International Journal of Advanced Intelligence Volume 2, Number 1, pp.1-14, July, 2010. c AIA International Advanced Information Institute From Cloud Computing to Language Engineering, Affective Computing and Advanced Intelligence Fuji Ren The University of Tokushima, Tokushima, 770-8506 Japan ren@is.tokushima-u.ac.jp Received (January 2010) Revised (May 2010) This paper discusses the definition, intension, and extension of language engineering, affective computing, and advanced intelligence, as well as the relationship among the three fields. By reporting the latest progress and future prospects, we attempt to unify language engineering and affective computing with the concept of advanced intelligence. Cloud computing has recently become a very popular topic. Instead of discussing the concept and intension of cloud computing, this paper focuses on how progress in language engineering, including natural language processing and natural language understanding, will enormously aid in the achievement of cloud computing. It particularly deals with how to construct clouds, how to sweep clouds, and how to predict and exploit clouds. Another concept discussed in this paper is affective computing. To a large extent, this is a breakthrough in advanced intelligence. Here, it refers to a high fusion of natural and artificial intelligence, and depends on the emotional capacity entrusted to the computer, including the capability of affective recognition and affective generation. Keywords: Language engineering; Affective computing; Advanced intelligence; Cloud computing. 1. Starting with Cloud : 以 云 开 始 On September 2, 2009, while flight CA164 was flying through the clouds and fog from Osaka to Shanghai, I was calmly seated reading the NIKKEI Japanese newspaper. Most pages were occupied by reports on the overwhelming victory of the Democratic Party of Japan in the general election of the House of Representatives in Japan. However, the news on pages 1 and 9 attracted my full attention: Google was actively engaged in the purchase of a business that had cloud computing as its main focus; with this purchase, the company was heading from the PC era to the new cloud era. The News reported an interview with Eric Schmidt, Google s Chief Executive Officer (CEO), who unveiled Google s strategy and grand plan for cloud computing. I was not highly interested in cloud computing before this, considering the concept to be just commercial hype and not worth a large amount of This paper is written based on the invited speech of CAAI-13 in Chinese. 1
2 Fuji Ren effort academically. Now, it seems that the concept of cloud computing has captured massive interest, as demonstrated by 云 (Cloud) 集 景 从 (gigantic and vigorous, responds quickly), 云 天 雾 地 (confused in the puzzles), 云 舒 霞 卷 (diverse poses, iridescent colors), and 人 云 亦 云 (parroting). Therefore, this paper on CAAI-13 begins with the cloud. The main scientific argument presented in this paper supports the realization of cloud computing, including how to construct the cloud, how to sweep the cloud, and how to forecast and exploit the cloud, all of which rely enormously on the development of language engineering. However, cloud computing cannot provide effective services without a friendly human machine natural language interface. For a long time, the field of information processing, or even the field of computing, has been concerned only with data information, while neglecting sentimental information. In many cases, sentiment has even been cast as the opposite of intelligence. However, information without sentiment is incomplete, because a conversation without sentiment cannot achieve the desired effect, not to mention applications that require advanced information processing, such as geriatric nursing, patient care, psychological consultations, and service centers. Japan is the most developed country in terms of robot research and applications; however, robots have not entered into families and hospitals, as had been anticipated. The most probable reason for this is that people have a natural resistance to services provided by emotionless machines. This paper contends that the real understanding of natural language and the fulfillment of cloud computing cannot be reached without dealing with the significant sentimental factor. This paper points out that the achievement and enjoyment of cloud computing is highly reliant on breakthroughs in advanced intelligence. In this paper, advanced intelligence refers to the high level of interaction between natural intelligence and artificial intelligence. It should be noted that this paper is not aimed at studying the intension and extension of cloud computing. However, for ease of reading, Section 2 gives a brief introduction to the definition of cloud computing. Section 3 describes the state of the art techniques in language engineering and super function based natural language processing. Section 4 provides some insight into affective computing, introducing the proposed mental state transition network, and the sentiment recognition and generation models. Section 5 presents an ecology graph of advanced intelligence by the integration of cloud computing, language engineering, and affective computing. Finally, Section 6 provides a conclusion and discusses future work. 2. Computational Cloud in the Sky: 天 边 飘 来 一 朵 计 算 的 云 Once upon a time, a cloud floated by, which was called a computing cloud or cloud computing. What is cloud computing? Until now, there has been no universally accepted definition. Cloud computing can be viewed as a new mode of business computing that
