∘C in 2 hours.
Set/verb 25/number ∘C/symbol
25∘C/temperature 2 hours/time
request Set (temperature = 25∘C, time = 14:30)and
Figure 2: Example of natural language processing in smart home interactions.
the segmented words with part-of-speech (POS) tags. Ininflective languages, the major goal of the segmentationprocess is to find roots of words (e.g., hours = hour+s/plural-noun). In noninflective languages such as Chinese, the majorgoal of the segmentation process is to correctly split acompound word in a sequence of morphemes (e.g., 美 人=美 (America) +人(people)). A named entity recognizergroups some words into meaningful units (e.g., temperatureand time). A semantic and speech act analyzer generates amachine-readable semantic form (e.g., set (temperature = 25,time = 14:30)) and identifies user’s intention that is implied inan input sentence (e.g., request). As shown in the NLP steps,the initial step in the development of NLP-based applicationsis to implement a high-performance morphological analyzer(i.e., a morpheme segmentation and part-of-speech (POS)tagging system). However, this implementation is not easybecause many information appliances have limited input andoutput capabilities, limited bandwidth, limited memory, lim-ited battery capacity, and restricted processing power. These
hardware limitations make it difficult to use the well-knownmorphological analysis models that require complex compu-tations on a large amount of training data. Although manyhigh-performance information appliances are developed atpresent, lightweight morphological analyzers are still neededto efficiently realize high-level NLP applications becausehigh-level linguistic models (e.g., named entity recognition,semantic analysis, speech act analysis, and so on) requirelarge memory and high-performance processor. To resolvethis problem, we propose a morpheme segmentation andPOS tagging model that combines a rule-based method witha statistical method. The current version of the proposedsystem operates in Korean, but we believe that changing thelanguage will not be a difficult task because the system simplyuses a combination of widely used language-independentNLP techniques such as a longest-matching method and ahidden Markov model (HMM).
This paper is organized as follows. In Section 2, we reviewthe previous work on morpheme segmentation and POS
International Journal of Distributed Sensor Networks 3
tagging systems. In Section 3, we present a hybrid systemformorpheme segmentation and POS tagging in informationappliances with restricted resources. In Section 4, we reportthe result of our experiments. Finally, we draw conclusions inSection 5.
2. Related Works
Morpheme segmentation and POS tagging have been widelystudied by many researchers [5–8]. Previous morphemesegmentationmethods can be classified into two groups: rule-based models [9–12] and tabular parsing models [13]. Sincethe rule-based models are based on stemming [9, 10] orlongest matching [11, 12], they are widely used for analyticlanguages (i.e., isolating languages) with low morpheme-per-word ratios (e.g., Chinese and English). Although rule-based models are simple and exhibit decent performance,they are not appropriate for synthetic languages (i.e., aggluti-native languages) with high morpheme-per-word ratios (e.g.,Korean, Japanese, and Turkish) because various linguisticproblems occur in separating a word into a sequence ofmorphemes. Therefore, tabular parsing models are widelyused for the Korean language, although they require complexcomputations to identify all possible morpheme candidates.However, it is impractical to use these tabular parsing modelsin information appliances, which typically have restrictedprocessing power. To resolve this problem, we propose anefficientmorpheme segmentationmethod based onmodifiedlongest-match-preference rules.
