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Abstract Understanding customer behavior and driving customer satisfaction are necessary for any business to succeed in the competing market. Companies need to be aware of the prevailing customer perceptions to make more accurate and effective plans for product development and marketing. Customer feedback is available through multiple channels. Specifically, we are interested in the unstructured data available as text that would be available through social media, comments from a survey, voice recordings of customer interactions, and chat transcripts. Analyzing such data correctly is critical, as it reveals everything from buying trends to product flaws and provides a significant business advantage. It would further strengthen business opportunity to uncover customer interests, product improvements, and marketing insights. In this paper, we explore different technologies of Deep Learning and Natural Language Processing (NLP) that would help analyze better the contextual information to capture customer feedback. [ABSTRACT FROM AUTHOR]

A semantic tagger aiming to detect relevant entities in Arabic medical documents and tagging them with their appropriate semantic class is presented. The system takes profit of a Multilingual Framework covering four languages (Arabic, English, French, and Spanish), in a way that resources available for each language can be used to improve the results of the others, this is specially important for less resourced languages as Arabic. The approach has been evaluated against Wikipedia pages of the four languages belonging to the medical domain. The core of the system is the definition of a base tagset consisting of the three most represented classes in SNOMED-CT taxonomy and the learning of a binary classifier for each semantic category in the tagset and each language, using a distant learning approach over three widely used knowledge resources, namely Wikipedia , Dbpedia , and SNOMED-CT . [ABSTRACT FROM AUTHOR]

Nowadays most of the Web pages contain little amount of structure and supporting information that can reveal their semantics or meanings. To enable automated processing of the Web pages, semantic information such as metadata and tags regarding to each page should be added to it. Several authoring tools have been developed to help users tackling this task. However, manual or semi-automatic authoring is implausible when we intend to annotate large amount of Web pages. In this work, we proposed a method to automatically generate some descriptive metadata and tags for a Web page. The idea is to apply the self-organizing map algorithm to cluster the Web pages and discover the relationships between these clusters. In the mean time, the themes of each cluster are also identified. We then use such relationships and themes to tag the Web pages and generate metadata for the Web pages. The result of experiments shows that our method may generate semantically relevant metadata and tags for the Web pages. [Copyright &y& Elsevier]

We describe our work on information extraction from multiple sources for the Multimedia Indexing and Searching Environment, a project aiming at developing technology to produce formal annotations about essential events in multimedia programme material. The creation of a composite index from multiple and multi-lingual sources is a unique aspect of this project. The domain chosen for tuning the software components and testing is football. Our information extraction system is based on the use of finite state machinery pipelined with full semantic analysis and discourse interpretation. [Copyright &y& Elsevier]