From Cloud computing to Language Engineering, Affective Computing and Advanced Intelligence 3 deploys software and data via the Internet. Software and data are stored in data centers on hardware that are extremely efficient in supporting the load. Users can leverage software and applications at a much lower cost than constructing their own systems. Cloud computing can effectively build computing tasks on resource pools with tremendously reliable hardware, enabling systems to access the scalable computing power, storages, and services as needed. Resource pools can take the form of data pools, software pools, computing pools, or even capability support pools, or any other future strategy that supports pools. Such types of resource pools are called clouds. The term cloud is used because a resource pool shares some features with clouds that waft across the sky as masses of condensed water droplets and frozen crystals. Clouds are massive blocks and dynamically extensible, with fuzzy boundaries and irregular shapes. Furthermore, clouds drift as puffs in the sky, so that people cannot exactly grasp where they are and when they come and go, but they are there when you look up into the sky. They have various types: iridescent clouds, auspicious clouds, dark clouds, heavy clouds, and fantastic and outrageous clouds, which properly describe resource pools in the cloud era. On the Internet, the term cloud has been widely used since one of the Internet frontier companies, Amazon, referred to network computing as the Elastic Compute Cloud (EC2) and gained considerable business success with the term. Cloud computing can be regarded as the development of distributed processing, parallel processing, and network computers, or even as a commercial product of computer science. The paradigm of cloud computing can take the form of gathering resources (often on a huge scale, such as the corpus required for multilanguage translation), or of computing power and the inference engine in a network of distributed computers (note that it is not the end computer or local server). Customers can access the cheap services delivered by cloud computing at any place (as long as they can see the sky ) and any time (unless a solar eclipse and power outage occur simultaneously). It is exciting to forecast that only a laptop or cell phone is required to access a service through the Internet. In principle, cloud computing is not a new concept. People who are users of mail servers, such as Hotmail or Gmail, enjoy the services of cloud computing. Even if you are away on business in Hokkaido or Las Vegas, you can receive and send mail via the Internet. You can also share photos of the Grand Canyon or videos of a birthday party taken minutes ago with your family and friends who are not present. This means that you are already living in the clouds. Recently, research into cloud computing has been greatly boosted by various events in which, for example, IBM proposed the Blue Cloud in 2007, Microsoft published Windows Azure in 2008, and Google initiated the commercial incorporation of the cloud mentioned above. In addition, many academic societies have established cloud computing specialized committees, and enterprises have begun to construct their own business clouds. The Kasumigaseki Cloud plan (with a
4 Fuji Ren budget of 20.74 billion yen), which was launched by the Ministry of Internal Affairs and Communications of Japan (MIC), aims at building a huge infrastructure for cloud computing in order to provide information and technical systems for government operation. A mid-term paper was published on August 10, 2009, and the new infrastructure will be completed in 2015, with the goal of equipping all of the IT systems of ministries and government offices into one cloud computing infrastructure. Meanwhile, the Japanese MIC has announced the founding of a certification authority for constructing self-governing clouds. The First China Cloud Computing Conference was held on May 22, 2009 in Beijing, China. From just a glance at the attendees, which range from government officials to academic society chairmen, and from enterprise CEOs to university professors, we can forecast both the glory of cloud computing and its extreme importance for China. However, current research on cloud computing has focused mainly on attempts to construct resource pools, including