The initial approaches to POS taggingwere based on rule-based models. Karlsson [14] applied constraint grammars(the grammar formalism was specified as a list of linguisticconstraints) to POS tagging. Some researchers dealt withPOS tagging as a part of syntactic analysis using rulesthat had been handcrafted on the basis of knowledge ofmorphology and intuition [15, 16]. Although these rule-basedmodels are simple and clear, they have some drawbacks.First, they require handcrafted linguistic knowledge, which isconsiderably costly to construct and maintain. Second, theycannot effectively handle unknown word patterns becausethey use lexical levels of predefined patterns. Approaches thatare designed to resolve these problems are mainly based onstatistical models. The HMM is a representative model ofstatistical POS tagging for many languages [17]. To improveperformance, some researchers have tried to apply effectivesmoothing methods or language-dependent characteristicsto a conventional HMM [17, 18]. Because these statisticalmodels can automatically obtain the necessary informationfor POS tagging, they do not require the construction andmaintenance of linguistic knowledge. In addition, they aregenerally more robust to unknown word patterns than tothe rule-based models. However, in information applianceswith a small main memory, it is impractical to use thesestatistical models because they have large memory require-ments. Conditional random fields (CRFs) and maximumentropyMarkovmodels (MEMMs) are good frameworks thatuse contextual features for building probabilistic models tosegment and label sequence data [19]. However, the strength
of these discriminative models cannot help being restrictedin information appliances with restricted processing powerbecause they generally require more complex computationsthan an HMM for parameter estimations and probabilitycalculations. Kudo et al. [20] proposed a compact CRF-based model in POS tagging of Japanese. Although Kudo’smodel showed good performances, it still requires largermemory capacity than an HMM-basedmodel because it usesadditional n-gram features in order to increase performances.In the experiments on automatic word spacing which areperformed in a commercial mobile phone with a XSCALEPXA270 CPU, 51.26 MB memory, and Windows Mobile 5.0,a CRF-based model was 2.11 times slower in response speedand 77.61 times larger in memory usage than an HMM-basedmodel. To resolve these problems, we proposed a modifiedhidden Markov model that requires much less memory forloading statistical information.
3.1. Modified Left-Longest-Match-Preference Method for Mor-pheme Segmentation. In English, a word is a spacing unit,but in Korean, an eojeol that consists of one or moremorphemes comprises a spacing unit. Therefore, for mor-phological analysis of Korean sentences, eojeol’s should befirst segmented into several morphemes. In this paper, werefer to an eojeol as a word for convenience because an eojeolplays the similar role as a word in the English language.To aid the readability of the examples, we use RomanizedKorean characters calledHangeul and insert hyphen symbolsbetween Korean characters called eumjeol’s. The segmentedmorphemes can then be recovered into their lemma forms(i.e., lexical roots). To perform these processes in informationappliances, we propose a method based on modified left-longest-match-preference (LLMP) rules. The conventionalLLMP model scans an input word from left to right andmatches the input word against each key in a morphemedictionary. Then, it returns a lemma form of the longest-matched key and continues to scan the remainder of theinput word. If a lemma has various POSs, the LLMP modelassigns the most frequent POS to the lemma. Owing to thecharacteristic of longest matching, the conventional LLMPmodel cannot find allmorpheme candidates in an inputword,as shown in Figure 3.
In Figure 3, the correct morpheme sequence of “jip-gwon-han (doing the seizure of power)” is “jip-gwon (seizureof power)/noun + ha (do)/verb suffix + n (-ing)/ending”in this context. However, the conventional LLMP modelonly returns “jip-gwon (seizure of power)/noun + han(hate)/noun” because “han (hate)/noun” is a longer mor-pheme than “ha (do)/verb suffix” and “n (-ing)/ending.”We refer to short morphemes that are covered by longmorphemes as hidden morphemes. To increase the recallrate of morphological analysis by resolving this hiddenmorpheme problem, we modify the LLMP model by addingsupplementary rules for finding hidden morphemes. Toconstruct the supplementary rules, we first implemented
4 International Journal of Distributed Sensor Networks
gwon-han (authority)/noun
gwon (volume)/unit noun
han (hate)/noun
Character 1: jip Character 2: gwon Character 3: han
jip-gwon (seizure of power)/noun
Morphemes hidden by han (hate)
Two morphemes generated by the conventional LIMP model
ha (do)
ha (do)
/adjective suffix
/verb suffix
jip (house)/noun
/proper noun
n (-ing)/ending
jip (Jeep)
Figure 3: Example of the wrong left longest match.
Table 1: Subset of the decomposition rules.
Target morpheme Morpheme sequence Frequencyhan ha (do)/adjective suffix + 𝑛 (-ing)/ending 18206 (5.56%)han ha (do)/verb suffix + 𝑛 (-ing)/ending 9865 (3.01%)ha-go ha (reason)/verb suffix + go (and)/ending 9494 (2.90%)hal ha (do)/verb suffix + 𝑙 (-ing)/ending 8816 (2.69%)jeog-𝑖𝑛 jeog (having)/noun suffix + 𝑖 (be)/copula + 𝑛 (-ng)/ending 7615 (2.32%)deul-𝑖 deul (-s)/noun suffix + 𝑖 (subject case)/propositional word 7085 (2.16%)
a Korean morpheme segmentation system based on theLLMP model. Second, we annotated alarge Korean corpususing the morpheme segmentation system. By comparingthe results of automatic annotation with the correct resultsof human annotation, we automatically collected the caseswhere a long morpheme should be divided into a set ofshorter morphemes. Finally, we selected the top-n cases thatmost frequently occurred and represented each case usingsymbolic rules, as listed in Table 1. We refer to these symbolicrules as decomposition rules.
