In this paper, we define a new cosine similarity between two interval valued neutrosophic sets based on Bhattacharya’s distance [19]. The notions of interval valued neutrosophic sets (IVNS, for short) will be used as vector representations in 3D-vector space. Based on the comparative analysis of the existing similarity measures for IVNS, we find that our proposed similarity measure is better and more robust. An illustrative example of the pattern recognition shows that the proposed method is (...) simple and effective. (shrink)

We propose the cosine similarity measures for intuitionistic fuzzy linguistic sets and interval-valued intuitionistic fuzzy linguistic sets, which are expressed by the linguistic scale function based on the cosine function. Then, the weighted cosine similarity measure and the ordered weighted cosine similarity measure for IFLSs and IVIFLSs are introduced by taking into account the importance of each element, and the properties of the cosine similarity measures are also given. The (...) main advantage of the proposed cosine similarity measures is that the decision-makers can flexibly select the linguistic scale function depending on the actual semantic situation. Finally, we present the application of the cosine similarity measures for intuitionistic fuzzy linguistic term sets and interval-valued intuitionistic fuzzy linguistic term sets to pattern recognition and medical diagnosis, and the existing cosine similarity measures are compared with the proposed cosine similarity measures by the illustrative example. (shrink)

In this paper, the cosine similarity measure of neutrosophic refined (multi-) sets is proposed and its properties are studied. The concept of this cosine similarity measure of neutrosophic refined sets is the extension of improved cosine similarity measure of single valued neutrosophic. Finally, using this cosine similarity measure of neutrosophic refined set, the application of medical diagnosis is presented.

We present the interval-valued intuitionistic fuzzy ordered weighted cosine similarity measure in this paper, which combines the interval-valued intuitionistic fuzzy cosine similarity measure with the generalized ordered weighted averaging operator. The main advantage of the IVIFOWCS measure provides a parameterized family of similarity measures, and the decision maker can use the IVIFOWCS measure to consider a lot of possibilities and select the aggregation operator in accordance with his interests. We have studied (...) some of its main properties and particular cases such as the interval-valued intuitionistic fuzzy ordered weighted arithmetic cosine similarity measure and the interval-valued intuitionistic fuzzy maximum cosine similarity measure. The IVIFOWCS measure not only is a generalization of some similarity measure, but also it can deal with the correlation of different decision matrices for interval-valued intuitionistic fuzzy values. Furthermore, we present an application of IVIFOWCS measure to the group decision-making problem. Finally the existing similarity measures are compared with the IVIFOWCS measure by an illustrative example. (shrink)

In this paper, the sine, cosine and cotangent similarity measures of interval rough neutrosophic sets is proposed. Some properties of the proposed measures are discussed. We have proposed multi attribute decision making approaches based on proposed similarity measures. To demonstrate the applicability, a numerical example is solved.

Automatic synonym extraction plays an important role in many natural language processing systems, such as those involving information retrieval and question answering. Recently, research has focused on extracting semantic relations from word embeddings since they capture relatedness and similarity between words. However, using word embeddings alone poses problems for synonym extraction because it cannot determine whether the relation between words is synonymy or some other semantic relation. In this paper, we present a novel solution for this problem by proposing (...) the SynoExtractor pipeline, which can be used to filter similar word embeddings to retain synonyms based on specified linguistic rules. Our experiments were conducted using KSUCCA and Gigaword embeddings and trained with CBOW and SG models. We evaluated automatically extracted synonyms by comparing them with Alma’any Arabic synonym thesauri. We also arranged for a manual evaluation by two Arabic linguists. The results of experiments we conducted show that using the SynoExtractor pipeline enhances the precision of synonym extraction compared to using the cosine similarity measure alone. SynoExtractor obtained a 0.605 mean average precision for the King Saud University Corpus of Classical Arabic with 21% improvement over the baseline and a 0.748 MAP for the Gigaword corpus with 25% improvement. SynoExtractor outperformed the Sketch Engine thesaurus for synonym extraction by 32% in terms of MAP. Our work shows promising results for synonym extraction suggesting that our method can also be used with other languages. (shrink)

Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to correctly recommend relevant services for users by using the recommendation method based on location and collaborative filtering. The similarity measure between users is low. This paper proposes a fusion method based on KL divergence and cosine similarity. KL divergence and cosine similarity have advantages by comparing three similar metrics at different K values. Using the fusion method of the two, (...) the user’s similarity with the preference is reused. By comparing the location-based collaborative filtering algorithm, user-based collaborative filtering algorithm, and user recommendation algorithm, the proposed method has the preparation rate, recall rate, and experimental effect advantage. In different median values, the proposed method also has an advantage in experimental results. (shrink)

Regular polysemes are sets of ambiguous words that all share the same relationship between their meanings, such as CHICKEN and LOBSTER both referring to an animal or its meat. To probe how a distributional semantic model, here exemplified by bidirectional encoder representations from transformers (BERT), represents regular polysemy, we analyzed whether its embeddings support answering sense analogy questions similar to “is the mapping between CHICKEN (as an animal) and CHICKEN (as a meat) similar to that which maps between LOBSTER (as (...) an animal) to LOBSTER (as a meat)?” We did so using the LRcos model, which combines a logistic regression classifier of different categories (e.g., animal vs. meat) with a measure of cosine similarity. We found that (a) the model was sensitive to the shared structure within a given regular relationship; (b) the shared structure varies across different regular relationships (e.g., animal/meat vs. location/organization), potentially reflective of a “regularity continuum;” (c) some high-order latent structure is shared across different regular relationships, suggestive of a similar latent structure across different types of relationships; and (d) there is a lack of evidence for the aforementioned effects being explained by meaning overlap. Lastly, we found that both components of the LRcos model made important contributions to accurate responding and that a variation of this method could yield an accuracy boost of 10% in answering sense analogy questions. These findings enrich previous theoretical work on regular polysemy with a computationally explicit theory and methods, and provide evidence for an important organizational principle for the mental lexicon and the broader conceptual knowledge system. (shrink)

Increasingly, taxonomies are being developed and used by industry practitioners to facilitate information interoperability and retrieval. Within a single industrial domain, there exist many taxonomies that are intended for different applications. Industry specific taxonomies often represent the vocabularies that are commonly used by the practitioners. Their jobs are multi-faceted, which include checking for code and regulatory compliance. As such, it will be very desirable if industry practitioners are able to easily locate and browse regulations of interest. In practice, multiple sources (...) of government regulations exist and they are often organized and classified by the needs of the issuing agencies that enforce them rather than the needs of the communities that use them. One way to bridge these two distinct needs is to develop methods and tools that enable practitioners to browse and retrieve government regulations using their own terms and vocabularies, for example, via existing industry taxonomies. The mapping from a single taxonomy to a single regulation is a trivial keyword matching task. We examine a relatedness analysis approach for mapping a single taxonomy to multiple regulations. We then present an approach for mapping multiple taxonomies to a single regulation by measuring the relatedness of concepts. Cosine similarity, Jaccard coefficient and market basket analysis are used to measure the semantic relatedness between concepts from two different taxonomies. Preliminary evaluations of the three relatedness analysis measures are performed using examples from the civil engineering and building industry. These examples illustrate the potential benefits of regulatory usage from the mapping between various taxonomies and regulations. (shrink)

This paper presents the results of a research work that aims to determine whether the upstream OCR correction can significantly improve the results of the Named Entity Recognition (NER) task. The experiments were applied to the ELTeC and Very Big Library (TGB) corpora. Our objective is to establish (i) a typology of OCR contaminations from the Kraken and Tesseract tools and (ii) a typology of errors in the automatic correction produced by the JamSpell tool. As part of our evaluation, we (...) study the intersections between the NEs detected by the spaCy tool in the reference texts and the two OCR versions on the one hand, and on the other hand the cosine similarity used to measure the textual distance between these versions. Our results show that automatic correction introduces biases in the textual data and that filtering the output NEs seems to be a more promising approach. Finally, we find that incorrect NEs are overwhelmingly short and hapax entities in corpus, and that the entity length and document frequency are the discriminatory criteria to exclude candidate NEs with a precision of over 98%. (shrink)

