Negative Rules Research Articles

Mining positive and negative rules via one-sided fuzzy three-way concept lattices

Mining association rules are a major area of research in data mining, as there are typically a large number of rules in each data set. And extracting necessary rules from each data set requires efficiency and careful consideration. In this paper, we propose a novel mining algorithm for efficiently acquiring positive and negative quantitative association rules (positive and negative QA-rules) in a fuzzy or multi-valued ordered context based on a three-way concept lattice (3CL). To this end, a new 3CL, called a one-sided fuzzy three-way concept lattice (OF3WC-lattice), is constructed based on a fuzzy formal context (FFcontext). The designed OF3WC-lattice describes the dependencies between objects and attributes, whether in the original FFcontext or its complement context. And the OF3WC-lattice is based on two kinds of two-way operators to make it consistent with practical linguistics. In addition, we propose an algorithm for constructing an OF3WC-lattice through analyzing relationships between the novel concept lattice and traditional one-sided fuzzy concept lattices. Experimental results demonstrate that our method effectively mines positive and negative QA-rules.

Negation recognition in clinical natural language processing using a combination of the NegEx algorithm and a convolutional neural network

BackgroundImportant clinical information of patients is present in unstructured free-text fields of Electronic Health Records (EHRs). While this information can be extracted using clinical Natural Language Processing (cNLP), the recognition of negation modifiers represents an important challenge. A wide range of cNLP applications have been developed to detect the negation of medical entities in clinical free-text, however, effective solutions for languages other than English are scarce. This study aimed at developing a solution for negation recognition in Spanish EHRs based on a combination of a customized rule-based NegEx layer and a convolutional neural network (CNN).MethodsBased on our previous experience in real world evidence (RWE) studies using information embedded in EHRs, negation recognition was simplified into a binary problem (‘affirmative’ vs. ‘non-affirmative’ class). For the NegEx layer, negation rules were obtained from a publicly available Spanish corpus and enriched with custom ones, whereby the CNN binary classifier was trained on EHRs annotated for clinical named entities (cNEs) and negation markers by medical doctors.ResultsThe proposed negation recognition pipeline obtained precision, recall, and F1-score of 0.93, 0.94, and 0.94 for the ‘affirmative’ class, and 0.86, 0.84, and 0.85 for the ‘non-affirmative’ class, respectively. To validate the generalization capabilities of our methodology, we applied the negation recognition pipeline on EHRs (6,710 cNEs) from a different data source distribution than the training corpus and obtained consistent performance metrics for the ‘affirmative’ and ‘non-affirmative’ class (0.95, 0.97, and 0.96; and 0.90, 0.83, and 0.86 for precision, recall, and F1-score, respectively). Lastly, we evaluated the pipeline against two publicly available Spanish negation corpora, the IULA and NUBes, obtaining state-of-the-art metrics (1.00, 0.99, and 0.99; and 1.00, 0.93, and 0.96 for precision, recall, and F1-score, respectively).ConclusionNegation recognition is a source of low precision in the retrieval of cNEs from EHRs’ free-text. Combining a customized rule-based NegEx layer with a CNN binary classifier outperformed many other current approaches. RWE studies highly benefit from the correct recognition of negation as it reduces false positive detections of cNE which otherwise would undoubtedly reduce the credibility of cNLP systems.

RoRED: Bootstrapping labeling rule discovery for robust relation extraction

Labeling rules can be leveraged to produce training data by matching the sentences in the corpus. However, the robustness of the relation extraction is reduced by noisy labels generated from incorrectly matched and missing sentences. To address this problem, we propose the bootstrapping labeling rule discovery method for robust relation extraction (RoRED). Specifically, we first define PN-rules to filter incorrectly matched sentences based on positive (P) and negative (N) rules. Second, we design a semantic-matching mechanism to match missing sentences based on semantic associations between rules, words, and sentences. Moreover, we present a co-training-based rule verification approach to refine the labels of matched sentences and improve the overall quality of bootstrapped rule discovery. Experiments on a real-world dataset indicate that RoRED achieves at least a 20% gain in F1 score compared to state-of-the-art methods.