By using the decomposition rules, the modified LLMPmodel adds hidden morphemes to the results obtained bythe initial analysis, performed using a conventional LLMPmodel. For example, the modified LLMP model matchesthe longest-match morpheme “han (hate)” against “han(hate)” → “ha (do)/verb suffix + n (-ing)/ending” and “han(hate)” → “ha (do)/adjective suffix + n (-ing)/ending” in thedecomposition rules.Then, it adds “ha (do)/verb suffix + n (-ing)/ending” and “ha (do)/adjective suffix + n (-ing)/ending”to the original morpheme sequence, as shown in Figure 4.
3.2. Simplified HMM for POS Tagging. Let𝑊1,𝑛
denote a sen-tence that consists of a sequence of 𝑛 words, 𝑤
1, 𝑤2, . . . , 𝑤
𝑛,
and let𝑇1,𝑛
denote the POS tag sequence, 𝑡1, 𝑡2, . . . , 𝑡
𝑛, of𝐶1,𝑛.
The tagging problem can then be formally defined as finding𝑇1,𝑛, which results in
𝑇 (𝑊1,𝑛)
def= argmax𝑇1,𝑛
𝑃 (𝑇1,𝑛
| 𝑊1,𝑛) = argmax
𝑇1,𝑛
𝑃 (𝑇1,𝑛,𝑊1,𝑛)
𝑃 (𝑊1,𝑛)
= argmax𝑇1,𝑛
𝑃 (𝑇1,𝑛,𝑊1,𝑛) .
(1)
In (1),𝑃(𝑊1,𝑛) is dropped as it is constant for all𝑇
1,𝑛terms.
Next, (1) is broken into smaller pieces to collect statisticsabout each piece, as shown in
𝑃 (𝑇1,𝑛,𝑊1,𝑛) =
𝑛
∏
𝑖=1
𝑃 (𝑤𝑖| 𝑡1,𝑖, 𝑤1,𝑖−1
) 𝑃 (𝑡𝑖| 𝑡1,𝑖−1
, 𝑤1,𝑖−1
) .
(2)
Equation (2) is simplified bymaking two assumptions: thecurrent POS tag is dependent only upon the previous POS tagand the current word is only affected by its POS tag. Equation(3) is a well-known HMMmodel for POS tagging:
𝑃 (𝑇1,𝑛,𝑊1,𝑛) ≈
𝑛
∏
𝑖=1
𝑃 (𝑤𝑖| 𝑡𝑖) 𝑃 (𝑡𝑖| 𝑡𝑖−1) . (3)
International Journal of Distributed Sensor Networks 5
18,206
9,86han (hate)/noun 5
9,494
Character 1: jip Character 2: gwon Character 3: han
FrequencyMorpheme sequenceTarget morpheme
· · · · · · · · ·
· · · · · · · · ·
jip-gwon (seizure of power)/noun
ha (do)
ha (do)/ending
ha-go (reason)
/adjective suffix
/verb suffix
n (-ing)
han (hate)/noun
This is no decomposition rule for “jip-gwon”
han (hate)
Decomposition rules
ha (do)/adjective suffix + n (-ing)/ending
ha (do)/verb suffix + n (-ing)/ending
ha (do)/verb suffix + go (and)/ending
Figure 4: Processing example of the modified LLMP model.
· · ·
Word 1: chong-20-nyeon-eul Word 2: jip-gwon-han(doing the seizure of power)
In-word HMM for word 1In-word HMM for word 2
han/nounchong/noun
chong/adjec
tive
jip-gwon/noun
n/ending
ha/
Observation probability of
Observation probability of
word 1
word 2
Transition probabilitiesbetween word 1 and word 2
(= linking probabilities of all morpheme candidatesequences between word 1 and word 2)
gwonhan/noun
gwon/unit nounjip/noun
jip/propernoun
?