Judgments of character traits tend to be overcorrelated, a bias known as the halo effect. We conducted two studies to test an explanation of the effect based on shared lexical context and connotation. Study 1 tested whether the context similarity of trait names could explain 39 participants’ ratings of the probability that two traits would co-occur. Over 126 trait pairs, cosine similarity between the word2vec vectors of the two words was a reliable predictor of the human judgments (...) of trait co-occurrence probability (cross-validated r2 =.19, p <.001). Two measures related to word similarity increased the variation accounted for in the human judgments to 45%, cross-validated (p <.001). In Experiment 2, 40 different participants judged similarity of word meaning within the pairs, confirming that the word pairs were not simply synonymous (Average [SD] = 40.8/100 [13.1/100]). Shared lexical context and word connotation play a role in shaping the halo effect. (shrink)

Decisions are taken by humans very often during professional as well as leisure activities. It is particularly evident during surfing the Internet: selecting web sites to explore, choosing needed information in search engine results or deciding which product to buy in an on-line store. Recommender systems are electronic applications, the aim of which is to support humans in this decision making process. They are widely used in many applications: adaptive WWW servers, e-learning, music and video preferences, internet stores etc. In (...) on-line solutions, such as e-shops or libraries, the aim of recommendations is to show customers the products which they are probably interested in. As input data the following are taken: shopping basket archives, ratings of the products or servers log files.The article presents a solution of recommender system which helps users to select an interesting product. The system analyses data from other customers' ratings of the products. It uses clustering methods to find similarities among the users and proposed techniques to identify users' profiles. The system was implemented in Apache Mahout environment and tested on a movie database. Selected similarity measures are based on: Euclidean distance, cosine as well as correlation coefficient and loglikehood function. (shrink)

Programming artificial intelligence to make fairness assessments of texts through top-down rules, bottom-up training, or hybrid approaches, has presented the challenge of defining cross-cultural fairness. In this paper a simple method is presented which uses vectors to discover if a verb is unfair or fair. It uses already existing relational social ontologies inherent in Word Embeddings and thus requires no training. The plausibility of the approach rests on two premises. That individuals consider fair acts those that they would be willing (...) to accept if done to themselves. Secondly, that such a construal is ontologically reflected in Word Embeddings, by virtue of their ability to reflect the dimensions of such a perception. These dimensions being: responsibility vs. irresponsibility, gain vs. loss, reward vs. sanction, joy vs. pain, all as a single vector. The paper finds it possible to quantify and qualify a verb as fair or unfair by calculating the cosine similarity of the said verb’s embedding vector against FairVec—which represents the above dimensions. We apply this to Glove and Word2Vec embeddings. Testing on a list of verbs produces an F1 score of 95.7, which is improved to 97.0. Lastly, a demonstration of the method’s applicability to sentence measurement is carried out. (shrink)

This paper introduces a refined single-valued neutrosophic set (RSVNS) and presents a similarity measure of RSVNSs. Then a multicriteria decision-making method with RSVNS information is developed based on the similarity measure of RSVNSs. By the similarity measure between each alternative and the ideal solution (ideal alternative), all the alternatives can be ranked and the best one can be selected as well. Finally, an actual example on the selecting problems of construction projects demonstrates the application (...) and effectiveness of the proposed method. (shrink)

Smarandache (1995) defined the notion of neutrosophic sets, which is a generalization of Zadeh's fuzzy set and Atanassov's intuitionistic fuzzy set. In this paper, we first develop some similarity measures of neutrosophic sets. We will present a method to calculate the distance between neutrosophic sets (NS) on the basis of the Hausdorff distance. Then we will use this distance to generate a new similarity measure to calculate the degree of similarity between NS. Finally we will prove (...) some properties of the proposed similarity measures. (shrink)

Network similarity measures have proven essential in the field of network analysis. Also, topological indices have been used to quantify the topology of networks and have been well studied. In this paper, we employ a new topological index which we call the Ediz eccentric connectivity index. We use this quantity to define network similarity measures as well. First, we determine the extremal value of the Ediz eccentric connectivity index on some network classes. Second, we compare the network (...) class='Hi'>similarity measure based on the Ediz eccentric connectivity index with other well-known topological indices such as Wiener index, graph energy, Randić index, the largest eigenvalue, the largest Laplacian eigenvalue, and connectivity eccentric index. Numerical results underpin the usefulness of the chosen measures. They show that our new measure outperforms all others, except the one based on Wiener index. This means that the measure based on Wiener index is still the best, but the new one has certain advantage to some extent. (shrink)

