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Service Robot can tell stories to various audience who need a robot to accompany, such as children, students, and old people. It is desired that robot can discuss the story with the users after it just told a story. Discussion can be achieved with dialogue template; however, it is costly to edit the templates manually. In this paper we report how we build a system that adopts the information extraction technology to synthesize dialogue template in AIML, so that a Chatbot can chat about a new story with a human user.

With the rising trends of dialog system, interactions with questions become a vital human-machine interface in realizing intentions and generating adequate feedback, thereby establishing question generation as an important and interesting research topic. In the education domain, question generation is also useful for reducing time and effort for teachers, as well as parents. Extensive research has been conducted on question generation recently but little focused on question generation in Chinese. In addition, previous papers examining question generation in Chinese, have been poorly reviewed due to the clumsy handling of sentence complexities of Chinese. In this article, we focus on developing a question generation technique based on children’s tales. For the training and testing dataset, approximately one hundred articles from the website, 兒童 故事(http://www.yfes.tn.edu.tw/yfesvi/story.htm) were used. To achieve better quality of the questions automatically generated, approaches and methods were carefully chosen in this system. The results show that questions generated from our system are mostly acceptable, which reflects the main objective of our system.

This paper reports how we build a Chinese Grammatical Error Diagnosis system in the NLPTEA-2018 CGED shared task. In 2018, we sent three runs with three different approaches. The first one is a pattern-based approach by frequent error pattern matching. The second one is a sequential labelling approach by conditional random fields (CRF). The third one is a rewriting approach by sequence to sequence (seq2seq) model. The three approaches have different properties that aim to optimize different performance metrics and the formal run results show the differences as we expected.

The objective of the study is to generate the product hierarchical categories in e-commerce, particularly for e-commerce giants such as Taobao or Jingdong. For e-commerce websites the amount of products is huge, and a hierarchical structure is necessary for consumers to browse them. We find that there are two problems in the current websites: firstly, the hierarchy is shallow; there are often too many products in the same category, it is hard for a consumer browse them. Secondly, the hierarchy is constructed manually, when new products come, it is hard to update the hierarchy. Based on the product description analysis, it is possible to solve the problems. In this study, we will use the deep learning word embedding technology and clustering algorithm to construct a deeper product hierarchy automatically. The results will help the customers to choose products with a more clear structure and also help the e-commerce company to save the maintaining effort on the product hierarchy.

Customer reviews provide opinions and relevant information that will affect the purchase behavior of other customers. Many studies focused on the prediction of the helpfulness of customer reviews to find the helpful reviews, which are traditionally determined by the helpful voting results. In our study, we find that the voting result of an online review is not a constant over time. Therefore, predicting the voting result based on the analysis of text is not enough; the temporal issue must be considered. We propose a system that can rank the reviews based on a set of linguistic features with a linear regression model. To evaluate our system, we collect Chinese custom reviews in eight product categories (books, digital cameras, tablet PC, backpacks, movies, men shoes, toys and cell phones) from Amazon.cn with the voting result on the helpfulness of the reviews. Since the voting result may be affected by voting time and total voting number, we define a new evaluation index and compare the regression results. The results show that the system has less prediction error when it takes the time information into the prediction model.

We introduce a method for learning to predict text and grammatical construction in a computer-assisted translation and writing framework. In our approach, predictions are offered on the fly to help the user make appropriate lexical and grammar choices during the translation of a source text, thus improving translation quality and productivity. The method involves automatically generating general-to-specific word usage summaries (i.e., writing suggestion module), and automatically learning high-confidence word- or phrase-level translation equivalents (i.e., translation suggestion module). At runtime, the source text and its translation prefix entered by the user are broken down into n-grams to generate grammar and translation predictions, which are further combined and ranked via translation and language models. These ranked prediction candidates are iteratively and interactively displayed to the user in a pop-up menu as translation or writing hints. We present a prototype writing assistant, TransAhead , that applies the method to a human-computer collaborative environment. Automatic and human evaluations show that novice translators or language learners substantially benefit from our system in terms of translation performance (i.e., translation accuracy and productivity) and language learning (i.e., collocation usage and grammar). In general, our methodology of inline grammar and text predictions or suggestions has great potential in the field of computer-assisted translation, writing, or even language learning.

