Publications

@conference{Carvalho2017239,

title = {WIKTDV: Data extraction and vector representation resource for Wiktionary senses},

author = {Danilo S Carvalho and Minh-Le Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043694499&doi=10.1109%2fKSE.2017.8119465&partnerID=40&md5=b849fb4ebc15878e48f4c65e8039b895},

doi = {10.1109/KSE.2017.8119465},

year  = {2017},

date = {2017-01-01},

journal = {Proceedings - 2017 9th International Conference on Knowledge and Systems Engineering, KSE 2017},

volume = {2017-January},

pages = {239-244},

abstract = {Effective use of collaborative web resources, such as Wikipedia and Wiktionary, has been a recurrent topic of research in the Natural Language Processing and Information Retrieval communities. The same can be said about the use of vector-based language representations, e.g., word, sentence, document embeddings. However, there is currently a shortage of resources that offer vector representations that can take advantage of the structural properties of web resources. This paper describes a system for extracting information from Wiktionary to a machine-readable format and using this information to obtain vector representations that can be used for semantic similarity computation and basic word sense disambiguation. The methodology used to build the system is also discussed. Experimental evaluation on the semantic similarity task indicate efficiency close to the reference method applied in this work. A web service and visualization facilities complete the set of contributions. © 2017 IEEE.},

note = {cited By 0},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Vo201724,

title = {Multi-channel LSTM-CNN model for Vietnamese sentiment analysis},

author = {Quan-Hoang Vo and Huy-Tien Nguyen and Bac Le and Minh-Le Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042233213&doi=10.1109%2fKSE.2017.8119429&partnerID=40&md5=441295949db139c45e50302879d06804},

doi = {10.1109/KSE.2017.8119429},

year  = {2017},

date = {2017-01-01},

journal = {Proceedings - 2017 9th International Conference on Knowledge and Systems Engineering, KSE 2017},

volume = {2017-January},

pages = {24-29},

abstract = {Convolutional neural network (CNN) and Long Short Term Memory (LSTM) have shown the state of the art results for sentiment analysis in English corpus. However, there are not many studies of this approach for Vietnamese corpus. In our work, CNN and LSTM are employed to generate information channels for Vietnamese sentiment analysis. Because each deep learning model (e.g. CNN, LSTM) has a particular advantage, this scenario provides a novel and efficient way for integrating the advantages of CNN and LSTM. In addition, we introduced a Vietnamese corpus, which collected comments/reviews from Vietnamese commercial web pages and was annotated by three human annotators. We evaluated our approach on our corpus and VLSP corpus. According to the experimental results, the proposed model outperforms SVM, LSTM, and CNN on the two datasets. © 2017 IEEE.},

note = {cited By 45},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Nguyen201770,

title = {Utilizing user posts to enrich web document summarization with matrix co-factorization},

author = {Minh-Tien Nguyen and Tran Viet Cuong and Nguyen Xuan Hoai and Minh-Le Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041042396&doi=10.1145%2f3155133.3155196&partnerID=40&md5=96422d5dca8d2451a3690813d7c611cc},

doi = {10.1145/3155133.3155196},

year  = {2017},

date = {2017-01-01},

journal = {ÄCM International Conference Proceeding Series"},

volume = {2017-December},

pages = {70-77},

abstract = {In the context of social media, users tend to post relevant information corresponding to an event mentioned in a Web document. This paper presents a model to capture the nature of the relationships between sentences and user posts such as relevant comments in sharing hidden topics for enriching summarization. Unlike the previous methods which usually base on hand-crafted features, our approach ranks sentences and comments based on their importance affecting the topics. The sentence-comment relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our newly proposed matrix co-factorization algorithm computes the score of each sentence and comment and extracts top m ranked sentences and m comments as the summarization. Experimental results on two datasets in English and Vietnamese of the social context summarization task and DUC 2004 confirm the efficiency of our model in summarizing Web documents. © 2017 Association for Computing Machinery.},