In this paper, we propose some transfor mations based on the centroid points between single valued neutrosophic numbers. We introduce these trans formations according to truth, indeterminacy and falsity value of single valued neutrosophic numbers. We propose a new similarity measure based on falsity value between single valued neutrosophic sets. Then we prove some properties on new similarity measure based on falsity value between falsity value between single valued neutrosophic sets. Furthermore, we propose similarity (...) class='Hi'>measure based on falsity value between single valued neutrosophic sets based on the centroid points of transformed single valued neutrosophic numbers. We also apply the proposed similarity measure between single valued neutrosophic sets to deal with pattern recognition problems. (shrink)

A summary of recent experimental results shows that entanglement can be generated more easily than before, and that there are improved chances for its persistence. An eminent finding of Generalised Quantum Theory is the insight that the notion of entanglement can be extended, such that, e.g., psychological or psychophysical problem areas can be included, too. First, a general condition for entanglement to occur is given by the term ‘common prearranged context’. A formalised treatment requires a quantitative definition of the (...) class='Hi'>similarity or dissimilarity between two complex structures which takes their internal structures into account. After some specific remarks on distance, metrics, and semi-metrics in mathematics, a procedure is described for setting up a similarity function with the required properties. This procedure is in analogy with the two-step character of measurement and with the well-known properties of perspective notions. A general methodology can be derived for handling perspective notions. Finally, these concepts supply heuristic clues towards a formalised treatment of the notions of ‘meaning’ and ‘interpretation’. (shrink)

Clustering plays an important role in data mining, pattern recognition, and machine learning. Single-valued neutrosophic sets are useful means to describe and handle indeterminate and inconsistent information that fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To cluster the data represented by single-valued neutrosophic information, this article proposes single-valued neutrosophic clustering methods based on similarity measures between SVNSs. First, we define a generalized distance measure between SVNSs and propose two distance-based similarity measures of SVNSs. (...) Then, we present a clustering algorithm based on the similarity measures of SVNSs to cluster single-valued neutrosophic data. Finally, an illustrative example is given to demonstrate the application and effectiveness of the developed clustering methods. (shrink)

In this paper, we utilized triangular conorms. The essence of using S-norm is that the similarity order does not change using different norms. In fact, we are investigating for a new conception for calculating the similarity of two Fermatean fuzzy sets. For this purpose, utilizing an S-norm, we first present a formula for calculating the similarity of two Fermatean fuzzy values, so that they are truthful in similarity properties. Following that, we generalize a formula for calculating (...) the similarity of the two Fermatean fuzzy sets which prove truthful in similarity conditions. Finally, various numerical examples have been presented to elaborate this method. (shrink)

TheConceptual Feature framework predicts that word meaning is represented within a high-dimensional semantic space bounded by weighted contributions of perceptual, affective, and encyclopedic information. The ACF, like latent semantic analysis, is amenable to distance metrics between any two words. We applied predictions of the ACF framework to abstract words using eyetracking via an adaptation of the classical “visual word paradigm”. Healthy adults selected the lexical item most related to a probe word in a 4-item written word array comprising the target (...) and three distractors. The relation between the probe and each of the four words was determined using the semantic distance metrics derived from ACF ratings. Eye movement data indicated that the word that was most semantically related to the probe received more and longer fixations relative to distractors. Importantly, in sets where participants did not provide an overt behavioral response, the fixation rates were nonetheless significantly higher for targets than distractors, closely resembling trials where an expected response was given. Furthermore, ACF ratings which are based on individual words predicted eye fixation metrics of probe-target similarity at least as well as latent semantic analysis ratings which are based on word co-occurrence. The results provide further validation of Euclidean distance metrics derived from ACF ratings as a measure of one facet of the semantic relatedness of abstract words and suggest that they represent a reasonable approximation of the organization of abstract conceptual space. The data are also compatible with the broad notion that multiple sources of information shape the organization of abstract concepts. While the adapted “VWP” is potentially a more metacognitive task than the classical visual world paradigm, we argue that it offers potential utility for studying abstract word comprehension. (shrink)