Improving the fuzzy expert system for diagnosing depressive disorders

This paper presents an improving knowledge base and inference engine of a medical expert system for diagnosing depressive disorders. This medical expert system calls PORUL.DEP. PORUL.DEP’s knowledge base includes more than 850 positive rules. PORUL.DEP has been tested on more than 260 medical records of depressed patients. PORUL.DEP gives a correct diagnosis of more than 95% with light depressive disorder and without depressive disorder, but the remaining depressive disorders are not accurate. Average percent of more than 24 %. A new expert system, called STRESSDIAG, was developed on combining positive rules (for confirmation of conclusion) and negative rules (for exclusion of conclusion) for diagnosing depressive disorders. STRESSDIAG’s knowledge base consists of more than 850 positive rules of PORUL.DEP and more than 120 negative rules. Abelian group operation of Mycin is used to improve the inference engine based on fuzzy relations. STRESSDIAG gives the correct diagnosis of more than 76% with 4 depressive disorders types and without depressive disorders. Average percent of more than 82 %, up nearly 60% compared to PORUL.DEP.

On the Principles of Believe the Positive and Believe the Negative for Diagnosis Using Two Continuous Tests

Believe the Positive (BP) and Believe the Negative (BN) rules for combining two continuous diagnostic tests are compared with proce dures based on likelihood ratio and linear combination of the two tests. The sensitivity-specificity relationship for BP/BN is illustrated through a graph ical presentation of a ”ROC surface”, which leads to a natural approach of choosing between BP and BN. With a bivariate normal model, it is shown that the discriminating power of this approach is higher when the correla tion between the two tests has different signs for non-diseased and diseased population, given the location and variations of the two distributions are fixed. The idea is illustrated through an example.

The (Greatest) Fragment of Classical Logic that Respects the Variable-Sharing Principle (in the FMLA-FMLA Framework)

We examine the set of formula-to-formula valid inferences of Classical Logic, where the premise and the conclusion share at least a propositional variable in common. We review the fact, already proved in the literature, that such a system is identical to the first-degree entailment fragment of R. Epstein's Relatedness Logic, and that it is a non-transitive logic of the sort investigated by S. Frankowski and others. Furthermore, we provide a semantics and a calculus for this logic. The semantics is defined in terms of a \(p\)-matrix built on top of a 5-valued extension of the 3-element weak Kleene algebra, whereas the calculus is defined in terms of a Gentzen-style sequent system where the left and right negation rules are subject to linguistic constraints.

A Markov logic network method for reconstructing association rule-mining tasks in library book recommendation

PurposeThis study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation.Design/methodology/approachThe authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets.FindingsThe method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields.Research limitations/implicationsThe time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future.Originality/valueThe authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.

Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 6. The Еxternal Logic

Introduction. The article continues a series of publications on the linguistics of the relationship (hereafter R-linguistics) and is concerned with the semantic interpretation in terms of the linguistic model that is the initial stage to consider the logic of natural language (external logic).Methodology and sources. The results obtained in the previous parts of the series are used as research tools. In particular, the verbal categorization method is used to represent concepts and verbs. To develop the necessary mathematical representations in the field of logic and semantics of natural language, the previously formulated concept of the interpretation operator is used. The interpretation operator maps the sentences of the language into the model, taking into account the previously interpreted sentences.Results and discussion. The problems that arise during the operation of the natural language interpretation operator are analyzed using examples of text translation and utterance algebra. The source of problems is the dependence of the interpretation of sentences on the already accumulated results of interpretation. The features of the interpretation of negation and double negation in the language are analyzed. In particular, the negation of a sentence affects the interpretation of previous sentences, and double negation usually denotes a single negation with an indication of its scope. It is shown that even from the point of view of classical logic, linguistic negation is not unconditional, and the operation of concatenation is not commutative and associative. General rules of text interpretation in the form of step-by-step mapping of sentence elements into a linguistic model are formulated.Conlcusion. From the considered examples of the implementation of the interpretation operator, it follows that the negation of a sentence requires a change in the meaning of the operation of attributing sentences in the text. For this reason, the negative particle ”not” in the language is actually a label for changing the interpretation rule. The double negation rule in sentence logic does not hold, so sentences containing double negations are likely to contain information about the scope of the sentence negation in the text. Based on the analysis, the contours of the interpretation operator for the linguistic model are indicated.

