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The exponential growth of social media has created informative content, which leads to the emersion of social listening tools, in order to serve the need of companies and organizations. These tools are developed mainly in English language and for those which deal with Vietnamese, they lack the analysis capabilities to capture insightful information. In our research, we propose a framework to automatically...
Nowadays, users are much more willing to share their daily activities, their thoughts or feelings, and even their intentions (e.g., buy an apartment, rent a car, travel to somewhere, etc.) on online social media channels. Understanding intents of online users, therefore, has become a crucial need in many different business areas like production, finance/banking, real estate, tourism, e-commerce, and...
Dialog act identification plays an important role in understanding conversations. It has been widely applied in many fields such as dialogue systems, automatic machine translation, automatic speech recognition, and especially useful in systems with human-computer natural language dialogue interfaces such as virtual assistants and chatbots. The first step of identifying dialog act is identifying the...
This paper investigates the effect of the label bias problem of maximum entropy Markov models for part-of-speech tagging, a typical sequence prediction task in natural language processing. This problem has been underexploited and underappreciated. The investigation reveals useful information about the entropy of local transition probability distributions of the tagging model which enables us to exploit...
Event Extraction is a complex and interesting topic in Information Extraction that includes event extraction methods from free text or web data. The result of event extraction systems can be used in several fields such as risk analysis systems, online monitoring systems or decide support tools. In this paper, we introduce a method that combines lexico -- semantic and machine learning to extract event...
In online contextual advertising, ad messages are displayed related to the content of the target Web page. It leads to the problem in information retrieval community: how to select the most relevant ad messages given the content of a page. To deal with this problem, we propose a framework that takes advantage of large scale external datasets. This framework provides a mechanism to discover the semantic...
We formulate semantic parsing as a parsing problem on a synchronous context free grammar (SCFG) which is automatically built on the corpus of natural language sentences and the representation of semantic outputs. We then present an online learning framework for estimating the synchronous SCFG grammar. In addition, our online learning methods for semantic parsing problems are also extended to deal...
Text categorization is an important research area in information retrieval. In order to save the storage space and get better accuracy, efficient and effective feature selection methods for reducing the data before analysis are highly desired. Usually, researches on feature selection use only a proper measurement such as information gain. In this paper, we propose a new feature selection method by...
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