20/number
nyeon/unit n
eul/
eul/noun
postpositionalword
nyeon/noun
oun
ha/
/verb suffix
/adjective suffix
(for total of 20 years)
Figure 5: Example of in-word HMMs based on the tabular parsing method.
In (3), 𝑃(𝑤𝑖| 𝑡𝑖) and 𝑃(𝑡
𝑖| 𝑡𝑖−1) are called an observa-
tion probability and a transition probability, respectively. InKorean, it is difficult to calculate both the observation proba-bility and the transition probability because a word generallyconsists of multiple morphemes. Therefore, many previoussystems have in-word HMMs for calculating the observationprobabilities of words, as shown in Figure 5.
In Figure 5, the gray rectangles represent the in-wordHMMs based on the modified LLMP model. However, thesein-word HMMs require more computing power, becausethey increase the complexity of POS tagging. To resolve this
problem, we simplify the observation probability and thetransition probability calculations based on the assumptionthat the first POS tag and the last POS tag provide importantclues to syntactically connect words, as shown in
𝑃𝑘(𝑤𝑖| 𝑡𝑖) ≈
𝑚
∏
𝑗=1
(
𝑃𝑘(seg𝑀
𝑗| seg𝑗) ⋅
𝑃𝑘(seg𝑇first
𝑗| seg𝑇last
𝑗−1)
) ,
where 𝑘 = 1 . . . 𝑐,
𝑃 (𝑡𝑖| 𝑡𝑖−1) ≈ 𝑃 (𝑡
first𝑖
| 𝑡last𝑖−1) .
(4)
6 International Journal of Distributed Sensor Networks
Word 1: chong-20-nyeon-eul Word 2: jip-gwon-han(doing the seizure of power)
In-word HMM for word 1 In-word HMM for word 2
chong/adjective
eul/20/ nyeon/unit noun postpositio
nal wordjip-gwon/noun
han/noun
≈ P3(verb suffix|noun)
· · ·ha/adjective suffix + n/ending
ha/verb suffix + n/ending
number
P3(segTfirstsj |segTlast
j−1 )
P(ti|ti−1) ≈ P(tfirstsi |tlast
i−1 ) = P(noun|postpositional word)
(for total of 20 years)
Figure 6: Example of the simplified HMM based on the modified LLMP model.
In (4), seg𝑗is the 𝑗th longest-morpheme segment that
the modified LLMP model generates from the 𝑖th word, 𝑤𝑖,
and seg𝑀𝑗is a morpheme sequence of the 𝑗th longest-mor-
pheme segment. seg𝑇first𝑗
and seg𝑇last𝑗−1
are the first POS tagin the 𝑗th longest-morpheme segment and the last POS tagin the 𝑗 − 1th longest-morpheme segment, respectively. 𝑃
𝑘
is the probability of the 𝑘th morpheme among 𝑐 morphemecandidate sequences in the 𝑖thword,𝑤
𝑖⋅𝑡first𝑖
and 𝑡last𝑖−1
are a POStag of the first morpheme in the 𝑖th word,𝑤
𝑖, and a POS tag of
the last morpheme in the 𝑖−1th word,𝑤𝑖−1
. Figure 6 shows anexample of the simplifiedHMMbased on themodified LLMPmodel.
As shown in Figure 6, the transition probability between“chong-20-nyeon-eul (for total of 20 years)” and “jip-gwon-han (doing the seizure of power)” is calculated based ongrammatical possibilities between the POS tag “noun” of thefirst morpheme “jip-gwon” in the current word and the POStag “postpositional word” of the last morpheme “eul” in theprevious word. The observation probability of the word “jip-gwon-han” is calculated as the maximum score among thefollowing three probabilities:
𝑃1("𝑗𝑖𝑝-𝑔𝑤𝑜𝑛/noun" | seg
1) × 𝑃1("noun" | 0)
× 𝑃1("ℎ𝑎𝑛/noun" | seg
2) × 𝑃1("noun" | "noun") ,
𝑃2("𝑗𝑖𝑝-𝑔𝑤𝑜𝑛/noun" | seg
1) × 𝑃2("noun" | 0)
× 𝑃2("ℎ𝑎/adjective suffix + 𝑛/ending" | seg
2)
× 𝑃2("adjective suffix + ending" | "noun") ,
𝑃3("𝑗𝑖𝑝-𝑔𝑤𝑜𝑛/noun" | seg
1) × 𝑃3("noun" | 0)
× 𝑃3("ℎ𝑎/verb suffix + 𝑛/ending" | seg
2)
× 𝑃3("verb suffix + ending" | "noun") .