note = {cited By 6},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tran201719,

title = {Towards domain adaptation for neural network language generation in dialogue},

author = {Van-Khanh Tran and Van-Tao Nguyen and Kiyoaki Shirai and Minh-Le Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043382754&doi=10.1109%2fNAFOSTED.2017.8108032&partnerID=40&md5=0466b2239fad9fa0e460255ae86b8d28},

doi = {10.1109/NAFOSTED.2017.8108032},

year  = {2017},

date = {2017-01-01},

journal = {2017 4th NAFOSTED Conference on Information and Computer Science, NICS 2017 - Proceedings},

volume = {2017-January},

pages = {19-24},

abstract = {Extending from limited domain to a new domain is crucial for Natural Language Generation in Dialogue, especially when there are sufficient annotated data in the source domain, but there is little labeled data in the target domain. This paper studies the performance and domain adaptation of two different Neural Network Language Generators in Spoken Dialogue Systems: a gating-based Recurrent Neural Network Generator and an extension of an Attentional Encoder-Decoder Generator. We found in model fine-tuning scenario that by separating slot and value parameterizations, the attention-based generators, in comparison to the gating-based generators, show ability to not only prevent semantic repetition in generated outputs and obtain better performance across all domains, but also adapt faster to a new, unseen domain by leveraging existing data. The empirical results show that the attention-based generator can adapt to an open domain when only a limited amount of target domain data is available. © 2017 IEEE.},

note = {cited By 1},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Carvalho2017171,

title = {Efficient neural-based patent document segmentation with term order probabilities},

author = {Danilo Silva Carvalho and Minh-Le Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070734361&partnerID=40&md5=e4947c5caa74b0f7e4b736e07b4651d1},

year  = {2017},

date = {2017-01-01},

journal = {ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},

pages = {171-176},

abstract = {The internationally growing trend of patent applications puts great pressure on the agents involved in managing this kind of information and creates a demand for efficient and effective patent analysis methods. This work presents a computationally efficient approach for patent docu- ment segmentation based on structured ANNs and a simple distributional semantics composition method. The conducted experiments indicate effec- tiveness of the approach, which benefits a wide array of patent processing techniques that work upon structured inputs. © ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.},

note = {cited By 1},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Trieu2017405,

title = {The JAIST machine translation systems for WMT 17},

author = {Hai-Long Trieu and Trung-Tin Pham and Le-Minh Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080439079&partnerID=40&md5=da84baff5e105a1af2df160a5b65389e},

year  = {2017},

date = {2017-01-01},

journal = {WMT 2017 - 2nd Conference on Machine Translation, Proceedings},

pages = {405-409},

abstract = {We describe the JAIST phrase-based machine translation systems that participated in the news translation shared task of the WMT17. In this work, we participated in the Turkish-English translation, in which only a small amount of bilingual training data is available, so that it is an example of the low-resource setting in machine translation. In order to solve the problem, we focus on two strategies: building a bilingual corpus from comparable data and exploiting existing parallel data based on phrase pivot translation. In order to utilize the strategies to enhance machine translation on the low-resource setting most effectively, we introduce a system combining the extracted corpus, the pivot translation, and the direct training data. Experimental results showed that our combined systems significantly improved the baseline models, which were trained on the small bilingual data. © 2017 Association for Computational Linguistics},

note = {cited By 2},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tran2017231,

title = {Neural-based natural language generation in dialogue using RNN encoder-decoder with semantic aggregation},

author = {Van-Khanh Tran and Le-Minh Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073185378&partnerID=40&md5=18f08ac1cc06c4bb48f0ce1b2d956c77},

year  = {2017},

date = {2017-01-01},

journal = {SIGDIAL 2017 - 18th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference},

