Empirical approach to conceptual case frame acquisition

Journal ArticleConceptual natural language processing systems usually rely on case frame instantiation to recognize events and role objects in text. But generating a good set of case frames for a domain is time-consuming, tedious, and prone to errors of omission. We have developed a corpus-based algorithm for acquiring conceptual case frames empirically from unannotated text. Our algorithm builds on previous research on corpus-based methods for acquiring extraction patterns and semantic lexicons. Given extraction patterns and a semantic lexicon for a domain, our algorithm learns semantic preferences for each extraction pattern and merges the syntactically compatible patterns to produce multi-slot case frames with selectional restrictions. The case frames generate more cohesive output and produce fewer false hits than the original extraction patterns. Our system requires only preclassified training texts and a few hours of manual review to filter the dictionaries, demonstrating that conceptual case frames can be acquired from unannotated text without special training resources

A Crowdsourced Frame Disambiguation Corpus with Ambiguity

We present a resource for the task of FrameNet semantic frame disambiguation of over 5,000 word-sentence pairs from the Wikipedia corpus. The annotations were collected using a novel crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. In contrast to the typical approach of attributing the best single frame to each word, we provide a list of frames with disagreement-based scores that express the confidence with which each frame applies to the word. This is based on the idea that inter-annotator disagreement is at least partly caused by ambiguity that is inherent to the text and frames. We have found many examples where the semantics of individual frames overlap sufficiently to make them acceptable alternatives for interpreting a sentence. We have argued that ignoring this ambiguity creates an overly arbitrary target for training and evaluating natural language processing systems - if humans cannot agree, why would we expect the correct answer from a machine to be any different? To process this data we also utilized an expanded lemma-set provided by the Framester system, which merges FN with WordNet to enhance coverage. Our dataset includes annotations of 1,000 sentence-word pairs whose lemmas are not part of FN. Finally we present metrics for evaluating frame disambiguation systems that account for ambiguity.Comment: Accepted to NAACL-HLT201

Implementation of Text Extraction From Video Using Morphology and DWT

Video has one of the most popular media for entertainment, study and types delivered through the internet, wireless network, broadcast which deals with the video content analysis and retrieval. The content based retrieval of image and video databases is an important application due to rapid proliferation of digital video data on the Internet and corporate intranets. Text which is extracted from video either embedded or superimposed within video frames is very useful for describing the contents of the frames, it enables both keyword and free-text based search from internet that find out the any contained display in the video. The algorithm performance on the basis of text localization and false positive rate has improved for different types of video. The overall accuracy of this methodology is high than that of any other methods. The advantage of this algorithm is that it minimise processing time

Compressing Presentation-Dominated Video Files

A technique is proposed for efficiently compressing presentation-dominated video files. Processing logic may receive, from a video conferencing platform, a video file including multiple frames and audio. The processing logic further may identify multiple presentation slides associated with the video file. In some instances, to identify the multiple presentation slides, the processing logic may receive the presentation slides from one of the video conferencing platform or a user of the video conferencing platform (e.g., via a client device). The processing logic may further generate a mapping of each of the multiple presentation slides to one or more of the multiple frames based on a similarity level between a given presentation slide and a given frame and organize the mapping in a data structure (frame-slide mapping data structure). This similarity level may be determined using one or more machine learning models. The processing logic may further compress the multiple presentation slides, the audio, and the frame-slide mapping data structure to obtain a compressed file. The video can be recreated/uncompressed by inserting the presentation slides into the video frames as determined by frame-slide mapping data structure and adding the audio (e.g., the audio channel) to the generated video. This results in less storage cost compared to conventional compression algorithms and better video quality for static contents (e.g. text, diagrams, sheets, etc.) of the recreated video

Analysis of Using Metric Access Methods for Visual Search of Objects in Video Databases

This article presents an approach to object retrieval that searches for and localizes all the occurrences of an object in a video database, given a query image of the object. Our proposal is based on text-retrieval methods in which video key frames are represented by a dense set of viewpoint invariant region descriptors that enable recognition to proceed successfully despite changes in camera viewpoint, lighting, and partial occlusions. Vector quantizing these region descriptors provides a visual analogy of a word - a visual word. Those words are grouped into a visual vocabulary which is used to index all key frames from the video database. Efficient retrieval is then achieved by employing methods from statistical text retrieval, including inverted file systems, and text-document frequency weightings. Though works in the literature have only adopted a simple sequential scan during search, we investigate the use of different metric access methods (MAM): M-tree, Slim-tree, and D-index, in order to accelerate the processing of similarity queries. In addition, a ranking strategy based on the spatial layout of the regions (spatial consistency) is fully described and evaluated. Experimental results have shown that the adoption of MAMs not only has improved the search performance but also has reduced the influence of the vocabulary size over test results, which may improve the scalability of our proposal. Finally, the application of spatial consistency has produced a very significant improvement of the results