The Abstract Conceptual Feature (ACF) framework predicts that word meaning is represented within a high‐dimensional semantic space bounded by weighted contributions of perceptual, affective, and encyclopedic information. The ACF, like latent semantic analysis, is amenable to distance metrics between any two words. We applied predictions of the ACF framework to abstract words using eyetracking via an adaptation of the classical “visual word paradigm” (VWP). Healthy adults (n = 20) selected the lexical item most related to a probe word in a (...) 4‐item written word array comprising the target and three distractors. The relation between the probe and each of the four words was determined using the semantic distance metrics derived from ACF ratings. Eye movement data indicated that the word that was most semantically related to the probe received more and longer fixations relative to distractors. Importantly, in sets where participants did not provide an overt behavioral response, the fixation rates were nonetheless significantly higher for targets than distractors, closely resembling trials where an expected response was given. Furthermore, ACF ratings which are based on individual words predicted eye fixation metrics of probe‐target similarity at least as well as latent semantic analysis ratings which are based on word co‐occurrence. The results provide further validation of Euclidean distance metrics derived from ACF ratings as a measure of one facet of the semantic relatedness of abstract words and suggest that they represent a reasonable approximation of the organization of abstract conceptual space. The data are also compatible with the broad notion that multiple sources of information (not restricted to sensorimotor and emotion information) shape the organization of abstract concepts. While the adapted “VWP” is potentially a more metacognitive task than the classical visual world paradigm, we argue that it offers potential utility for studying abstract word comprehension. (shrink)

This article analyses the relationship between the concept of single aspect similarity and proposed measures of similarity. More precisely, it compares eleven measures of similarity in terms of how well they satisfy a list of desiderata, chosen to capture common intuitions concerning the properties of similarity and the relations between similarity and dissimilarity. Three types of measures are discussed: similarity as commonality, similarity as a function of dissimilarity, and similarity as a joint (...) function of commonality and difference. Relative to the desiderata, it is found that a measure of the second type fares the best. However, rather than recommend this measure alone as a measure of similarity, it is suggested that there are at least three separate concepts of single aspect similarity, corresponding to the three types of measures. In light of this proposal, three of the eleven measures (and variants of these) are deemed acceptable. (shrink)

A similarity index allowing comparisons of human populations has been defined as the common “Percentage of Isoactive Genes” or PIG, which can be calculated from any gene frequency distribution characterizing two populations. The complement to one of this value has been proved to be a distance, a measure which can be used in most techniques of cluster analysis as well as in usual representations of multivariated data (dendrograms, etc...). Furthermore, the formula can be generalized to a set of (...) populations. From a biological point of vue, PIG values are particularly meaningful, whereas other previously defined indices which tend to measure similarity or difference between populations neither have such an advantage, nor can they be expressed by a single and clear number like a percentage. Moreover,comparisons using PIG indices depend at first on the fluctuations of the most frequent genes of a distribution; on the other hand, different measures principally reflect the variations of low frequency genes, which unfortunately are nearly always poorly estimated, due to weak samples of populations.Interestingly, PIG values can be applied to any frequency distribution of any kind of objects. They can also extend to a whole set of distributions by using a mean value. When applying these measures to a series of polymorphic genetical systems such as ABO, Rhesus, MNSs, Gm and HLA, one can account for the variability or the homogeneity particular to the different human groups, with simple and objective criteria. (shrink)

Measuring perceived similarity is an important issue in visual perception of faces, since a measure of the perceived similarity between faces may be used to investigate fundamental tasks like face categorization and recognition. Despite its fundamental role, measuring perceived similarity between faces is not trivial from both a theoretical and methodological point of view. In this paper we present theoretical arguments that undermine the method currently most used to measure perceived similarity between faces in (...) visual perception, and we propose an alternative method. We finally compare the two methods and find some empirical evidence that the proposed method can provide a more reliable evaluation of the perceived similarity between faces. (shrink)