data, platforms, software, and so on. In fact, real cloud computing should be built upon the basis of natural language understanding, because the attainment of cloud computing should rely on lightweight devices (such as cell phones) to access services, and not on traditional facilities (such as heavyweight PCs). Therefore, speech recognition and language understanding are essential. The language grasped by human beings is not Java or C++, rather natural languages such as English, Chinese, Japanese, and so on. For this reason, the new U.S. president Barack Obama changed the phrase The Digital Earth, which was proposed by Al Gore, to The Smarter Planet. Cloud computing cannot be called a real service if it does not transfer binary data to concepts that people can understand. The main reason why cloud computing is experiencing such a rapid pace of innovation and practice in the information technology field is the rapid development of computing, storage, and communication technology, as well as innovations in artificial intelligence. The current technology has basically achieved the transfer from data to information, and from information to knowledge. However, there is still no big breakthrough in theory and engineering to implement the transfer from knowledge to intelligence, without which it cannot become a service in the true sense. Imagine the following scenario: a lady who wants to do fitness exercises tells her requirements to her cell phone in Chinese. Such a query is processed by a speech recognition engine and a language understanding engine, and then the cell phone retrieves the fitness cloud via the Internet. The fitness cloud searches for related information from the huge clouds and generates knowledge about human body structure, obesity causes, chemical reactions, etc. Such knowledge is indeed true; however, is it helpful to the lady? Therefore, we consider that the conversion from knowledge to intelligent strategy is critical for accomplishing real services. In this example, the service provider should tell the lady what to do, for example, 15 minutes of morning jogging, swimming for one hour per week, or 20 minutes of
From Cloud computing to Language Engineering, Affective Computing and Advanced Intelligence 5 evening walking. This illustrates that intelligent strategy reasoning involves both the customer (natural intelligence) and an inference engine (artificial intelligence). This is the so-called advanced intelligence. The value of cloud computing v can be described as v = d s (1 + n) (1 + a i ) 2 (1) where d signifies the value of resource pools, s the value of service, n the value of language understanding, and a i the value of advanced intelligence. 3. NLP Based on Super Function: 云 树 之 思 (Memories of a friend far away) 与 云 屯 席 卷 (Things come and go fast and imposingly) Out of professional habit, after deciding to begin this paper with the word cloud, we collected the words, phrases, and sentences related to cloud in Chinese characters from a Chinese dictionary and the Internet, and annotated them with eight types of sentiments. The 345 terms were selected and processed by our sentiment analysis tool. The detailed results are not presented here because of space limitations. How can you enable computers to understand and process natural language? For Chinese language, the four general steps are word segmentation, syntactic parsing, semantic analysis, and context analysis. A real understanding of natural language also requires scene and pragmatic analysis. The Chinese idiom 云 树 之 思 (Memories of a friend far away) can be segmented as 云 / 树 / 之 / 思, 云 树 / 之 / 思, or 云 树 之 思. For the purpose of machine translation, a phrase such as 云 树 之 思 should be loaded in the dictionary as one item. However, among the various usages of the word 云 树, it can also be used as a person s name, which causes ambiguity for language processing. In addition, the computer understanding of idioms has not yet been well solved. The idiom 云 屯 席 卷 (Things come and go fast and imposingly) has two meanings in the Chinese dictionary: one is used for describing an astronomical phenomenon in which clouds gather suddenly, and the other is used as a metaphor, meaning that things come and go fast and imposingly. The contextual environment must be understood in order to clarify such types of semantic ambiguities. The goal of complete natural language understanding for machines (like human beings) will not be reached overnight. Therefore, we advocate language engineering, which regards natural language as a function family, whose input and output vary with different applications, and whose required resources differ accordingly. Such functions are different from functions in the traditional sense, in that they may be linear or nonlinear, with discrete or continuous features, and they can be described by both fuzzy math and absolute values. For these reasons, we call this function family Super Function (SF). The paradigm is illustrated in Fig. 1.