pages = {231-240},

abstract = {Natural language generation (NLG) is an important component in spoken dialogue systems. This paper presents a model called Encoder-Aggregator-Decoder which is an extension of an Recurrent Neural Network based Encoder-Decoder architecture. The proposed Semantic Aggregator consists of two components: an Aligner and a Refiner. The Aligner is a conventional attention calculated over the encoded input information, while the Refiner is another attention or gating mechanism stacked over the attentive Aligner in order to further select and aggregate the semantic elements. The proposed model can be jointly trained both text planning and text realization to produce natural language utterances. The model was extensively assessed on four different NLG domains, in which the results showed that the proposed generator consistently outperforms the previous methods on all the NLG domains. © 2017 Association for Computational Linguistics.},

note = {cited By 17},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tran2017442,

title = {Natural language generation for spoken dialogue system using RNN encoder-decoder networks},

author = {Van-Khanh Tran and Le-Minh Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043390374&doi=10.18653%2fv1%2fk17-1044&partnerID=40&md5=e09901b293db8a8cd6a7a479f6a16b44},

doi = {10.18653/v1/k17-1044},

year  = {2017},

date = {2017-01-01},

journal = {CoNLL 2017 - 21st Conference on Computational Natural Language Learning, Proceedings},

pages = {442-451},

abstract = {Natural language generation (NLG) is a critical component in a spoken dialogue system. This paper presents a Recurrent Neural Network based Encoder-Decoder architecture, in which an LSTM-based decoder is introduced to select, aggregate semantic elements produced by an attention mechanism over the input elements, and to produce the required utterances. The proposed generator can be jointly trained both sentence planning and surface realization to produce natural language sentences. The proposed model was extensively evaluated on four different NLG datasets. The experimental results showed that the proposed generators not only consistently outperform the previous methods across all the NLG domains but also show an ability to generalize from a new, unseen domain and learn from multi-domain datasets. © 2017 Association for Computational Linguistics.},

note = {cited By 30},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Trieu2017511,

title = {Towards developing dialogue systems with entertaining conversations},

author = {Hai-Long Trieu and Hiroyuki Iida and Nhien Pham Hoang Bao and Le-Minh Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065871212&doi=10.5220%2f0006192105110518&partnerID=40&md5=becabde9da82c188038dbcdc76db0291},

doi = {10.5220/0006192105110518},

year  = {2017},

date = {2017-01-01},

journal = {ICAART 2017 - Proceedings of the 9th International Conference on Agents and Artificial Intelligence},

volume = {2},

pages = {511-518},

abstract = {This paper explores a novel approach to developing a dialogue system that is able to make entertaining conversations with users. It proposes a method to improve the current goal-driven dialogue systems which support users for specific tasks while satisfying users' goals with entertaining conversations. It then develops a dialogue system in which a set of features are considered to generate entertaining conversations, while reasonably prolonging the original too short dialogue. The game refinement measure is employed for the assessment of attractiveness since the conversations in dialogue systems can be seen as the process by which a player creates shoots or moves to win a game. The dialogues generated by the proposed method are evaluated by human subjects. The results confirm the effectiveness of the proposed method. The present idea can be a promising way to realize dialogue systems with entertaining conversations although further investigations are needed. © 2017 by SCITEPRESS - Science and Technology Publications, Lda.},

note = {cited By 2},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Trieu20171043,

title = {Applying semantic similarity to phrase pivot translation},

author = {Hai-Long Trieu and Le-Minh Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013629392&doi=10.1109%2fICTAI.2016.0157&partnerID=40&md5=25e81914122837a5d160ac3b0900e7fb},

doi = {10.1109/ICTAI.2016.0157},

year  = {2017},

date = {2017-01-01},

journal = {Proceedings - 2016 IEEE 28th International Conference on Tools with Artificial Intelligence, ICTAI 2016},