Around the world, many liberal democracies are facing in recent years serious challenges and threats emanating inter alia from the rise of political populism. Such challenges and threats are feeding an almost existential discourse about the crisis of democracy, and recent legal and political developments in Israel aimed at weakening the power of the Supreme Court and other rule of law institutions have also been described in such terms. This Article primarily intends to explore the relevance of the discourse surrounding (...) the decline of liberal democracy, and its possible relevance for Israeli democracy, by examining the principal similarities and differences between specific legislative and administrative measures recently taken or contemplated in Israel and in two Central European states: Poland and Hungary. We focus on three sets of illiberal measures adopted or contemplated in Hungary, Poland, and Israel: (i) measures directed at limiting the power of the judiciary; (ii) measures intended to restrict the operation of civil society organizations; and (iii) measures directed at curbing dissent to governmental policies and at influencing the discourse in the media and academia. Although Israeli democratic institutions still retain much of their independence and vitality, we nonetheless find some degree of similarity between measures taken or contemplated by Hungary, Poland, and Israel, despite the many differences between their legal systems, historical contexts, political cultures, and the distinct stages of backsliding they seem to experience. (shrink)

In the domain of legal information retrieval, an important challenge is to compute similarity between two legal documents. Precedents play an important role in The Common Law system, where lawyers need to frequently refer to relevant prior cases. Measuring document similarity is one of the most crucial aspects of any document retrieval system which decides the speed, scalability and accuracy of the system. Text-based and network-based methods for computing similarity among case reports have already been proposed in (...) prior works but not without a few pitfalls. Since legal citation networks are generally highly disconnected, network based metrics are not suited for them. Till date, only a few text-based and predominant embedding based methods have been employed, for instance, TF-IDF based approaches, Word2Vec and Doc2Vec based approaches. We investigate the performance of 56 different methodologies for computing textual similarity across court case statements when applied on a dataset of Indian Supreme Court Cases. Among the 56 different methods, thirty are adaptations of existing methods and twenty-six are our proposed methods. The methods studied include models such as BERT and Law2Vec. It is observed that the more traditional methods that rely on a bag-of-words representation performs better than the more advanced context-aware methods for computing document-level similarity. Finally we nominate, via empirical validation, five of our best performing methods as appropriate for measuring similarity between case reports. Among these five, two are adaptations of existing methods and the other three are our proposed methods. (shrink)

Similarity is one of the most important relations humans perceive, arguably subserving category learning and categorization, generalization and discrimination, judgment and decision making, and other cognitive functions. Researchers have proposed a wide range of representations and metrics that could be at play in similarity judgment, yet have not comprehensively compared the power of these representations and metrics for predicting similarity within and across different semantic categories. We performed such a comparison by pairing nine prominent vector semantic representations (...) with seven established similarity metrics that could operate on these representations, as well as supervised methods for dimensional weighting in the similarity function. This approach yields a factorial model structure with 126 distinct representation‐metric pairs, which we tested on a novel dataset of similarity judgments between pairs of cohyponymic words in eight categories. We found that cosine similarity and Pearson correlation were the overall best performing unweighted similarity functions, and that word vectors derived from free association norms often outperformed word vectors derived from text (including those specialized for similarity). Importantly, models that used human similarity judgments to learn category‐specific weights on dimensions yielded substantially better predictions than all unweighted approaches across all types of similarity functions and representations, although dimension weights did not generalize well across semantic categories, suggesting strong category context effects in similarity judgment. We discuss implications of these results for cognitive modeling and natural language processing, as well as for theories of the representations and metrics involved in similarity. (shrink)

We describe a method for automatic word sense disambiguation using a text corpus and a machine- readable dictionary (MRD). The method is based on word similarity and context similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain similar words. The circularity of this definition is resolved by an iterative, converging process, in which the system learns from the corpus a set of typical usages for each of the senses of (...) the polysemous word listed in the MRD. A new instance of a polysemous word is assigned the sense associated with the typical usage most similar to its context. Experiments show that this method performs well, and can learn even from very sparse training data. (shrink)

Proponents of meaning holism often invoke notions of meaning similarity and semantic spatiality in order to counter accusations that holism renders language unstable and chaotic. However, talk of such notions often falls short of being explicit and formal. In this paper I present an algebraically couched theory of inferential similarity and spatiality, motivated by measurement theory, and I apply it to the discussion of meaning holism. I argue that the proposed theory offers new and improved conceptual resources for (...) facing the challenges raised against the thesis. (shrink)