6 Fuji Ren Input Super Function Output Sources Fig. 1. Super Function in language engineering. In a machine translation system, a super function 1 is the one that shows the correspondence between the source language and the target language. The correspondence can exist in different levels in words, phrases, sentences, paragraphs, or even the complete text, and the parameters in the super function can be nested. Fig. 2 illustrates the architecture of machine translation based on a simple super function. This system was proved to be effective when evaluated on a Japanese- English translation task 2. Besides its application in machine translation, the super function was also implemented in a touring assistance system, an English composition support system 3, and a question answering (QA) system 4. Note that we introduce semantic expansion to understand queries during the development of the QA system, and utilize pragmatic information while building the search engine for Chinese classics, such as The Analects of Confucius 5. Cloud computing without natural language is pale, 云 里 雾 里 (confused as if falling into the cloud and fog), and 云 雾 茫 茫 (perplexed as if falling into vast clouds). It can see the realm of 云 蒸 霞 蔚 (Delightful scenery as if rising up and gathering together like clouds) and 云 开 雾 释 (become to peace from anxiety, as if the weather turns to bright from dark) only by basing cloud computing on natural language processing and understanding. 4. Affective Computing: 云 心 水 性 (Flirting and caprice in nature) 与 云 容 月 貌 (Elegance and delicacy in manner) This section presents sentiment recognition and generation based on the mental state transition network. To begin this section, following are some words from an invited speech by BUPT in 2004: The Internet is ubiquitous, spreading over the Earth and extending into space. However, the data flow on the Internet is digital information without sentiment. Only when information is integrated with sentiment will it become true intelli-
From Cloud computing to Language Engineering, Affective Computing and Advanced Intelligence 7 text language in source Morphology analysis SF matching Bilingual Dictionary SF base text language in target Morphology revision SF mapping Fig. 2. Machine translation based on Super Function. gence. a In life, we all have strong experiences regarding sentiment. For example, the same dialog and content will exhibit different functions and meanings to different people in different environments. Sentiment understanding and processing are key components in intelligence science and technology. We may likely discover emotion entropy and an emotion transition network, much like discovering information entropy and energy conversion law. As mentioned earlier, achievement of the desired cloud computing services is inseparable from emotional processing. Without emotion information, it is difficult to achieve a harmonic and natural man-machine interface for applications such as patient care, geriatric nursing, call centers, psychological consultation, and human communication. Because of this, cloud computing cannot offer satisfactory service to customers. However, it is still an open problem to enable computers to take real advantage of emotion. Take the idioms 云 心 水 性 (flirting and caprice in nature) and 云 容 月 貌 (elegance and delicacy in manner). To serve as examples, the sentiment result given by seven annotators is shown in Table 1, in which we also attach the results of several other words that appear in the following sections. Although the annotation did not take the context into consideration, we realize that the sentiment of language can be recognized cognitively, which is why it is a Fuji Ren, Natural Language Processing and Understanding, http://www.bupt.edu.cn/quick/ link/www/lecture/index.html.
8 Fuji Ren Table 1. Sentiment statistics of 云 心 水 性 and 云 容 月 貌. sentiment Expect Joy Love Surprise Anxiety Sorrow Angry Hate 云 心 水 性 (YUN XIN SHUI XING) 0.00 0.00 0.00 0.00 0.00 0.00 0.40 0.75 云 容 月 貌 (YUN RONG YUE MAO) 0.00 0.00 0.75 0.00 0.00 0.00 0.00 0.00 Statistics of other Chinese words containing 云 (cloud) cited in the paper 人 云 亦 云 (REN YUN YI YUN) 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.61 腾 云 驾 雾 (TENG YUN JIA WU) 0.00 0.30 0.35 0.17 0.05 0.00 0.00 0.00 云 飞 烟 灭 (YUN FEI YAN MIE) 0.00 0.00 0.00 0.00 0.00 0.53 0.00 0.00 云 集 景 从 (YUN JI JING CONG) 0.00 0.30 0.57 0.10 0.00 0.00 0.00 0.00 云 开 雾 释 (YUN KAI WU SHI) 0.00 0.52 0.00 0.00 0.00 0.00 0.00 0.00 云 舒 霞 卷 (YUN SHU XIA JUAN) 0.00 0.00 0.70 0.00 0.00 0.00 0.00 0.00 云 树 之 思 (YUN SHU ZHI SI) 