Multi-faceted Assessment of Trademark Similarity

Trademarks are intellectual property assets with potentially high reputational value. Their infringement may lead to lost revenue, lower profits and damages to brand reputation. A test normally conducted to check whether a trademark is highly likely to infringe other existing, already registered, trademarks is called a likelihood of confusion test. One of the most influential factors in this test is establishing similarity in appearance, meaning or sound. However, even though the trademark registration process suggests a multi-faceted similarity assessment, relevant research in expert systems mainly focuses on computing individual aspects of similarity between trademarks. Therefore, this paper contributes to the knowledge in this field by proposing a method, which, similar to the way people perceive trademarks, blends together the three fundamental aspects of trademark similarity and produces an aggregated score based on the individual visual, semantic and phonetic assessments. In particular, semantic similarity is a new aspect, which has not been considered by other researchers in approaches aimed at providing decision support in trademark similarity assessment. Another specific scientific contribution of this paper is the innovative integration, using a fuzzy engine, of three independent assessments, which collectively provide a more balanced and human-centered view on potential infringement problems. In addition, the paper introduces the concept of degree of similarity since the line between similar and dissimilar trademarks is not always easy to define especially when dealing with blending three very different assessments. The work described in the paper is evaluated using a database comprising 1,400 trademarks compiled from a collection of real legal cases of trademark disputes. The evaluation involved two experiments. The first experiment employed information retrieval measures to test the classification accuracy of the proposed method while the second used human collective opinion to examine correlations between the trademark scoring/rating and the ranking of the proposed method, and human judgment. In the first experiment, the proposed method improved the F-score, precision and accuracy of classification by 12.5%, 35% and 8.3%, respectively, against the best score computed using individual similarity. In the second experiment, the proposed method produced a perfect positive Spearman rank correlation score of 1.00 in the ranking task and a pairwise Pearson correlation score of 0.92 in the rating task. The test of significance conducted on both scores rejected the null hypotheses of the experiment and showed that both scores correlated well with collective human judgment. The combined overall assessment could add value to existing support systems and be beneficial for both trademark examiners and trademark applicants. The method could be further used in addressing recent cyberspace phenomena related to trademark infringement such as customer hijacking and cybersquatting. Keywords—Trademark assessment, trademark infringement, trademark retrieval, degree of similarity, fuzzy aggregation, semantic similarity, phonetic similarity, visual similarity

The place(s) of pain and its linguistic descriptions - the morphology and lexico-semantics of English pain descriptors : a cognitive linguistic perspective

This paper aims at identifying the place of pain in language by analysing, in most part, adjectival pain descriptors (in terms of their morphology and lexico-semantics), especially the ones present in the English (original) version of the McGill Pain Questionnaire (Melzack & Torgerson 1971, Melzack 1975), mainly through the cognitive linguistic prisms. This self-report questionnaire (given by doctors to their patients so that the latter can describe their pain in terms of various qualities and intensity) has for years been successfully employed in clinical settings, but its diagnostic potency may be to some extent compromized by the interplay of both linguistic and extra-linguistic factors. Thus, in order to check how potent these MPQ descriptors are (and whether they are still potent), the present analysis is enriched with the discussion of these adjectival pain collocations not only in the context of the MPQ, but also in other ‘localizations’, be it an alternative pain questionnaire, and fragments of academic articles and books addressing certain types/ qualities of pain. Adopting such an approach provides the chance to glimpse the pain descriptors in question in the broader context, that is, how pain is ‘located’ in the academic discourse of pain experts and clinicians, but also, and perhaps even more importantly, how ‘lay’ pain sufferers ‘position’ their pain(s). The analysis carried out and the conclusions drawn reveal an interesting ‘place’—a point of convergence, an intersection of pain (as a multi-layered construct) and metaphorinfused language. My conviction, then, is that pain is placed in and predominantly expressed via metaphoric language at various (less and more subtle) levels, and also that pain metaphor is not only a research object, but may additionally prove an efficient (diagnostic) research tool

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Application of Machine Learning within Visual Content Production