Syntactic- and morphology-based text augmentation framework for Arabic sentiment analysis.

Arabic language is a challenging language for automatic processing. This is due to several intrinsic reasons such as Arabic multi-dialects, ambiguous syntax, syntactical flexibility and diacritics. Machine learning and deep learning frameworks require big datasets for training to ensure accurate predictions. This leads to another challenge faced by researches using Arabic text; as Arabic textual datasets of high quality are still scarce. In this paper, an intelligent framework for expanding or augmenting Arabic sentences is presented. The sentences were initially labelled by human annotators for sentiment analysis. The novel approach presented in this work relies on the rich morphology of Arabic, synonymy lists, syntactical or grammatical rules, and negation rules to generate new sentences from the seed sentences with their proper labels. Most augmentation techniques target image or video data. This study is the first work to target text augmentation for Arabic language. Using this framework, we were able to increase the size of the initial seed datasets by 10 folds. Experiments that assess the impact of this augmentation on sentiment analysis showed a 42% average increase in accuracy, due to the reliability and the high quality of the rules used to build this framework.

Mis-categorized entities detection

Entity categorization, the process of categorizing entities into groups, is an important problem with many applications. However, in practice, many entities are mis-categorized, such as Google Scholar and Amazon products. In this paper, we study the problem of discovering mis-categorized entities from a given group of categorized entities. This problem is inherently hard: All entities within the same group have been “well” categorized by the state-of-the-art solutions. Apparently, it is nontrivial to differentiate them. We propose a novel rule-based framework to solve this problem. It first uses positive rules to compute disjoint partitions of entities, where the partition with the largest size is taken as the correctly categorized partition, namely the pivot partition. It then uses negative rules to identify mis-categorized entities in other partitions that are dissimilar to the entities in the pivot partition. We describe optimizations on applying these rules and discuss how to generate positive/negative rules. In addition, we propose novel strategies to resolve inconsistent rules. Extensive experimental results on real-world datasets show the effectiveness of our solution.

A Simple Logical Matrix and Sequent Calculus for Parry’s Logic of Analytic Implication

We provide a logical matrix semantics and a Gentzen-style sequent calculus for the first-degree entailments valid in W. T. Parry’s logic of Analytic Implication. We achieve the former by introducing a logical matrix closely related to that inducing paracomplete weak Kleene logic, and the latter by presenting a calculus where the initial sequents and the left and right rules for negation are subject to linguistic constraints.

Sí hay negación lógica

En este artículo discutimos la tesis de Jc Beall según la cual no hay negación lógica. Evaluamos la solidez del argumento con el que defiende su tesis y presentamos dos razones para rechazar una de sus premisas: que la negación tiene que ser excluyente o exhaustiva. La primera razón involucra una presentación alternativa de las reglas de la negación en sistemas de secuentes diferentes al que Beall presupone. La segunda razón establece que la negación no tiene que ser excluyente o exhaustiva.

Negation detection for sentiment analysis: A case study in Spanish

AbstractAccurate negation identification is one of the most important tasks in the context of sentiment analysis. In order to correctly interpret the sentiment value of a particular expression, we need to identify whether it is in the scope of negation. While much of the work on negation detection has focused on English, we have seen recent developments that provide accurate identification of negation in other languages. In this paper, we provide an overview of negation detection systems and describe an implementation of a Spanish system for negation cue detection and scope identification. We apply this system to the sentiment analysis task, confirming also for Spanish that improvements can be gained from accurate negation detection. The paper contributes an implementation of negation detection for sentiment analysis in Spanish and a detailed error analysis. This is the first work in Spanish in which a machine learning negation processing system is applied to the sentiment analysis task. Existing methods have used negation rules that have not been assessed, perhaps because the first Spanish corpus annotated with negation for sentiment analysis has only recently become available.