0.06 0.00 0.34 0.00 0.15 0.33 0.00 0.00 云 天 雾 地 (YUN TIAN WU DI) 0.06 0.00 0.34 0.00 0.15 0.00 0.15 0.65 云 屯 席 卷 (YUN TUN XI JUAN) 0.00 0.00 0.57 0.00 0.00 0.00 0.00 0.00 云 雾 茫 茫 (YUN WU MANG MANG) 0.00 0.00 0.07 0.00 0.14 0.14 0.00 0.00 云 蒸 龙 变 (YUN ZHENG LONG BIAN) 0.13 0.30 0.28 0.00 0.00 0.00 0.00 0.00 云 蒸 霞 蔚 (YUN ZHENG XIA WEI) 0.00 0.12 0.65 0.00 0.00 0.00 0.00 0.00 useful in human communication. How do we enable computers to recognize human sentiment? We have proposed a model for emotion recognition and generation based on a mental state transition network 6. Suppose that human emotion can be divided into limited mental states and that using external stimuli, we can transfer from one state to another. Such transition possibility can be recovered through the observation of huge learning samples. This is a called mental state transition network, which is illustrated in Fig. 3. Based on the mental state transition network, we propose a model for emotion recognition and generation, as shown in Fig. 4. Currently, the external stimuli include language, speech, and facial expressions. Because the mental state transition network is built upon the average probability distribution of groups of people, we can import a personalized database into the system. Given the external stimuli, the system can predicate the emotional state of people through the mental state transition network. The integration of language, speech, and facial expressions will depend on the means of receiving external stimuli 7. It can be observed from Fig. 4 that resource data is essential for calculating the emotional state from language, speech, and facial expressions. Our research group is making an effort to construct a database for facial expression recognition, and has published an emotion corpus that deals with the analysis of Chinese blogs 8 b. The research on speech and the emotion correlation database is ongoing. b More details can be found at http://a1-www.is.tokushima-u.ac.jp/member/ren/rencecps1.0/ Ren CECps1.0.html
From Cloud computing to Language Engineering, Affective Computing and Advanced Intelligence 9 a11 Joy 1 a12 2 a22 a88 Love Anxiety 8 a18 a21 a81 a23 a16a61 a32 a27/a72natural a25a52 Surprise 3 a78a87 Origin a14 0 Hate 7 a85 a58a26/a62 a41a36 a34a43 a67 a47 a63 Anxiety 4 a76 a74 a15 a77 a51 Angry 6 a56 a65 Sorrow 5 a45 a54 a66 a55 a33 a44 Fig. 3. Mental state transition network. As an experiment, we installed a sentiment generation engine on the robot UB in our lab. UB can capture human emotion and display different emotions accordingly, and create further reflections, such as changing topics and expressing happiness or anger. Let the efficiency of information processing be v i, and the traditional processing without sentiment analysis be v d. The efficiency of sentiment processing v a can be computed as: v i = αv d + βv a + (1 α β) v d v a, (2)
10 Fuji Ren TEXT based Emotion on language recognition information module Ontology Emotion based on recognition speech information module recognition Human emotion engine Emotion based on recognition facial expression module Corpus personalized DB Mental state transition network External Emotion interface generation Machine DB emotion Ontology engine speech TEXT voice emotion Human virtual personalized Mental state gesture DB transition network. Fig. 4. Emotion recognition and generation model. where α and β are harmonic coefficients for specific information processing. 5. Ecograph of Advanced Intelligence: 腾 云 驾 雾 (Intoxicated like walking in a cloud) and 云 蒸 龙 变 (Heroes rise up when the time is ripe) The service media of cloud computing will not be PCs but rather mobile terminals or new products such as PDAs. In the 36 years since the birth of cell phones, human life has dramatically changed. Cell phones make it easier to communicate, and thus reduce the toil of life. In recent years, additional functions such as video, radio, and network access have been installed in cell phones. At present, nearly a half of the world s population is loyal to the use of cell phones. In spite of this, we totally agree with Martin Cooper, the inventor of the cell phone, in his view that Cell phone technology is still in its infancy. Cooper has stated that future technology will enable cell phones to be applied in the fields of health and nursing, in order to detect sudden symptoms, such as infarctions, and to help users control their heart rate, body weight, and body temperature. In the field of transportation, this technology may even help to regulate citizen behavior (from Xinhua Net, September 11, 2009).