The automated identification of national implementations of European directives by text similarity techniques has shown promising preliminary results. Previous works have proposed and utilized unsupervised lexical and semantic similarity techniques based on vector space models, latent semantic analysis and topic models. However, these techniques were evaluated on a small multilingual corpus of directives and NIMs. In this paper, we utilize word and paragraph embedding models learned by shallow neural networks from a multilingual legal corpus of European directives and (...) national legislation to develop unsupervised semantic similarity systems to identify transpositions. We evaluate these models and compare their results with the previous unsupervised methods on a multilingual test corpus of 43 Directives and their corresponding NIMs. We also develop supervised machine learning models to identify transpositions and compare their performance with different feature sets. (shrink)

Similarity profiled association mining from time stamped transaction databases is an important topic of research relatively less addressed in the field of temporal data mining. Mining temporal patterns from these time series databases requires choosing and applying similarity measure for similarity computations and subsequently pruning temporal patterns. This research proposes a novel z-space based interest measure named as Krishna Sudarsana for time-stamped transaction databases by extending interest measure Srihass proposed in previous research. Krishna Sudarsana (...) is designed by using the product based fuzzy Gaussian membership function and performs similarity computations in z-space to determine the similarity degree between any two temporal patterns. The interest measure is designed by considering z-values between z = 0 and z = 3.09. Applying the Krishna Sudarsana requires moving the threshold value given by user to a different transformation space which is a defined as a function of standard deviation. In addition to proposing interest measure, new expressions for standard deviation and equivalent z-space threshold are derived for similarity computations. For experimental evaluation, we considered Naïve, Sequential and Spamine algorithms that applies Euclidean distance function and compared performance of these three approaches to Z-Spamine algorithm that uses Krishna Sudarsana by choosing various test cases. Experiment results proved the performance of the proposed approach is better to Sequential approach that uses snapshot database scan strategy and Spamine approach that uses lattice based database scan strategy. (shrink)

Is the term ‘racist’ being applied so widely that it is losing its moral force? Theorists and pundits from across the political spectrum think that it is. They call such a change of meaning “conceptual inflation” and argue that we should try to stop it by restricting the use of ‘racist’ or replacing ‘racist’ with new expressions. But what evidence do we have that ‘racist’ is inflated? Economists do not track currency inflation with mere vibes; they use measurements such as (...) the consumer price index. For example, using CPI, it is observable that there has been considerable US dollar inflation over the last three decades or so: $1.00 in 1989 has the purchasing power of $2.54 in 2024. If ‘racist’ has undergone a similar transformation, as critics of conceptual inflation contend, how could that be measured? Before plunging into the normative debate about whether conceptual inflation is good or bad, we should gather systematic empirical evidence about whether the purported conceptual inflation of ‘racist’ is in fact happening. That is what we do in this paper: we use an apparent time study, a technique drawn from sociolinguistics, to measure whether ‘racist’ is undergoing conceptual inflation. An apparent time study looks at the linguistic behavior of people of different ages as a way of measuring linguistic variation over time. We distinguish two processes that constitute conceptual inflation: extension expansion and intensity bleaching. Across three studies, we found some equivocal evidence that ‘racist’ has undergone extension expansion—its reference class may have slightly expanded—but no evidence that it has undergone intensity bleaching—its moral force remains the same. By comparing ‘racist’ with other terms that have plausibly undergone meaning change, like ‘queer’, we argue that claims about the conceptual inflation of ‘racist’, as they are standardly understood, are overblown. (shrink)

The purpose of this study is to propose new similarity measures namely rough variational coefficient similarity measure under the rough neutrosophic environment. The weighted rough variational coefficient similarity measure has been also defined. The weighted rough variational coefficient similarity measures between the rough ideal alternative and each alternative are xxxxx calculated to find the best alternative. The ranking order of all the alternatives can be determined by using the numerical values of similarity measures. (...) Finally, an illustrative example has been provided to show the effectiveness and validity of the proposed approach. Comparisons of decision results of existing rough similarity measures have been provided. (shrink)