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Abstrak :
This book constitutes the refereed proceedings of the First CCF Conference, NLPCC 2012, held in Beijing, China, during October/November, 2012. The 43 revised full papers presented were carefully reviewed and selected from 151 submissions. The papers are organized in topical sections on applications on language computing, fundamentals on language computing, machine translation and multi-lingual information access, NLP for search, ads and social networks, question answering and web mining.

Abstrak :
In essence, Computing with Words (CWW) is a system of computation in which the objects of computation are predominantly words, phrases and propositions drawn from a natural language. CWW is based on fuzzy logic. In science there is a deep-seated tradition of according much more respect to numbers than to words. In a fundamental way, CWW is a challenge to this tradition. What is not widely recognized is that, today, words are used in place of numbers in a wide variety of applications ranging from digital cameras and household appliances to fraud detection systems, biomedical instrumentation and subway trains. CWW offers a unique capability?the capability to precisiate natural language. Unprecisiated (raw) natural language cannot be computed with. A key concept which underlies precisiation of meaning is that of the meaning postulate: A proposition, p, is a restriction on the values which a variable, X?a variable which is implicit in p?is allowed to take. CWW has an important ramification for mathematics. Addition of the formalism of CWW to mathematics empowers mathematics to construct mathematical solutions of computational problems which are stated in a natural language. Traditional mathematics does not have this capability.

Abstrak :
Riset terdahulu menunjukkan adanya misrepresentasi identitas agama pada media Indonesia. Menurut studi sebelumnya, misrepresentasi identitas marjinal pada dataset dan word embedding untuk natural language processing dapat merugikan identitas marjinal tersebut, dan karenanya harus dimitigasi. Riset ini menganalisis keberadaan bias agama pada beberapa dataset dan word embedding NLP berbahasa Indonesia, dampak bias yang ditemukan pada downstream performance, serta proses dan dampak debiasing untuk dataset dan word embedding. Dengan menggunakan metode uji Pointwise Mutual Information  (PMI ) untuk deteksi bias pada dataset dan word similarity untuk deteksi bias pada word embedding, ditemukan bahwa dua dari tiga dataset, serta satu dari empat word embedding yang digunakan pada studi ini mengandung bias agama. Model machine learning yang dibentuk dari dataset dan word embedding yang mengandung bias agama memiliki dampak negatif untuk downstream performance model tersebut, yang direpresentasikan dengan allocation harm dan representation harm. Allocation harm direpresentasikan oleh performa false negative rate (FNR) dan false positive rate (FPR) model machine learning yang lebih buruk untuk identitas agama tertentu, sedangkan representation harm direpresentasi oleh kesalahan model dalam mengasosiasikan kalimat non-negatif yang mengandung identitas agama sebagai kalimat negatif. Metode debiasing pada dataset dan word embedding mampu memitigasi bias agama yang muncul pada dataset dan word embedding, tetapi memiliki performa yang beragam dalam mitigasi allocation dan representation harm. Dalam riset ini, akan digunakan lima metode debiasing: dataset debiasing dengan menggunakan sentence templates, dataset debiasing dengan menggunakan kalimat dari Wikipedia, word embedding debiasing dengan menggunakan Hard Debiasing,  joint debiasing dengan sentence templates, serta joint debiasing menggunakan kalimat dari Wikipedia. Dari lima metode debiasing, joint debiasing dengan sentence templates memiliki performa yang paling baik dalam mitigasi allocation harm dan representation harm. ......Previous research has shown the existence of misrepresentation regarding various religious identities in Indonesian media. Misrepresentations of other marginalized identities in natural language processing (NLP) resources have been recorded to inflict harm against such marginalized identities, and as such must be mitigated. This research analyzes several Indonesian language NLP datasets and word embeddings to see whether they contain unwanted bias, the impact of bias on downstream performance, the process of debiasing datasets or word embeddings, and the effect of debiasing on them. By using the Pointwise Mutual Test (PMI) test to detect dataset bias and word similarity to detect word embedding bias, it is found that two out of three datasets and one out of four word embeddings contain religion bias. The downstream performances of machine learning models which learn from biased datasets and word embeddings are found to be negatively impacted by the biases, represented in the form of allocation and representation harms. Allocation harm is represented by worse false negative rate (FNR) and false positive rate (TPR) of models with respect to certain religious identities, whereas representation harm is represented by the misprediction of non-negative sentences containing religious identity terms as negative sentences. Debiasing at dataset and word embedding level was found to correctly mitigate the respective biases at dataset and word embedding level. Nevertheless, depending on the dataset and word embedding used to train the model, the performance of each debiasing method can vary highly at downstream performance. This research utilizes five debiasing methods: dataset debiasing using sentence templates, dataset debiasing using sentences obtained from Wikipedia, word embedding debiasing using Hard Debiasing, joint debiasing using sentence templates, as well as joint debiasing using sentences obtained from Wikipedia. Out of all five debiasing techniques, joint debiasing using sentence templates performs the best on mitigating both allocation and representation harm.