Bimalleolar Fracture In Athlete With Negative Ottawa Ankle Rules

Bimalleolar Fracture In Athlete With Negative Ottawa Ankle Rules

Compassion fatigue, emotional labor, and emotional display among hospice nurses

The purpose of this study was to investigate relationships between compassion fatigue, emotional labor, and emotional display among hospice nurses (N = 90; 94.4% women). The hospice nurses indicated the emotional labor they utilize is primarily suppressing both positive and negative affect. Compassion fatigue was positively associated with expressing negative emotions, faking negative emotions, negative display rule perceptions, and surface acting, and negatively associated with suppressing negative emotions. Interventions and policies that foster greater authenticity and support emotional regulation may help reduce compassion fatigue among hospice nurses.

Back Pain‐Inducing Test, a Novel and Sensitive Screening Test for Painful Osteoporotic Vertebral Fractures: A Prospective Clinical Study

To detect painful vertebral fractures (VFs) in back pain populations at risk of osteoporosis, we designed a physical examination test (the Back Pain-Inducing Test [BPIT]) that included three movements: lying supine, rolling over, and sitting up. If back pain is induced during any of these movements, the result is defined as positive, thereby establishing a presumptive diagnosis of painful VFs. Pain severity is quantified using a self-reported numerical rating scale (NRS). The presence or absence of painful VFs is verified by whole-spine magnetic resonance imaging (MRI), the gold standard for final diagnosis. According to the standards for reporting diagnostic accuracy, a real-world, prospective, and observational study was performed on 510 back pain patients (enrolled from a single institute) at risk of osteoporosis. The sensitivity, specificity, and accuracy of the BPIT for identifying painful VFs were 99.1% (95% CI, 97.5% to 99.8%), 67.9% (95% CI, 60.4% to 74.5%), and 89.0%, respectively. The positive and negative predictive values were 86.6% (95% CI, 82.9% to 89.6%) and 97.4% (95% CI, 92.6% to 99.3%), respectively. Cutoff NRS scores for lying supine, rolling over, and sitting up were 3, 0, and 2, respectively. The corresponding area under the receiver operating characteristic curves (AUROCs) of each movement was 0.898 (95% CI, 0.868 to 0.922), 0.884 (95% CI, 0.854 to 0.911), and 0.910 (95% CI, 0.882 to 0.933), respectively. Although the high prevalence of VFs in the enrolled cohort partially limits the external validity of the predictive value in the general population, we conclude that the BPIT is potentially effective for detecting painful VFs in back pain populations at risk of osteoporosis. This test may be used as a stratification tool in decision-making on subsequent imaging procedures: a negative BPIT rules out painful VFs and indicates that an MRI should be spared, whereas a positive BPIT means that an MRI is necessary and is likely to identify painful VFs. © 2019 American Society for Bone and Mineral Research.

Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining.

Diagnostic tests are widely used in emergency departments to make detailed investigations on diagnosis and treat patients correctly. However, since these tests are expensive and time-consuming, ordering correct tests for patients is crucial for efficient use of hospital resources. Thus, understanding the relation between diagnosis and diagnostic test requirement becomes an important issue in emergency departments. Association rule mining was used to extract hidden patterns and relation between diagnosis and diagnostic test requirement in real-life medical data received from an emergency department. Apriori was used as an association rule mining algorithm. Diagnosis was grouped into 21 categories based on International Classification of Disease, and laboratory tests were grouped into four main categories (hemogram, biochemistry, cardiac enzyme, urine and human excrement related). Both positive and negative rules were discovered. Since the nature of the data had the dominance of negative values, higher number of negative rules with higher confidences were discovered compared to positive ones. The extracted rules were validated by emergency department experts and practitioners. It was concluded that understanding the association between patient's diagnosis and diagnostic test requirement can improve decision-making and efficient use of resources in emergency departments. Association rules can also be used for supporting physicians to treat patients.

