Vast volumes of digital video data are generated recently in our daily life. One of the most challenging problems is classifying and retrieving the desired information from huge collections of digital video. Consequently, the closed caption text has been utilized as an alternative to enhance the video retrieval and classification. Some systems are designed based on English closed caption howeve...

We address the problem of classifying scenes from feature films into semantic categories and propose a robust framework for this problem. We propose that the Finite State Machines (FSM) are suitable for detecting and classifying scenes and demonstrate their usage for three types of movie scenes; conversation, suspense and action. Our framework utilizes the structural information of the scenes t...

In this paper, we present a novel approach to classify texture collections. This approach does not require experts to provide annotated training set. Given the image collection, we extract a set of invariant descriptors from each image. The descriptors of all images are vector-quantized to form ‘keypoints’. Then we represent the texture images by ‘bag-of-keypoints’ vectors. By analogy text clas...

The problem of classifying scenes from feature films into semantic categories is addressed and a robust framework for this problem is proposed. It is proposed that the finite state machines (FSM) are suitable for detecting and classifying scenes and their usage is demonstrated for three types of movie scenes: conversation, suspense and action. This framework utilises the structural information ...

This paper reports on experiments in classifying the semantic role annotations assigned to prepositional phrases in both PENN TREEBANK (version II) and FRAMENET (version 0.75). In both cases, experiments are done to see how the prepositions can be classified given the dataset’s role inventory, using standard word-sense disambiguation features, such as the parts of speech of surrounding words, a...

Single-word vector space models have been very successful at learning lexical information. However, they cannot capture the compositional meaning of longer phrases, preventing them from a deeper understanding of language. We introduce a recursive neural network (RNN) model that learns compositional vector representations for phrases and sentences of arbitrary syntactic type and length. Our mode...

Classifying Discourse Relations Mridhula Raghupathy & Hena Mehta [email protected] | [email protected] Faculty Advisors: Dr. Aravind Joshi, Dr. Ani Nenkova, & Dr. Alan Lee Abstract The goal of this project was to study properties of discourse relations as they appear in the Penn Discourse Tree Bank (PDTB), a large corpus of naturally occurring text whose discourse relations and their fe...

The bag-of-words (BOW) model is the common approach for classifying documents, where words are used as feature for training a classifier. This generally involves a huge number of features. Some techniques, such as Latent Semantic Analysis (LSA) or Latent Dirichlet Allocation (LDA), have been designed to summarize documents in a lower dimension with the least semantic information loss. Some sema...

We present a statistical system for identifying the semantic relationships or semantic roles for two major Indian Languages, Hindi and Urdu. Given an input sentence and a predicate/verb, the system first identifies the arguments pertaining to that verb and then classifies it into one of the semantic labels which can either be a DOER, THEME, LOCATIVE, CAUSE, PURPOSE etc. The system is based on 2...

The development of search engines is taking at a very fast rate. A lot of algorithms have been tried and tested. But, still the people are not getting precise results. Social networking sites are developing at tremendous rate and their growth has given birth to the new interesting problems. The social networking sites use semantic data to enhance the results. This provides us with a new perspec...