(5)
In the above example, we can assign 𝑃𝑖("𝑗𝑖𝑝-𝑔𝑤𝑜𝑛/
noun" | seg1) × 𝑃1("noun"0) to 1.0 without any calculation
because themorpheme sequence of the first segment is always“jip-gwon/noun.” This strategy makes the simplified HMM
use less memory. As we illustrated above through examples,the modified LLMP model ignores many morpheme candi-dates. Due to this pruning process, the simplified HMM candramatically reduce the amount of calculations required toobtain the observation probabilities. Finally, the maximumscores from (1) and (4) are calculated using the well-knownViterbi algorithm [21].
4. Experiments
4.1. Data Sets and Experimental Settings. To evaluate theproposed model experimentally, we used the 21st CenturySejong Project’s POS-tagged corpus [22]. Table 2 describes theSejong POS-tagged corpus in brief.
We divided the POS-tagged corpus into training and testdata, at a ratio of nine to one. We then performed a 10-foldcross-validation using the following evaluation measures:precision, recall rate, and F1-measure. In order to evaluatethe usefulness of the proposed model in a real informationappliance environment, we implemented it in a commercialmobile phone with a XSCALE PXA270 CPU, 51.26 MBmemory, and Windows Mobile 5.0.
4.2. Experimental Results. The first experiment performedwas intended to evaluate the changes in performance withthe proposed model, based on the number of decompositionrules.We computed the average performance of the proposedmodel at various cutoff points in Figure 7.
In Figure 7, the more rules the proposed model had, thehigher performance it obtained. However, we believe that themodel incorporating top-40% rules is the most suitable forinformation appliances because themodels havingmore rulesrequire more processing time and larger working memories,while delivering limited performance improvement overmodels with smaller rule sets.
In the second experiment, we compared the performanceof the proposed model with those that are representative ofprevious models, using the same training and testing data, aslisted in Table 3.
International Journal of Distributed Sensor Networks 7
0.85
0.86
0.87
0.88
0.89
0.90
0.91
0.92
Top n% of decomposition rules10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Figure 7: F1-measure scores at various cutoff points.
In Table 3, “LLMP” is a morphological analyzer based onconventional LLMP rules. This morphological analyzer doesnot need additional POS tagging processes because it returnsone morpheme sequence per word. “Tabular parsing +HMM” is a POS tagger based on an HMM that selects themost reasonable sequence among all possiblemorpheme can-didates generated by the tabular parsing method. The systemis one of state-of-the-art Korean morphological analyzerswhich show F
1-measures of 94∼95% [18]. “Modified LLMP +
Simplified HMM” is the proposed POS tagger that selectsthe most probable sequence among a number of morphemecandidates generated by the modified LLMP model. Aslisted in Table 3, “Tabular parsing + HMM” exhibited thebest performance in terms of all measures. However, theperformance differences between the proposed model andthe “Tabular parsing +HMM”modelweremuch smaller thanthose between the proposed model and the “LLMP.”This factreveals that the decomposition rules are very effective. On theother hand, the proposed model significantly outperformedthe “LLMP.”
In the last experiment, we compared the memory usageand response time of the above models, as listed in Table 4.
Table 4: Comparison of memory usage and response time.