pages = {1043-1047},

abstract = {Pivot methods have shown to be an effective solution to overcome the problem of unavailable large bilingual corpora in statistical machine translation. The representative approach of pivot methods is the phrase pivot translation which is based on common pivot phrases to produce connections between source-pivot and pivot-Target phrase tables. Nevertheless, this approach produces insufficient connections behind the phrase tables because pivot phrases still contain the same meaning even when they are not matched to each other. In this work, we propose applying semantic similarity between pivot phrases to phrase pivot translation. In order to extract similar pivot phrases, we used string similarity measures for phrase similarity, and WordNet and Word2Vec were used for word similarity. The experiments show that using semantic similarity is able to extract more informative phrases, which can support for phrase pivot translation. © 2016 IEEE.},

note = {cited By 1},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Nguyen2017619,

title = {Learning to Summarize Web Documents using Social Information},

author = {Minh-Tien Nguyen and Duc-Vu Tran and Chien-Xuan Tran and Minh-Le Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013641514&doi=10.1109%2fICTAI.2016.97&partnerID=40&md5=49b63da3bb54f6f38619d4cf95c1bd61},

doi = {10.1109/ICTAI.2016.97},

year  = {2017},

date = {2017-01-01},

journal = {Proceedings - 2016 IEEE 28th International Conference on Tools with Artificial Intelligence, ICTAI 2016},

pages = {619-626},

abstract = {This paper presents a method named SoSVMRank, which integrates the social information of a Web document to generate a high-quality summarization. In order to do that, the summarization was formulated as a learning to rank task, in which the order of a sentence or comment was determined by its informative information. The informative information was measured by a set of local and social features in which the social features were exploited to support the local ones when modeling a sentence or comment. To enrich information, new features were also proposed. After ranking, top m ranked sentences and comments were selected as the summarization. Our method was extensively evaluated on two datasets. Promising results indicate that: (1) by using new features, our method achieves improvements in both ROUGE- 1 and ROUGE-2 of the summarization over state-of-The-Art baselines and (2) integrating social information benefits the summarization. © 2016 IEEE.},

note = {cited By 7},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Ong2016205,

title = {Reranking CCG parser for Jazz chord sequences},

author = {Hong Xuan Öng and Satoshi Tojo and others"},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007001618&doi=10.1109%2fKSE.2016.7758054&partnerID=40&md5=15f6b3be33d62300181e33237e8d5c41},

doi = {10.1109/KSE.2016.7758054},

year  = {2016},

date = {2016-01-01},

journal = {Proceedings - 2016 8th International Conference on Knowledge and Systems Engineering, KSE 2016},

pages = {205-211},

abstract = {When listen to the music, specifically chord progression, each person can have more than one understanding or feeling about what they heard which we call musical intuitions of listeners or human capacity of musical understanding. To disambiguate the chord progression, Granroth-Wilding et al. have employed Probabilistic Combinatory Categorial Grammar (PCCG), and they acquired the recall value of 88.78%, and the precision value of 90.18%. Because this chord progression parser only outputs the one with the highest probability, the correct solution may still reside in the following candidates. In this paper, we use the reranking model to improve the performance of the parser. By selecting a set of simple n-gram features and configuring perceptron algorithm for finding optimizing parameters, we have improved performance of the system by 2.2%, and even 6.57% when we could perfectly pick up correct candidates from 5000-best results. © 2016 IEEE.},

note = {cited By 0},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Trieu2016259,

title = {Dealing with out-of-vocabulary problem in sentence alignment using word similarity},

author = {Hai Long Trieu and Minh Le Nguyen and Phuong-Thai Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015839847&partnerID=40&md5=2a499d26d847621b06076910c8a571f6},

