Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting
In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work.
Code (1)
Tasks
Learning-To-RankRetrievalSimilar Papers 제목 키워드 기반
Ranking of Bangla Word Graph using Graph-based Ranking Algorithms
Ranking words is an important way to summarize a text or to retrieve information. A word graph is a way to represent the words of a sentence or a text as the vertices of a graph and to show the relationship among the wor…
Topic representation: finding more representative words in topic models
The top word list, i.e., the top-M words with highest marginal probability in a given topic, is the standard topic representation in topic models. Most of recent automatical topic labeling algorithms and popular topic qu…
RerankingTopic ModelsWordRank: Learning Word Embeddings via Robust Ranking
Embedding words in a vector space has gained a lot of attention in recent years. While state-of-the-art methods provide efficient computation of word similarities via a low-dimensional matrix embedding, their motivation …
Learning Word EmbeddingsWord EmbeddingsWord SimilarityIs this word borrowed? An automatic approach to quantify the likeliness of borrowing in social media
Code-mixing or code-switching are the effortless phenomena of natural switching between two or more languages in a single conversation. Use of a foreign word in a language; however, does not necessarily mean that the spe…
ClusteringUnsupervised Candidate Ranking for Lexical Substitution via Holistic Sentence Semantics
A key subtask in lexical substitution is ranking the given candidate words. A common approach is to replace the target word with a candidate in the original sentence and feed the modified sentence into a model to capture…
Semantic Similarity