The W.A.I.O.T. Definition of High-Grade and Low-Grade Peri-Prosthetic Joint Infection.

The definition of peri-prosthetic joint infection (PJI) has a strong impact on the diagnostic pathway and on treatment decisions. In the last decade, at least five different definitions of peri-prosthetic joint infection (PJI) have been proposed, each one with intrinsic limitations. In order to move a step forward, the World Association against Infection in Orthopedics and Trauma (W.A.I.O.T.) has studied a possible alternative solution, based on three parameters: 1. the relative ability of each diagnostic test or procedure to Rule OUT and/or to Rule IN a PJI; 2. the clinical presentation; 3. the distinction between pre/intra-operative findings and post-operative confirmation. According to the WAIOT definition, any positive Rule IN test (a test with a specificity > 90%) scores +1, while a negative Rule OUT test (a test with a sensitivity > 90%) scores −1. When a minimum of two Rule IN and two Rule OUT tests are performed in a given patient, the balance between positive and negative tests, interpreted in the light of the clinical presentation and of the post-operative findings, allows to identify five different conditions: High-Grade PJI (score ≥ 1), Low-Grade PJI (≥0), Biofilm-related implant malfunction, Contamination and No infection (all scoring < 0). The proposed definition leaves the physician free to choose among different tests with similar sensitivity or specificity, on the basis of medical, logistical and economic considerations, while novel tests or diagnostic procedures can be implemented in the definition at any time, provided that they meet the required sensitivity and/or specificity thresholds. Key procedures to confirm or to exclude the diagnosis of PJI remain post-operative histological and microbiological analysis; in this regard, given the biofilm-related nature of PJI, microbiological investigations should be conducted with proper sampling, closed transport systems, antibiofilm processing of tissue samples and explanted biomaterials, and prolonged cultures. The proposed WAIOT definition is the result of an international, multidisciplinary effort. Next step will be a large scale, multicenter clinical validation trial.

Deducing certain fixes to graphs

This paper proposes to deduce certain fixes to graphs G based on data quality rules Σ and ground truth Γ ( i.e., validated attribute values and entity matches). We fix errors detected by Σ in G such that the fixes are assured correct as long as Σand Γ are correct. We deduce certain fixes in two paradigms. (a) We interact with users and "incrementally" fix errors online. Whenever users pick a small set V 0 of nodes in G , we fix all errors pertaining to V 0 and accumulate ground truth in the process. (b) Based on accumulated Γ, we repair the entire graph G offline; while this may not correct all errors in G , all fixes are guaranteed certain. We develop techniques for deducing certain fixes. (1) We define data quality rules to support conditional functional dependencies, recursively defined keys and negative rules on graphs, such that we can deduce fixes by combining data repairing and object identification. (2) We show that deducing certain fixes is Church-Rosser, i.e., the deduction converges at the same fixes regardless of the order of rules applied. (3) We establish the complexity of three fundamental problems associated with certain fixes. (4) We provide (parallel) algorithms for deducing certain fixes online and offline, and guarantee to reduce running time when given more processors. Using real-life and synthetic data, we experimentally verify the effectiveness and scalability of our methods.

Reasoning Between the Lines: a Logic of Relational Propositions

This paper describes how Rhetorical Structure Theory (RST) and relational propositions can be used to define a method for rendering and analyzing texts as expressions in propositional logic. Relational propositions, the implicit assertions that correspond to RST relations, are defined using standard logical operators and rules of inference. The resulting logical forms are used to construct logical expressions that map to RST tree structures. The resulting expressions show that inference is pervasive within coherent texts. To support reasoning over these expressions, a set of rules for negation is defined. The logical forms and their negation rules can be used to examine the flow of reasoning and the effects of incoherence. Because there is a correspondence between logical coherence and the functional relationships of RST, an RST analysis that cannot pass the test of logic is indicative either of a problematic analysis or of an incoherent text. The result is a method for analyzing the logic implicit within discursive reasoning.