year  = {2016},

date = {2016-01-01},

journal = {Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016},

pages = {259-266},

abstract = {Sentence alignment plays an essential role in building bilingual corpora which are valuable resources for many applications like statistical machine translation. In various approaches of sentence alignment, length-and-word-based methods which are based on sentence length and word correspondences have been shown to be the most effective. Nevertheless a drawback of using bilingual dictionaries trained by IBM Models in length-and-word-based methods is the problem of out-of-vocabulary (OOV). We propose using word similarity learned from monolingual corpora to overcome the problem. Experimental results showed that our method can reduce the OOV ratio and achieve a better performance than some other lengthand- word-based methods. This implies that using word similarity learned from monolingual data may help to deal with OOV problem in sentence alignment.},

note = {cited By 4},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Son2016252,

title = {Recognizing logical parts in legal texts using neural architectures},

author = {Nguyen Truong Son and Ho Bao Quoc and Akira Shimazu and others},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006967216&doi=10.1109%2fKSE.2016.7758062&partnerID=40&md5=d1263aa95fbb64ee235de5b982b05b89},

doi = {10.1109/KSE.2016.7758062},

year  = {2016},

date = {2016-01-01},

journal = {Proceedings - 2016 8th International Conference on Knowledge and Systems Engineering, KSE 2016},

pages = {252-257},

abstract = {This paper proposes neural networks approaches to recognize logical parts in Vietnamese legal documents. We utilize four models based on recurrent neural networks including Long Short Term Memory (LSTM), Bidirectional LSTM and their combination with Conditional Random Fields. The experimental results on the Vietnamese Business Law data set shows the promising of this approach. Although, these approaches don't use any engineering features like traditional approaches, they can produce the state-of-the-art performance. © 2016 IEEE.},

note = {cited By 3},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Nguyen20162409,

title = {SoLSCSum: A linked sentence-comment dataset for social context summarization},

author = {Minh-Tien Nguyen and Chien-Xuan Tran and Duc-Vu Tran and Minh-Le Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84996537865&doi=10.1145%2f2983323.2983376&partnerID=40&md5=9d85eaee3c9a9f8463c159d767564ef2},

doi = {10.1145/2983323.2983376},

year  = {2016},

date = {2016-01-01},

journal = {International Conference on Information and Knowledge Management, Proceedings},

volume = {24-28-October-2016},

pages = {2409-2412},

abstract = {This paper presents a dataset named SoLSCSum for social context summarization. The dataset includes 157 open-domain articles along with their comments collected from Yahoo News. The articles and their comments were manually annotated by two annota-tors to extract standard summaries. The inter-annotator agreement is 74.5% and Cohen's Kappa is 0.5845. To illustrate the potential use of our dataset, a learning to rank model was trained by using a set of local and cross features. Experimental results demonstrate that: (1) our model trained by Ranking SVM obtains significant improvements from 5.5% to 14.8% of ROUGE-1 over state-of-the-art baselines in document summarization and (2) our dataset can be used to train summary methods such as SVM. © 2016 ACM.},

note = {cited By 14},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tung2016332,

title = {Semantic Parsing for Vietnamese Question Answering System},

author = {Vu Xuan Tung and Nguyen Le Minh and Duc Tam Hoang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964706599&doi=10.1109%2fKSE.2015.42&partnerID=40&md5=3d3887c3a2df39a82a935095f0c9cac3},

doi = {10.1109/KSE.2015.42},

year  = {2016},

date = {2016-01-01},

journal = {Proceedings - 2015 IEEE International Conference on Knowledge and Systems Engineering, KSE 2015},

pages = {332-335},

abstract = {Based on a framework for English, we developed a Vietnamese Question Answering System. The learning paradigm in the framework reduces the burden of providing supervision during semantic parsing. Whilst taking the advantages from this mechanism, we further create our own feature calculation which is suitable for Vietnamese. A method of dynamic learning for feature computation is also presented in this work. © 2015 IEEE.},

note = {cited By 2},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Hoang201685,

title = {L2S: Transforming Natural Language Questions into SQL Queries},

author = {Duc Tam Hoang and Minh Le Nguyen and Son Bao Pham},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964788492&doi=10.1109%2fKSE.2015.38&partnerID=40&md5=c898c4461dc5e529db8cd2fefe5fd535},

