This article reviews the literature of multimedia glosses in computer assisted language learning (CALL) and their effects on L2 vocabulary acquisition during the past seventeen years. Several studies have touched on this area to examine the potential of multimedia in a CALL environment in aiding L2 vocabulary acquisition. In this review, the researchers investigate the studies that deal with different modes of multimedia annotations in a CALL environment. This article reviews the empirical studies of multimedia glosses in reading as well as in listening comprehension activities and discusses the factors under which these studies have been conducted and their impact on L2 vocabulary acquisition. The study aims to understand how multimedia glosses have been used in the past to support second language vocabulary acquisition and also to explore any evidence regarding how multimedia glosses in a CALL environment can enhance the acquisition of L2 vocabulary. Only empirical studies (18 studies published in international refereed journals and conference) have been reviewed exclusively. The article discusses the findings of the reviewed studies and recommends future research.
Due to the growing popularity of email, spam email II. RELATED WORK continues to evolve and grow. All the unsolicited email and the emails not intended for a particular user are classified as Spam Spam filter is being implemented in various classification Email. The spammers are very intelligent in concealing their mail methods [1][2]. All the methods use the features of spam email from spam filters. The spam creates increase in the network to classify them. Most popularly used techniques are discussed traffic and unnecessary wastage of time for the user. One of the in this section. foremost challenges in designing a spam filter is that the spam is user dependent. A mail that appears to be legitimate to one user Content Based Filtering: may appear spam to another and vice versa. This imposes a requirement on the design of spam filter that it should be made Content based filtering uses keywords in the email for user defined and user controllable. It is possible to locate all the classification. An approach proposed in [3] has incorporated mails related to a particular category if the number of emails in keywords for spam email filtering. Representative Text, a the inbox is less. As the number of emails in the inbox increases it collection of spam keywords, is used in [4] for email becomes time consuming and difficult for a user to search all the classification by comparing the email tokens and the available emails belonging to a particular category. A filter similar to spam keyword database. A heuristic based interactive approach to filter can be designed to make such categorization automatic. The spam filter design is implemented in [5]. paper proposes a method to create an email classification filter to satisfy both the above objectives. It uses ontology for Statistical basedfiltering: understanding the content of the email and Bayesian approach for making the classification. Statistical based method is a popular approach in spai filter Keywords: Ontology, Spam, Ham design. It assigns a probability or score to each keyword and the overall score or probability is used to classify the incoming I. INTRODUCTION email. Bayesian based statistical approach [6] [7] uses the In day-to-day life, Email has become an essential part in our probability of spam keywords in email classification. An life. From user's perspective email falls under either of two approach to decrease the overhead in Bayesian approach is categories, Ham or Spam. All Legitimate emails and emails proposed in [8]. intended to the receiver are called ham and all unsolicited Machine learning approachforfiltering. emails are called spam. There are more problems due to spam. To name some, Spam email creates bottleneck in server byOntology is used as one of the learning tools for email increasing network traffic and creates unnecessary wastage of classification. A user defined ontological database proposed in time for the email user. This forces researchers to create a [9] uses both the semantic and syntactic features of email. better spam filter.Frequency ...
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