In this paper we report how we improve our Chinese grammar error detection system. We focus on the recognition of word selection error, which is the hardest error type defined in 2015 NLP-TEA shared task CGED. Four major error types was defined in CGED shared task, including redundant word error, missing word error, word selection error and word disorder error. Based on the conditional random fields (CRF) model, our system trains a linear tagger that can be used to recognize the errors in learners' essays. In 2015 CGED shared task, we have found that our system can achieve high precision and accuracy. Our previous system used the features in the sentence only, however, it is not sufficient to recognize the word selection error. In this paper, we propose that by integrating the collocation feature into the CRF model, our system can improve the performance on the recognition of the selection errors.

We introduce a method for learning to translate out-of-vocabulary (OOV) words. The method focuses on combining sublexical/constituent translations of an OOV to generate its translation candidates. In our approach, wildcard searches are formulated based on our OOV analysis, aimed at maximizing the probability of retrieving OOVs’ sublexical translations from existing resources of Machine Translation (MT) systems. At run-time, translation candidates of the unknown words are generated from their suitable sublexical translations and ranked based on monolingual and bilingual information. We have incorporated the OOV model into a state-of-the-art machine translation system and experimental results show that our model indeed helps to ease the impact of OOVs on translation quality, especially for sentences containing more OOVs (significant improvement).

In the age of information network explosion, users can be connected to various forms of social networking sites whenever and wherever through the Internet, And through the social networking platform to interact with people. Through the media in this way, people know each other's interests and preferences by the relationship between social friends, but for many international companies, it is important to know how to understand individual preferences, because when you know the individual preference information, it can carry out personal preference for advertising, product recommendation, article recommended and other diversified personal social service, which can increase the click rate and exposure of the products, better close to the needs of the user's life. Therefore, with the above through social science and technology development trend arising from current social phenomenon, research of this thesis, mainly expectations for analysis by the information of interaction between people on the social network, such as: user clicked fan page, user's graffiti wall message information, friend clicked fan page etc. three kinds of personal information for personal preference analysis, and from this huge amount of personal data to find out corresponding diverse group for personal preference category. We can by personal preference information for diversify personal advertising, product recommendation and other services. The thesis at last through the actual business verification, the research can improve website browsing pages growth 11%, time on site growth 15%, site bounce rate dropped 13.8%, product click through rate growth 43%, more fully represents the results of this research fit the use's preference.

We introduce a method that extracts keywords in a language with the help of the other. The method involves estimating preferences for topical keywords and fusing language-specific word statistics. At run-time, we transform parallel articles into word graphs, build crosslingual edges for word statistics integration, and exploit PageRank with word keyness information for keyword extraction. We apply our method to keyword analysis and language learning. Evaluation shows that keyword extraction benefits from cross-language information and language learners benefit from our keywords in reading comprehension test.

This paper reports how to build a Chinese Grammatical Error Diagnosis system based on the conditional random fields (CRF). The system can find four types of grammatical errors in learners’ essays. The four types or errors are redundant words, missing words, bad word selection, and disorder words. Our system presents the best false positive rate in 2015 NLP-TEA-2 CGED shared task, and also the best precision rate in three diagnosis levels.

We introduce a method that extracts keywords in a language with the help of the other. In our approach, we bridge and fuse conventionally irrelevant word statistics in languages. The method involves estimating preferences for keywords w.r.t. domain topics and generating cross-lingual bridges for word statistics integration. At run-time, we transform parallel articles into word graphs, build cross-lingual edges, and exploit PageRank with word keyness information for keyword extraction. We present the system, BiKEA , that applies the method to keyword analysis. Experiments show that keyword extraction benefits from PageRank, globally learned keyword preferences, and cross-lingual word statistics interaction which respects language diversity.

The hand-held systems and the wireless Internet access are widely available in recent years. However, the mobile learning with web content is still inconvenient. For example, the information has not well organized and it is uneasy to surf on the small screen of handheld device. We propose a mobile system based on the content of Wikipedia. Wikipedia is a free content resource and has abundant contents of text and pictures. We use the Wikipedia wrapper that we developed before, to develop the mobile-learning interface of cross-language and cross-platform applications. Our system can present the content of Wikipedia on the small screens of PDA, and can be use on mobile learning. A teaching scenario of mobile learning during a museum visiting is discussed in this paper.

In this research, we propose a Chinese essay error detection system that can be used in online writing tutorial environment. The system consists of word segmentation, template module, and language model module. We build the system with a dictionary for word segmentation and generation of detection templates from news corpus. We also gather the character error probabilities from students’ essays to build error model. Error types include pronunciation-related errors and composition-related errors. Our system provides two operating modes for users with different goals. For example, we can help students learn to write effectively by providing high precision correction mode; for teachers, we provide high correction detection mode which can help teachers to check students’ essays.