doi = {10.1109/KSE.2015.38},

year  = {2016},

date = {2016-01-01},

journal = {Proceedings - 2015 IEEE International Conference on Knowledge and Systems Engineering, KSE 2015},

pages = {85-90},

abstract = {The reliability of a question answering system is bounded by the availability of resources and linguistic tools. In this paper, we introduce a hybrid approach to transforming natural language questions into structured queries. It alleviates the lack of experts in domain observation and the deficient performance of linguistic tools. Specifically, we exploit the semantic information for mapping natural language terminologies to structured query and bipartite graph model for the matching phase. Experimental results on the Vietnam national university entrance exam dataset and the Geoqueries880 dataset achieve accuracies of 91.14% and 87.55%, respectively. © 2015 IEEE.},

note = {cited By 1},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Nguyen2016108,

title = {Recognizing Textual Entailment in Vietnamese Text: An Experimental Study},

author = {Minh-Tien Nguyen and Quang-Thuy Ha and Thi-Dung Nguyen and Tri-Thanh Nguyen and Le-Minh Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964725492&doi=10.1109%2fKSE.2015.23&partnerID=40&md5=d17bf859f8ed15798dcc1304327b343a},

doi = {10.1109/KSE.2015.23},

year  = {2016},

date = {2016-01-01},

journal = {Proceedings - 2015 IEEE International Conference on Knowledge and Systems Engineering, KSE 2015},

pages = {108-113},

abstract = {This paper proposes a model which utilizes Support Vector Machines (SVMs) - a machine learning approach for recognizing textual entailment in Vietnamese text, including three steps: (1) feature extraction, (2) training and (3) judgement by voting. In the first step, many features (e.g., Euclidean distance, Cosine, if-idf, etc) were extracted to train three classification models for the second step. The final step judged whether there is an entailment relation between a text and a hypothesis (another text can be plausibly inferred from the original one) or not. To improve the recognition quality, a combination of classifiers was proposed under voting method as human judgement on Vietnamese version of RTE-3. By using voting, our approach obtained significant improvements (from 1.2% to 9.4% of F-score) in comparison with baselines and ensemble methods, e.g. AdaBoost and Bagging. © 2015 IEEE.},

note = {cited By 7},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Phan201685,

title = {Exploiting tree structures for classifying programs by functionalities},

author = {Viet Anh Phan and Ngoc Phuong Chau and Minh Le Nguyen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006974296&doi=10.1109%2fKSE.2016.7758034&partnerID=40&md5=dccdafb1810db4ea6a2ef5384d0b1d6a},

doi = {10.1109/KSE.2016.7758034},

year  = {2016},

date = {2016-01-01},

journal = {Proceedings - 2016 8th International Conference on Knowledge and Systems Engineering, KSE 2016},

pages = {85-90},

abstract = {Änalyzing source code to solve software engineering problems such as fault prediction, cost, and effort estimation always receives attention of researchers as well as companies. The traditional approaches are based on machine learning, and software metrics obtained by computing standard measures of software projects. However, these methods have faced many challenges due to limitations of using software metrics which were not enough to capture the complexity of programs. The aim of this paper is to apply several natural language processing techniques, which deal with software engineering problems by exploring information of programs' abstract syntax trees (ASTs) instead of software metrics. To speed up computational time, we propose a pruning tree technique to eliminate redundant branches of ASTs. In addition, the k-Nearest Neighbor (kNN) algorithm was adopted to compare with other methods whereby the distance between programs is measured by using the tree edit distance (TED) and the Levenshtein distance. These algorithms are evaluated based on the performance of solving 104-label program classification problem. The experiments show that due to the use of appropriate data structures although kNN is a simple machine learning algorithm, the classifiers achieve the promising results. © 2016 IEEE."},

note = {cited By 5},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}