37,399 research outputs found
Diversity, Assortment, Dissimilarity, Variety: A Study of Diversity Measures Using Low Level Features for Video Retrieval
In this paper we present a number of methods for re-ranking video search results in order to introduce diversity into the set of search results. The usefulness of these approaches is evaluated in comparison with similarity based measures, for the TRECVID 2007 collection and tasks [11]. For the MAP of the search results we find that some of our approaches perform as well as similarity based methods. We also find that some of these results can improve the P@N values for some of the lower N values. The most successful of these approaches was then implemented in an interactive search system for the TRECVID 2008 interactive search tasks. The responses from the users indicate that they find the more diverse search results extremely useful
Grounding semantics in robots for Visual Question Answering
In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning
Drug prescription support in dental clinics through drug corpus mining
The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients
Automated legal sensemaking: the centrality of relevance and intentionality
Introduction: In a perfect world, discovery would ideally be conducted by the senior litigator who is
responsible for developing and fully understanding all nuances of their client’s legal strategy. Of
course today we must deal with the explosion of electronically stored information (ESI) that
never is less than tens-of-thousands of documents in small cases and now increasingly involves
multi-million-document populations for internal corporate investigations and litigations.
Therefore scalable processes and technologies are required as a substitute for the authority’s
judgment. The approaches taken have typically either substituted large teams of surrogate
human reviewers using vastly simplified issue coding reference materials or employed
increasingly sophisticated computational resources with little focus on quality metrics to insure
retrieval consistent with the legal goal. What is required is a system (people, process, and
technology) that replicates and automates the senior litigator’s human judgment.
In this paper we utilize 15 years of sensemaking research to establish the minimum acceptable
basis for conducting a document review that meets the needs of a legal proceeding. There is
no substitute for a rigorous characterization of the explicit and tacit goals of the senior litigator.
Once a process has been established for capturing the authority’s relevance criteria, we argue
that literal translation of requirements into technical specifications does not properly account for
the activities or states-of-affairs of interest. Having only a data warehouse of written records, it
is also necessary to discover the intentions of actors involved in textual communications. We
present quantitative results for a process and technology approach that automates effective
legal sensemaking
Profiling of OCR'ed Historical Texts Revisited
In the absence of ground truth it is not possible to automatically determine
the exact spectrum and occurrences of OCR errors in an OCR'ed text. Yet, for
interactive postcorrection of OCR'ed historical printings it is extremely
useful to have a statistical profile available that provides an estimate of
error classes with associated frequencies, and that points to conjectured
errors and suspicious tokens. The method introduced in Reffle (2013) computes
such a profile, combining lexica, pattern sets and advanced matching techniques
in a specialized Expectation Maximization (EM) procedure. Here we improve this
method in three respects: First, the method in Reffle (2013) is not adaptive:
user feedback obtained by actual postcorrection steps cannot be used to compute
refined profiles. We introduce a variant of the method that is open for
adaptivity, taking correction steps of the user into account. This leads to
higher precision with respect to recognition of erroneous OCR tokens. Second,
during postcorrection often new historical patterns are found. We show that
adding new historical patterns to the linguistic background resources leads to
a second kind of improvement, enabling even higher precision by telling
historical spellings apart from OCR errors. Third, the method in Reffle (2013)
does not make any active use of tokens that cannot be interpreted in the
underlying channel model. We show that adding these uninterpretable tokens to
the set of conjectured errors leads to a significant improvement of the recall
for error detection, at the same time improving precision
Enabling Quality Control for Entity Resolution: A Human and Machine Cooperation Framework
Even though many machine algorithms have been proposed for entity resolution,
it remains very challenging to find a solution with quality guarantees. In this
paper, we propose a novel HUman and Machine cOoperation (HUMO) framework for
entity resolution (ER), which divides an ER workload between the machine and
the human. HUMO enables a mechanism for quality control that can flexibly
enforce both precision and recall levels. We introduce the optimization problem
of HUMO, minimizing human cost given a quality requirement, and then present
three optimization approaches: a conservative baseline one purely based on the
monotonicity assumption of precision, a more aggressive one based on sampling
and a hybrid one that can take advantage of the strengths of both previous
approaches. Finally, we demonstrate by extensive experiments on real and
synthetic datasets that HUMO can achieve high-quality results with reasonable
return on investment (ROI) in terms of human cost, and it performs considerably
better than the state-of-the-art alternatives in quality control.Comment: 12 pages, 11 figures. Camera-ready version of the paper submitted to
ICDE 2018, In Proceedings of the 34th IEEE International Conference on Data
Engineering (ICDE 2018
- …