Measure LLMP Tabular parsing +HMM
Modified LLMP +Simplified HMM
Memory usage(MB) 1.6 3.0 1.85
Response time(sec/sentence) 0.0154 0.1495 0.0195
As listed in Table 4, the proposed model used muchless memory and required much less processing time thanthe “Tabular parsing + HMM” model. Let N denote thenumber of eomjeol’s in an eojeol. In the scan proce-dure from left to right for matching an eojeol againsteach key in a morpheme dictionary, the tabular pars-ing model has the time complexity O(N3) because itshould check all grammatical connections between twoadjacent eomjeols in a similar manner with CYK algo-rithm (http://en.wikipedia.org/wiki/CYK algorithm). How-ever, the modified LLMP has the time complexity O(N) asalready known. Let S denote the number of observations inan HMM, and let T denote the number of transitions in anHMM. In the POS tagging procedure, the simplified HMMhas the same time complexity O(Tx|S|2) with the ordinaryHMM. However, the sizes of T and S in the simplified HMMare about 3 times smaller and about 5 times smaller thanthose in the ordinary HMM, respectively. As shown in thetime complexity analysis, “LLMP” is the most lightweightmodel exhibiting high speed. However, the proposed modelis the more suitable for information appliances because theprecision and recall rate of the “LLMP” model are too lowfor NLP applications. Although the computing power ofinformation appliances is rapidly increasing, the memoryusage and the processing time of the “Tabular parsing +HMM” model are still limiting factors for information appli-ances. For example, we can easily find many wireless sensornetwork (WSN) gateways and security appliances with 64∼256MBs. Inmany systems, the size of internal usermemory isrestricted within a few MBs. The experimental platform (i.e.,the commercial mobile phone with a XSCALE PXA270 CPU,51.26MBmemory, andWindowsMobile 5.0) had only 8MBsof internal user memory which is too little to implement NLPapplications. In addition, information appliances may useinformation retrieval (IR) techniques for extracting keywordsfrom instruction manuals or online contents. In this case,“Modified LLMP + Simplified HMM” spent 0.3 seconds perMB for indexing keywords, but “Tabular parsing + HMM”spent 85 seconds per MB for indexing keywords. In partic-ular, in mobile devices with restricted battery capacity, longprocessing times lead to rapid battery consumption. Basedon these experimental results, if computational cost andmemory limitations are important factors, the combinationof the modified LLMP model and the simplified HMM maybe one of the best solutions.
4.3. Contribution to Distributed Sensor Networks. Smarthome technology can be used in the following key areas
8 International Journal of Distributed Sensor Networks
in which various sensors should interact with each otherin order to detect residents’ behaviors and protect againstdangerous situations [3]:
(i) safety area: intruder detection, burglar deception, firedetection, video surveillance, and so on;
(ii) comport area: temperature control, light control,windows control, and so on.
To realize this smart home environment, sensor net-work systems should gather information detected by sensorsand should transmit the information to tablet terminals,gateways, or information appliances. If the sensor networksystems adopt NLP techniques (i.e., NLP techniques areembedded in sensors or gateways), they will be able tomore promptly detect various events and more accuratelydetermine their actions against events. For example, if akeyword detector based on NLP techniques is embedded ina motion sensor (or CCTV), the sensor network system cangenerate necessary actions when the keywords like “money”and “give me” are included in the conversation between anintruder and a resident or when a resident shouts “fire” whilefast moving. As a result, the proposed model can contributeto making sensor network systems better at understandingresidents’ contexts.
5. Conclusions
We proposed a morpheme segmentation and POS taggingmodel for an information appliance. To reduce the numberof morpheme candidates, the proposed model uses a methodthat expands the set of morpheme candidates generated bylongest-match-preference rules, instead of using the well-known tabular parsing method. To reduce the computationalcost and memory usage, the proposed model uses a methodthat simplifies an inner HMM, which is necessary in orderto find the correct sequence of morphemes in a word.In the experiments, the proposed model exhibited goodperformance in terms of the various evaluation measuressuch as precision, recall rate, memory usage, and responsetime. On the basis of these experiments, we conclude that theproposed model is suitable for information appliances withmany hardware limitations because it requires less memoryand consumes less battery power.
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper.
Acknowledgments
This research was supported by the IT R&D program ofMOTIE/MSIP/KEIT [10041678, The Original TechnologyDevelopment of Interactive Intelligent Personal AssistantSoftware for the Information Service on multiple domains].This research was also supported by the Basic Science
Research Program through the National Research Founda-tion of Korea (NRF) funded by the Ministry of Education,Science and Technology (2013R1A1A4A01005074).
References
[1] M. Caillet, J. F. Pessiot, M. R. Amini, and P. Gallinari, “Unsuper-vised learning with term clustering for thematic segmentationof texts,” in Proceedings of the RIAO Conference (RIAO ’04),2004.