We are living in an era where digital content is being produced at a dazzling pace. The heterogeneity of contents and contexts is so varied that a numerous amount of applications have been created to respond to people and market demands. The visual content production pipeline is the generalisation of the process that allows a content editor to create and evaluate their product, such as a video, an image, a 3D model, etc. Such data is then displayed on one or more devices such as TVs, PC monitors, virtual reality head-mounted displays, tablets, mobiles, or even smartwatches. Content creation can be simple as clicking a button to film a video and then share it into a social network, or complex as managing a dense user interface full of parameters by using keyboard and mouse to generate a realistic 3D model for a VR game. In this second example, such sophistication results in a steep learning curve for beginner-level users. In contrast, expert users regularly need to refine their skills via expensive lessons, time-consuming tutorials, or experience. Thus, user interaction plays an essential role in the diffusion of content creation software, primarily when it is targeted to untrained people. In particular, with the fast spread of virtual reality devices into the consumer market, new opportunities for designing reliable and intuitive interfaces have been created. Such new interactions need to take a step beyond the point and click interaction typical of the 2D desktop environment. The interactions need to be smart, intuitive and reliable, to interpret 3D gestures and therefore, more accurate algorithms are needed to recognise patterns. In recent years, machine learning and in particular deep learning have achieved outstanding results in many branches of computer science, such as computer graphics and human-computer interface, outperforming algorithms that were considered state of the art, however, there are only fleeting efforts to translate this into virtual reality. In this thesis, we seek to apply and take advantage of deep learning models to two different content production pipeline areas embracing the following subjects of interest: advanced methods for user interaction and visual quality assessment. First, we focus on 3D sketching to retrieve models from an extensive database of complex geometries and textures, while the user is immersed in a virtual environment. We explore both 2D and 3D strokes as tools for model retrieval in VR. Therefore, we implement a novel system for improving accuracy in searching for a 3D model. We contribute an efficient method to describe models through 3D sketch via an iterative descriptor generation, focusing both on accuracy and user experience. To evaluate it, we design a user study to compare different interactions for sketch generation. Second, we explore the combination of sketch input and vocal description to correct and fine-tune the search for 3D models in a database containing fine-grained variation. We analyse sketch and speech queries, identifying a way to incorporate both of them into our system's interaction loop. Third, in the context of the visual content production pipeline, we present a detailed study of visual metrics. We propose a novel method for detecting rendering-based artefacts in images. It exploits analogous deep learning algorithms used when extracting features from sketches

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Bilkent News Portal : a system with new event detection and tracking capabilities

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 65-71.News portal services such as browsing, retrieving, and filtering have become an important research and application area as a result of information explosion on the Internet. In this work, we give implementation details of Bilkent News Portal that contains various novel features ranging from personalization to new event detection and tracking capabilities aiming at addressing the needs of news-consumers. The thesis presents the architecture, data and file structures, and experimental foundations of the news portal. For the implementation and evaluation of the new event detection and tracking component, we developed a test collection: BilCol2005. The collection contains 209,305 documents from the entire year of 2005 and involves several events in which eighty of them are annotated by humans. It enables empirical assessment of new event detection and tracking algorithms on Turkish. For the construction of our test collection, a web application, ETracker, is developed by following the guidelines of the TDT research initiative. Furthermore, we experimentally evaluated the impact of various parameters in information retrieval (IR) that has to be decided during the implementation of a news portal that provides filtering and retrieval capabilities. For this purpose, we investigated the effects of stemming, document length, query length, and scalability issues.Öcalan, Hüseyin ÇağdaşM.S

Jaina-Prosopography I: Sociology of Jaina-Names

The sociology of Indian names is still in its infancy. The present article explores theoretical and pragmatic solutions for two elementary difficulties, faced by all prosopographies and text-encoding initiatives, namely, the creation of standardised lists of names, and the accurate identification of individuals. Its central concern is the analysis of the structure of Jaina names, particularly monastic names, which entail an entire sociology of the Jaina tradition, and require custom-made coding schemes to be accurately represented in a prosopological database. After analysing the classification of name-types in Jaina-scriptures, compared with contemporary semantics and pragmatics, and methodological conundrums of coding Jaina householder and monastic names, a suitable coding scheme is proposed, and a “naming formula” for Jaina monastic “full names” from the perspective of functional grammar. The study will finally show, at hand of the example of the names of Mahāvīra, that problems of identification of individuals on the basis of Jaina monastic names are similar to problems of identification in Jaina biography or the iconography of the Jinas

Semi-Supervised Learning For Identifying Opinions In Web Content

Thesis (Ph.D.) - Indiana University, Information Science, 2011Opinions published on the World Wide Web (Web) offer opportunities for detecting personal attitudes regarding topics, products, and services. The opinion detection literature indicates that both a large body of opinions and a wide variety of opinion features are essential for capturing subtle opinion information. Although a large amount of opinion-labeled data is preferable for opinion detection systems, opinion-labeled data is often limited, especially at sub-document levels, and manual annotation is tedious, expensive and error-prone. This shortage of opinion-labeled data is less challenging in some domains (e.g., movie reviews) than in others (e.g., blog posts). While a simple method for improving accuracy in challenging domains is to borrow opinion-labeled data from a non-target data domain, this approach often fails because of the domain transfer problem: Opinion detection strategies designed for one data domain generally do not perform well in another domain. However, while it is difficult to obtain opinion-labeled data, unlabeled user-generated opinion data are readily available. Semi-supervised learning (SSL) requires only limited labeled data to automatically label unlabeled data and has achieved promising results in various natural language processing (NLP) tasks, including traditional topic classification; but SSL has been applied in only a few opinion detection studies. This study investigates application of four different SSL algorithms in three types of Web content: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. SSL algorithms are also evaluated for their effectiveness in sparse data situations and domain adaptation. Research findings suggest that, when there is limited labeled data, SSL is a promising approach for opinion detection in Web content. Although the contributions of SSL varied across data domains, significant improvement was demonstrated for the most challenging data domain--the blogosphere--when a domain transfer-based SSL strategy was implemented