From Cloud computing to Language Engineering, Affective Computing and Advanced Intelligence 11 This year, our lab gained a Grant-in-Aid for Challenging Exploratory Research by JSPS to establish an engineering system for spiritual enrichment c. The aim of this project is to establish an engineering discipline and architecture to reduce suicides, and to control and cure depression. If such technology could be imported into a cell phone, it could influence the emotions of individuals to control conflict or even war. We believe that the current technology of the cell phone is still in its infancy because advanced intelligence has not been implemented into it. What is advanced intelligence? Various definitions for advanced intelligence have been proposed. The founder of Fuzzy Mathematics, Lotfi A. Zadeh, defined advanced intelligence as the integration of artificial and natural intelligence. The scholars attending the International Conference of AI 2006 reached the consensus that advanced intelligence is research that integrates traditional AI with computing intelligence and behavior intelligence. Fig. 5 shows the ecograph of advanced intelligence. Data Information Knowledge Language engineering Artificialinteligence Naturalinteligence intelligence Advanced Intelligent decision Intelligent action Physiological response Common sense Swarm intelligence recognition/generation Sentiment Fig. 5. Ecograph of advanced intelligence. We cite the search engine as an example in order to explain the ecograph of advanced intelligence. c See http://a1-www.is.tokushima-u.ac.jp/member/ren/richeng/chinese/index.html.
12 Fuji Ren IT giants such as Google, Yahoo!, and Microsoft have made great advances in the development of search engines. However, the main barriers that they face are understanding natural language and breaking the limits of advanced intelligence. Although a search engine can utilize various AI technologies to collect massive information from the huge pool of Internet data and extract knowledge from this information, a slightly better search engine can support simple man-machine interaction using search results from hundreds of thousands of related items of information or knowledge. The real problem arises: what is the use of the extracted information? It is impossible to ask every user to be an expert in the information field. The traditional AI (shown on the left side of the advanced intelligence ecograph) cannot offer intelligent strategy or generate intelligent behavior. From the right side of the advanced intelligence ecograph in Fig. 5, we can discover that advanced intelligence is the attainment of man-machine integration, that is, human machine organic coexistence. Because a human is an intelligent agent, he possesses various sentiments and will display physical reflection and emotions to external stimuli, and then trigger common knowledge and experience. The tens of billions of individuals constitute swarm intelligence, which exploits the natural intelligence that humans possess, and combines it with artificial intelligence entrusted to computers to form the so-called advanced intelligence, which ultimately results in intelligent strategy and behavior. Consider the following examples. With the trigger entertainment of the weekend, a system with AI may provide you with many answers, such as night clubs, bars, golf courses, fishing sites, and so on. If you are a man with mental integrity, just thinking of night clubs will cause you to have uneasy thoughts, and this will provide feedback to the AI system. When you are interested in yoga and display pleasant emotions, an advanced intelligence system will remind you of your favorite yoga center. Imagine that the door of your garage opens automatically, that your fitness gear is prepared, and that the system says Buddy, WUXING ZHUANG yoga center, let s go!. The services that we are currently considering are just the tip of the iceberg. However, we assert that real cloud computing will not be achieved without a breakthrough in advanced intelligence. Why did we choose 腾 云 驾 雾 and 云 蒸 龙 变 as the section title? This can be explained by Table 1. 腾 云 驾 雾 has the sentiment of love (0.35), happiness (0.3), surprise (0.17), and anxiety (0.05), while 云 蒸 龙 变 has the sentiment of happiness (0.3), anticipation (0.13), and love (0.28). 6. Conclusions: 云 飞 烟 灭 (Vanish) or 白 云 蓝 天 (Bright prospect) Although we literally discussed clouds and cloud computing, this paper stresses on language engineering, affective computing, and advanced intelligence. We cited cloud computing as an example, and proposed a paradigm to evaluate the value of cloud computing, which is growing linearly alongside language