Abstrak :
Chatbot merupakan salah satu aplikasi berupa sarana yang dapat mengoptimalkan distribusi layanan kepada pelanggan dengan meminimalkan komunikasi dengan agen manusia langsung di tingkat pertama. Percakapan chatbot dapat diterapkan melalui teks atau text-to-speech atau suara. Pertumbuhan pasar itu sendiri meningkat dalam beberapa tahun terakhir. Tujuan utama dari penulisan ini adalah untuk mempertimbangkan platform chatbot terbaik menggunakan Analytic Hierarchy Process (AHP) yang dikombinasikan dengan metode Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) untuk pasar di Indonesia. Dengan demikian, pasar dapat memperoleh keunggulan kompetitif dan mencapai kepuasan pelanggan dengan pengiriman layanan responsif yang didukung oleh platform chatbot terpilih. ...... A chatbot is one of applications which can optimize service distribution to the customer by minimizing the communication with live human agents in the first level. Chatbot’s conversation could be applied via text or text-to-speech or voice. The marketplace growth itself is mounting in past years. The main purpose of this paper is to consider the best chatbot platform using Analytic Hierarchy Process (AHP) combined with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods for the marketplace in Indonesia. Thus, the marketplace could gain a competitive advantage and achieve customer satisfaction with responsive service delivery supported by a selected chatbot platform.

Abstrak :
This book constitutes the refereed proceedings of the 8th International Conference on Advances in Natural Language Processing, JapTAL 2012, Kanazawa, Japan, in October 2012. The 27 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 42 submissions. The papers are organized in topical sections on machine translation, multilingual issues, resouces, semantic analysis, sentiment analysis, as well as speech and generation.

Abstrak :
Predicting prosody from text for text-to-speech synthesis covers the specific aspects of prosody, mainly focusing on how to predict the prosodic information from linguistic text, and then how to exploit the predicted prosodic knowledge for various speech applications. Author K. Sreenivasa Rao discusses proposed methods along with state-of-the-art techniques for the acquisition and incorporation of prosodic knowledge for developing speech systems.

Abstrak :
ABSTRAK

Pariwisata Indonesia adalah salah satu penyumbang terbesar devisa negara. Pada 2015 devisa yang dihasilkan sektor pariwisata adalah sebesar $ 12,23 miliar dan diproyeksikan bahwa pada tahun 2020 akan memberikan kontribusi devisa negara sebesar $ 20 miliar. Kemajuan teknologi secara fundamental telah mengubah cara informasi diproduksi dan digunakan untuk banyak hal termasuk di sektor pariwisata. Dalam industri pariwisata, pengalaman pelanggan penting untuk pengembangan dan reputasi industri. Diperlukan pendekatan baru untuk mengukur tingkat kepuasan pelanggan dan persepsi wisatawan melalui analisis sentimen. Dalam penelitian ini permasalahan yang menjadi perhatian adalah bagaimana memanfaatkan analisis sentimen untuk menentukan persepsi wisatawan mengenai 3A (atraksi, amenitas, dan aksesibilitas) di destinasi wisata dan mengukur korelasi antara persepsi wisatawan dengan tingkat pertumbuhan wisatawan, menggunakan metode text mining NLP (Natural Language Processing) untuk mengembangkan strategi peningkatan kunjungan wisatawan dan pengembangan destinasi wisata. Hasil dari penelitian yang dilakukan didapatkan hasil terdapat korelasi negatif yang kuat antara sentimen negatif dengan tingkat pertumbuhan kunjungan wisatawan. Tingkat pertumbuhan wisatawan akan menurun ketika sentimen negatif dari wisatawan meningkat. Penurunan tingkat pertumbuhan wisatawan berdampak pada potensi hilangnya pendapatan negara. Analisis sentimen dapat memberikan gambaran persepsi wisatawan secara lengkap terkait aspek amenitas, aksesibilitas, dan atraksi di destinasi pariwisata.

