3,549 research outputs found
Visual Re-ranking with Natural Language Understanding for Text Spotting
Many scene text recognition approaches are based on purely visual information
and ignore the semantic relation between scene and text. In this paper, we
tackle this problem from natural language processing perspective to fill the
gap between language and vision. We propose a post-processing approach to
improve scene text recognition accuracy by using occurrence probabilities of
words (unigram language model), and the semantic correlation between scene and
text. For this, we initially rely on an off-the-shelf deep neural network,
already trained with a large amount of data, which provides a series of text
hypotheses per input image. These hypotheses are then re-ranked using word
frequencies and semantic relatedness with objects or scenes in the image. As a
result of this combination, the performance of the original network is boosted
with almost no additional cost. We validate our approach on ICDAR'17 dataset.Comment: Accepted by ACCV 2018. arXiv admin note: substantial text overlap
with arXiv:1810.0977
Visual re-ranking with natural language understanding for text spotting
The final publication is available at link.springer.comMany scene text recognition approaches are based on purely visual information and ignore the semantic relation between scene and text. In this paper, we tackle this problem from natural language processing perspective to fill the gap between language and vision. We propose a post processing approach to improve scene text recognition accuracy by using occurrence probabilities of words (unigram language model), and the semantic correlation between scene and text. For this, we initially rely on an off-the-shelf deep neural network, already trained with large amount of data, which provides a series of text hypotheses per input image. These hypotheses are then re-ranked using word frequencies and semantic relatedness with objects or scenes in the image. As a result of this combination, the performance of the original network is boosted with almost no additional cost. We validate our approach on ICDAR'17 dataset.Peer ReviewedPostprint (author's final draft
Search Engine Similarity Analysis: A Combined Content and Rankings Approach
How different are search engines? The search engine wars are a favorite topic
of on-line analysts, as two of the biggest companies in the world, Google and
Microsoft, battle for prevalence of the web search space. Differences in search
engine popularity can be explained by their effectiveness or other factors,
such as familiarity with the most popular first engine, peer imitation, or
force of habit. In this work we present a thorough analysis of the affinity of
the two major search engines, Google and Bing, along with DuckDuckGo, which
goes to great lengths to emphasize its privacy-friendly credentials. To do so,
we collected search results using a comprehensive set of 300 unique queries for
two time periods in 2016 and 2019, and developed a new similarity metric that
leverages both the content and the ranking of search responses. We evaluated
the characteristics of the metric against other metrics and approaches that
have been proposed in the literature, and used it to (1) investigate the
similarities of search engine results, (2) the evolution of their affinity over
time, (3) what aspects of the results influence similarity, and (4) how the
metric differs over different kinds of search services. We found that Google
stands apart, but Bing and DuckDuckGo are largely indistinguishable from each
other.Comment: Shorter version of this paper was accepted in the 21st International
Conference on Web Information Systems Engineering (WISE 2020). The final
authenticated version is available online at
https://doi.org/10.1007/978-3-030-62008-0_
A Perspectival Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, Wikipedia, and YouTube
Contrary to Google Search's mission of delivering information from "many
angles so you can form your own understanding of the world," we find that
Google and its most prominent returned results -- Wikipedia and YouTube, simply
reflect the narrow set of cultural stereotypes tied to the search language for
complex topics like "Buddhism," "Liberalism," "colonization," "Iran" and
"America." Simply stated, they present, to varying degrees, distinct
information across the same search in different languages (we call it 'language
bias'). Instead of presenting a global picture of a complex topic, our online
searches turn us into the proverbial blind person touching a small portion of
an elephant, ignorant of the existence of other cultural perspectives. The
language we use to search ends up as a cultural filter to promote ethnocentric
views, where a person evaluates other people or ideas based on their own
culture. We also find that language bias is deeply embedded in ChatGPT. As it
is primarily trained on English language data, it presents the Anglo-American
perspective as the normative view, reducing the complexity of a multifaceted
issue to the single Anglo-American standard. In this paper, we present evidence
and analysis of language bias and discuss its larger social implications.
Toward the end of the paper, we propose a potential framework of using
automatic translation to leverage language bias and argue that the task of
piecing together a genuine depiction of the elephant is a challenging and
important endeavor that deserves a new area of research in NLP and requires
collaboration with scholars from the humanities to create ethically sound and
socially responsible technology together
Effect of heuristics on serendipity in path-based storytelling with linked data
Path-based storytelling with Linked Data on the Web provides users the ability to discover concepts in an entertaining and educational way. Given a query context, many state-of-the-art pathfinding approaches aim at telling a story that coincides with the user's expectations by investigating paths over Linked Data on the Web. By taking into account serendipity in storytelling, we aim at improving and tailoring existing approaches towards better fitting user expectations so that users are able to discover interesting knowledge without feeling unsure or even lost in the story facts. To this end, we propose to optimize the link estimation between - and the selection of facts in a story by increasing the consistency and relevancy of links between facts through additional domain delineation and refinement steps. In order to address multiple aspects of serendipity, we propose and investigate combinations of weights and heuristics in paths forming the essential building blocks for each story. Our experimental findings with stories based on DBpedia indicate the improvements when applying the optimized algorithm
A Comparison of Source Distribution and Result Overlap in Web Search Engines
When it comes to search engines, users generally prefer Google. Our study
aims to find the differences between the results found in Google compared to
other search engines. We compared the top 10 results from Google, Bing,
DuckDuckGo, and Metager, using 3,537 queries generated from Google Trends from
Germany and the US. Google displays more unique domains in the top results than
its competitors. Wikipedia and news websites are the most popular sources
overall. With some top sources dominating search results, the distribution of
domains is also consistent across all search engines. The overlap between
Google and Bing is always under 32%, while Metager has a higher overlap with
Bing than DuckDuckGo, going up to 78%. This study shows that the use of another
search engine, especially in addition to Google, provides a wider variety in
sources and might lead the user to find new perspectives.Comment: Submitted to the 85th Annual Meeting of the Association for
Information Science & Technology and will be published in the conference
proceeding
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