Improvement of Vietnamese noun phrases chunking in text-based person image search

Natural language processing is a crucial and beneficial task in various multimedia processing applications. Text-based person search (TBPS) application involves finding person images in an image gallery using an input as a text sentence. Previous studies have shown that noun phrases is one of the most important components in person description sentence as users employ noun phrases to describe the person characteristics. However, in recent TBPS the noun phrase chunking mainly bases on some set of rules. Low quality noun phrase chunking may lead to irrelevant results of text-based person search. In this paper, a method for Vietnamese noun phrase chunking named VNPC (Vietnamese Noun Phrase Chunking) is proposed. The method is based on Conditional Random Fields (CRFs) model and it is improved with a task-dependent post processing ruler to be integrated in Vietnamese text-based person image search framework. Compared to the Vietnamese Noun Phrase Chunking based on Conditional Random Fields [14] using only simple CRFs, which achieved an average recall and precision of 82.72% and 82.62% respectively, we achieved better results in chunking (with the same dataset splitting ratio). Experimental results show that the proposed method allows to obtain a high quality noun phrase chunking performance with 88.02%, 90.09%, and 89.01% of Precision, Recall, F1 score. And it helps to improve the person search results in TBPS by 1.325%, 0.675% and 0.25%, of accuracies at the top-1, top-5 at the top-10 respectively.