[2] A. Kamilaris and A. Pitsillides, “Social networking of the smarthome,” in Proceedings of the 21st International Symposium onPersonal Indoor and Mobile Radio Communications (PIMRC’10), pp. 2632–2637, September 2010.
[3] M. B. Chandak and R. Dharaskar, “Natural language processingbased context sensitive, content specific architecture and itsspeech based implementation for smart home applications,”International Journal of Smart Home, vol. 4, no. 2, pp. 1–10, 2010.
[4] A. Hurson, Connected Computing Environment, AcademicPress, 2012.
[5] B. Merialdo, “Tagging English text with a probabilistic model,”Computational Linguistics, vol. 20, no. 2, pp. 155–171, 1994.
[6] E. Brill, “Transformation-based error-driven learning and natu-ral language processing: a case study in part-of-speech tagging,”Computational Linguistics, vol. 21, no. 4, pp. 543–564, 1995.
[7] T. Brants, “TnT: a statistical part-of-speech tagger,” in Proceed-ings of the Conference on Applied Natural Language Processing(ANLC ’00), pp. 224–231, Stroudsburg, Pa, USA, 2000.
[8] C. Kruengkrai, K. Uchimoto, J. Kazama, Y. Wang, K. Torisawa,and H. Isahara, “Joint ChineseWord segmentation and POStagging using an error-driven Word-character hybrid model,”IEICE Transactions on Information and Systems, vol. E92-D, no.12, pp. 2298–2305, 2009.
[9] M. Porter, “An algorithm for suffix stripping,” Program, vol. 14,no. 3, pp. 130–137, 1980.
[10] J. Lovins, “Development of a stemming algorithm,”MechanicalTranslation and Computational Linguistics, vol. 11, no. 1-2, pp.22–31, 1968.
[11] H. H. Htay and K. N. Murthy, “Myanmar word segmentationusing syllable level longest matching,” in Proceedings of the 6thWorkshop on Asian Language Resources, pp. 41–48, 2008.
[12] H. Tseng and K. Chen, “Design of Chinese morphologicalanalyzer,” in Proceedings the 1st SIGHAN workshop on ChineseLanguage Processing, vol. 18, pp. 1–7, 2002.
[13] Y. Hong, M.-W. Koo, and G. Yang, “Korean morphologicalanalyzer for speech translational system,” in Proceedings of theInternational Conference on Spoken Language Processing (ICSLP’96), pp. 673–676, October 1996.
[14] F. Karlsson, “Constraint grammar as a framework for parsingrunning text,” in Proceedings of the 13th Conference on Compu-tational linguistics, pp. 168–173, 1990.
[15] A. Voutilainen, “A syntax-based part of speech analyzer,” inProceedings of the 7th Conference of the European Chapter of theAssociation for Computational Linguistics, pp. 157–164, 1995.
[16] K. W. Church, “Stochastic parts program and noun phraseparser for unrestricted text,” in Proceedings of the Conference onApplied Natural Language Processing, pp. 136–143, 1988.
[17] E. Charniak, C. Hendrickson, N. Jacobson, and M. Perkowitz,“Equations for part-of-speech tagging,” inProceedings of the 11thNational Conference on Artificial Intelligence, pp. 784–789, July1993.
International Journal of Distributed Sensor Networks 9
[18] D. Lee and H. Rim, “Probabilistic models for Korean mor-phological analysis,” in Proceedings of the International JointConference on Natural Language Processing, pp. 197–202, 2005.
[19] J. Lafferty, A. McCallum, and F. Pereira, “Conditional ran-dom fields: probabilistic models for segmenting and labelingsequence data,” in Proceedings of the International Conferenceon Machine Learning, pp. 282–289, 2001.
[20] T. Kudo, K. Yamamotoz, and Y. Matsumoto, “Applying con-ditional random fields to Japanese morphological analysis,” inProceedings of the Conference on Empirical Methods on NaturalLanguage Processing (EMNLP ’04), pp. 230–237, 2004.
[21] G. D. Forney Jr., “The Viterbi algorithm,” Proceedings of theIEEE, vol. 61, no. 3, pp. 268–278, 1973.
[22] “The National Institute of the Korean Language: final report onachievements of 21st Sejong project: electronic dictionary,” 2007.