From Cloud computing to Language Engineering, Affective Computing and Advanced Intelligence 13 engineering, and quadratically along side advanced intelligence. We emphasized that natural language processing and understanding were achieved through data, information, knowledge, and intelligence. We presented the significance of affective computing and proposed a value function for information processing, including data and affective processing. Finally, we provided an ecograph of advanced intelligence. The author is not an expert in the field of cloud computing but aims to point out that the achievement of cloud computing, including how to construct the cloud, how to sweep the cloud, and how to forecast and exploit the cloud, is enormously reliant on the development of language engineering and breakthroughs in advanced intelligence. Although not addressed in this paper, breakthroughs in advanced intelligence will also be required to protect cloud computing services from lightning strikes, which is one of the security problems of cloud computing. To conclude this paper, we present the sentiment statistics of Chinese terms with 云 (cloud), which were derived by the members of our sentiment research group. 0 19 40 Surprise Anxiety Sorrow Angry Hate cluster-worddistribution 13 47 Love Expect Joy 0 20 40 60 80 100 120 140 160 11 48 157 Fig. 6. Word distribution for different sentiments. sentimental terms: 325, non-sentimental terms: 20, total terms: 345 Fig. 6 shows that among 345 terms, 45% have the sentiment Love, 14% Joy, 14% Anxiety, 12% Sorrow, and although no terms belong to Angry, Hate occupies 6%. These emotions of the words with cloud can be used to describe the situation of current cloud computing, although not the slightest logical relationship exists between them.
14 Fuji Ren To avoid being dubbed a flash in the pan, cloud computing should place particular emphasis on language engineering, affective computing, and advanced intelligence. By doing so, we can forecast white clouds high in the blue sky. Acknowledgments This discussion of cloud computing has referred to many Internet pages. However, the references are not clearly identified, because the authors are anonymous and not regulated, but they require special acknowledgement for their assistance. Many thanks to the members of the sentiment research group of Ren-lab, The University of Tokushima, and Dr. Caixia Yuan for their intelligent work on collecting and annotating data. References 1. F. Ren. Super-Function Based Machine Translation, Communications of COLIPS, 9(1), pp.83-100, 1999. 2. M. Sasayama, F. Ren and S. Kuroiwa. Automatic Super-function Extraction for Translation of Spoken Dialogue, International Journal of Innovative Computing, Information and Control, 4(6), pp.1371-1382, 2008. 3. F. Ren. Machine-Aided English Writing Function in MMM Project, Journal of Asian Information-Science-Life, 2(3), pp.267-282, 2006. 4. H. Hu, P. Jiang, Fuji Ren and S. Kuroiwa. A New Question Answering System for Chinese Restricted Domain, IEICE Transactions on Information and Systems, E89-D(6), pp.1848-1859, 2006. 5. Y. Yang, S. Tsuchiya and F. Ren. Construction of Analects of Confucius Knowledge Base Including Pragmatics Information, IEEJ Trans. EIS, 129(1), pp.133-139, 2009. 6. F. Ren. Recognizing Human Emotion and Creating Machine Emotion, Invited Paper, Information, 8(1), pp.7-20, 2005. 7. F. Ren. Affective Information Processing and Recognizing Human Emotion, Electronic Notes in Theoretical Computer Science, 225(2009), pp.39-50, 2009. 8. C. Quan and F. Ren. A Blog Emotion Corpus for Emotional Expression Analysis in Chinese, Computer Speech and Language, 24(1), pp.726-749, 2010. Fuji Ren (Member) He received the Ph.D. Degree in 1991 from Faculty of Engineering, Hokkaido University, Japan. He worked at CSK, Japan, where he was a chief researcher of NLP from 1991. From 1994 to 2000, he was an associate professor in the Faculty of Information Sciences, Hiroshima City University. He became a professor in the faculty of engineering, the University of Tokushima in 2001. His research interests include Natural Language Processing, Artificial Intelligence, Language Understanding and Communication, and Affective Computing. He is a member of the CAAI, IEEJ, IPSJ, JSAI, AAMT and a senior member of IEEE, IEICE. He is a Fellow of The Japan Federation of Engineering Societies. He is the President of International Advanced Information Institute.