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ABSTRACT

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Indonesian tourism is one of the biggest contributors to the countrys foreign exchange. In 2015 the foreign exchange generated by the tourism sector was $ 12:23 billion and it is projected that in 2020 will Contribute to the countrys foreign exchange of $ 20 billion. Technological advances have fundamentally changed the way information is produced and used for many things Including in the tourism sector. In the tourism industry, customer experience is important for the development and reputation of the industry. A new approach is needed to measure customer satisfaction and tourist perceptions through sentiment analysis. In this study the goal is how to use sentiment analysis to Determine the perceptions of tourists regarding 3A (attractions, amenities and accessibility) in tourist destinations and measure the correlation between perceptions with tourist tourist growth rates, using the NLP (Natural Language Processing) text mining method to develop strategies for increasing tourist visits and developing tourist destinations. The results of the research Showed that there was a strong negative correlation between negative sentiment and the level of tourist tourist growth. The level of tourist growth when the negative sentiment will Decrease from tourists increases. Tourist Declining growth rates have an impact on the potential loss of state income. Sentiment analysis can provide a complete description of tourist perceptions regarding aspects of amenities, accessibility, and Attractions in tourism destinations. using the NLP (Natural Language Processing) text mining method to develop strategies for increasing tourist visits and developing tourist destinations. The results of the research Showed that there was a strong negative correlation between negative sentiment and the level of tourist tourist growth. The level of tourist growth when the negative sentiment will Decrease from tourists increases. Tourist Declining growth rates have an impact on the potential loss of state income. Sentiment analysis can provide a complete description of tourist perceptions regarding aspects of amenities, accessibility, and Attractions in tourism destinations. using the NLP (Natural Language Processing) text mining method to develop strategies for increasing tourist visits and developing tourist destinations. The results of the research Showed that there was a strong negative correlation between negative sentiment and the level of tourist tourist growth. The level of tourist growth when the negative sentiment will Decrease from tourists increases. Tourist Declining growth rates have an impact on the potential loss of state income. Sentiment analysis can provide a complete description of tourist perceptions regarding aspects of amenities, accessibility, and Attractions in tourism destinations. The results of the research Showed that there was a strong negative correlation between negative sentiment and the level of tourist tourist growth. The level of tourist growth when the negative sentiment will Decrease from tourists increases. Tourist Declining growth rates have an impact on the potential loss of state income. Sentiment analysis can provide a complete description of tourist perceptions regarding aspects of amenities, accessibility, and Attractions in tourism destinations. The results of the research Showed that there was a strong negative correlation between negative sentiment and the level of tourist tourist growth. The level of tourist growth when the negative sentiment will Decrease from tourists increases. Tourist Declining growth rates have an impact on the potential loss of state income. Sentiment analysis can provide a complete description of tourist perceptions regarding aspects of amenities, accessibility, and Attractions in tourism destinations.

 

