Overview of Natural Language Processing of Captions for Retrieving Multimedia Data

نویسندگان

  • Eugene J. Guglielmo
  • Neil C. Rowe
چکیده

This paper briefly describes the current implementation status of an intelligent information retrieval system, MARIE, that employs natural language processing techniques. Descriptive captions are used to identify photographic images concerning various military projects. The captions are parsed to produce a logical form from which nouns and verbs are extracted to form the primary keywords. User queries are also specified in natural language. A two-phase search process employing coarse-grain and fine-grain match processes is used to find the captions that best match the query. A type hierarchy based on object-oriented programming constructs is used to represent the semantic knowledge base. This knowledge base contains knowledge of various military concepts and terminology with specifics from the Naval Weapons Center. Methods are used for creating the logical form during semantic analysis, generating the keywords to be used in the coarse-grain match process, and fine-grain matching between query and caption logical forms.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

The effects of captioning texts and caption ordering on L2 listening comprehension and vocabulary learning

This study investigated the effects of captioned texts on second/foreign (L2) listening comprehension and vocabulary gains using a computer multimedia program. Additionally, it explored the caption ordering effect (i.e. captions displayed during the first or second listening), and the interaction of captioning order with the L2 proficiency level of language learners in listening comprehension a...

متن کامل

Semantic Restructuring of Natural Language Image Captions to Enhance Image Retrieval

The rapid growth in the volume of visual information can make the task of finding and accessing visual information of interest, overwhelming for users. Semantic analysis of image captions can be used in conjunction with image retrieval systems (IMR) to retrieve selected images more precisely. To do this, we first exploit a Natural Language Processing (NLP) framework in order to extract concepts...

متن کامل

Statistical versus symbolic parsing for captioned-information retrieval

Our MARIE project has been investigating information retrieval of multimedia data using a new idea: putting primary emphasis on caption processing. Although content analysis methods such as substring searching for text media and shape matching for picture media can obviate captions, content analysis usually requires unacceptably-large amounts of time at retrieval time. Captions can be cachings ...

متن کامل

Multi-Media Fusion through Application of Machine Learning and NLP

This paper describes work on a system to dynamically cluster and present information from live multimedia news sources. Features are obtained by applying statistical and natural language processing techniques to texts associated with the media (new wire stories or closed-captions). Conceptual clustering is employed to dynamically build a set of hierarchical clusters. Results are presented objec...

متن کامل

Understanding of Navy Technical Language via Statistical Parsing

A key problem in indexing technical information is the interpretation of technical words and word senses, expressions not used in everyday language. This is important for captions on technical images, whose often pithy descriptions can be valuable to decipher. We describe the natural-language processing for MARIE-2, a natural-language information retrieval system for multimedia captions. Our ap...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 1992