Abstrak :
Chatbot merupakan salah satu aplikasi berupa sarana yang dapat mengoptimalkan distribusi layanan kepada pelanggan dengan meminimalkan komunikasi dengan agen manusia langsung di tingkat pertama. Percakapan chatbot dapat diterapkan melalui teks atau text-to-speech atau suara. Pertumbuhan pasar itu sendiri meningkat dalam beberapa tahun terakhir. Tujuan utama dari penulisan ini adalah untuk mempertimbangkan platform chatbot terbaik menggunakan Analytic Hierarchy Process (AHP) yang dikombinasikan dengan metode Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) untuk pasar di Indonesia. Dengan demikian, pasar dapat memperoleh keunggulan kompetitif dan mencapai kepuasan pelanggan dengan pengiriman layanan responsif yang didukung oleh platform chatbot terpilih. ......A chatbot is the most recent application which can optimize service distribution to the customer by minimizing the communication with live human agents in the first level. Chatbot’s conversation could be applied via text or text-to-speech or voice. The marketplace growth itself is mounting in past years. The main purpose of this paper is to consider the best chatbot platform using Analytic Hierarchy Process (AHP) combined with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods for the marketplace in Indonesia. Thus, the marketplace could gain a competitive advantage and achieve customer satisfaction with responsive service delivery supported by a selected chatbot platform.

Abstrak :
Penggunaan analisis sentimen semakin umum digunakan. Dalam pengembangan analisis sentimen ini banyak tantangan yang perlu dihadapi. Karena analisis ini termasuk Natural Language Processing NLP, hal yang perlu dimengerti adalah kompleksitas bahasa. Dengan berkembangnya teknologi Artificial Neural Network, ANN semakin banyak permasalahan yang bisa diselesaikan. Ada banyak contoh struktur ANN dan untuk penelitian ini yang digunakan adalah Convolutional Neural Network CNN dan Recurrent Neural Network RNN. Kedua jenis ANN tersebut sudah menunjukkan performa yang baik untuk beberapa tugas NLP. Maka akan dilakukan analisis sentimen dengan menggunakan kedua jenis ANN tersebut dan dibandingkan kedua performa ANN tersebut. Untuk data yang akan digunakan diambil dari publikasi stanford dan untuk mengubah data tersebut bisa digunakan pada ANN digunakan word2vec. Hasil dari analisis menunjukkan bahwa RNN menunjukkan hasil yang lebih baik dari CNN. Walaupun akurasi tidak terlalu terlihat perbedaan yaitu pada RNN yang mencapai 88.35 0.07 dan CNN 87.11 0.50, tetapi waktu pelatihan RNN hanya membutuhkan waktu 8.256 detik sedangkan CNN membutuhkan waktu 544.366 detik. ......Term of sentiment analysis become popular lately. There are many challenges developing sentiment analysis that need to be addressed. Because this kind analysis is including Natural Language Processing, the thing need to understand is the complexity of the language. With the current development of Artificial Neural Network ANN, more problems can be solved. There are many type of ANN and for this research Convolutional Neural Network CNN and Recurrent Neural Network will be used. Both already showing great result for several NLP tasks. Data taken from stanford publication and transform it with word2vec so could be used for ANN. The result shows that RNN is better than CNN. Even the difference of accuracy is not significant with 88.35 0.07 for RNN and 87.11 0.50 for CNN, the training time for RNN only need 8.256 secods while CNN need 544.366 seconds.

Abstrak :
Penelitian ini membahas tentang pengembangan sistem Chatbot pada customer service bengkel motor dengan menggunakan algoritma cosine similarity. Cosine Similarity merupakan algoritma dengan basis dua vektor yang dihitung persamaannya berdasarkan sudut kedua vektor tersebut untuk mengukur tingkat kemiripan teks. Masukan sistem berupa percakapan teks yang pada proses selanjutnya diubah menjadi vektor dengan besar nilai vektor mengikuti dataset yang ada menggunakan metode Bag Of Words dengan dataset untuk membalas percakapan tersebut. Kemiripan suatu teks menggunakan akurasi dari perhitungan cosine similarity dengan akurasi sebesar 82.7%. Diamati faktor-faktor yang mempengaruhi akurasi setiap pengguna. Dalam penelitian ini, sistem menggunakan dataset sebesar 472 data katalog sepeda motor.

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This paper discusses the development of a chatbot system in a motorcycle garage using the cosine similarity algorithm. Cosine similarity is an algorithm to calculate the degree of similarity of two vectors based on the value of the angle between the two vectors. The chatbot receives an input consisting of a sentence which is then converted into a vector using the Bag of Words algorithm. Using the cosine similarity algorithm, an accuracy of 82.7% is achieved. This paper utilizes 472 motorcycle catalogues as a dataset to perform the calculation and prediction previously mentioned.