Semantic Role Labeling of Chinese Nominal Predicates with Dependency-Driven Constituent Parse Tree Structure

This paper explores a tree kernel based method for semantic role labeling （SRL） of Chinese nominal predicates via a convolution tree kernel. In particular, a new parse tree representation structure, called dependency-driven constituent parse tree （D-CPT）, is proposed to combine the advantages of both constituent and dependence parse trees. This is achieved by directly representing various kinds of dependency relations in a CPT-style structure, which employs dependency relation types instead of phrase labels in CPT （Constituent Parse Tree）. In this way, D-CPT not only keeps the dependency relationship information in the dependency parse tree （DPT） structure but also retains the basic hierarchical structure of CPT style. Moreover, several schemes are designed to extract various kinds of necessary information, such as the shortest path between the nominal predicate and the argument candidate, the support verb of the nominal predicate and the head argument modified by the argument candidate, from D-CPT. This largely reduces the noisy information inherent in D-CPT. Finally, a convolution tree kernel is employed to compute the similarity between two parse trees. Besides, we also implement a feature-based method based on D-CPT. Evaluation on Chinese NomBank corpus shows that our tree kernel based method on D-CPT performs significantly better than other tree kernel-based ones and achieves comparable performance with the state-of-the-art feature-based ones. This indicates the effectiveness of the novel D-CPT structure in representing various kinds of dependency relations in a CPT-style structure and our tree kernel based method in exploring the novel D-CPT structure. This also illustrates that the kernel-based methods are competitive and they are complementary with the feature- based methods on SRL.

Sense and Reference of Predicates： Comments on Frege＇s Theory of Sense-Reference

Frege argued that a predicate was a functional expression and the reference of it a concept, which as a predicative function had one or more empty places and was thus incomplete. Frege＇s view gives rise to what has been known as the paradox of the concept ＂horse.＂ In order to resolve this paradox, I argue for an opposite view which retains the point that a predicate is a function, i.e. that a predicative function is complete in a sense. Specifically speaking, a predicate performing the function of a predicate has at least one empty place and has no reference, while a predicate performing the function of a subject does not have any empty place but does have a reference. Frege not only regarded a concept with one or more empty places as the reference of a predicate but also took a set of objects without any empty place to be the extension of a concept with one or more empty places. Thus, it presents a complex relationship between the reference of a predicate and its corresponding extension, leading to disharmony in his theory. I argue that this is because there is a major defect in Frege＇s theory of meaning, namely the neglect of common names. What he called extensions of concepts are actually extensions of common names, and the references of predicates and the extensions of common names have a substantial difference despite being closely related.

A Study of Nominal Predicate Sentences Under the Framework of Cognitive Grammar

Cognitive grammar,as a linguistic theory that attaches importance to the relationship between language and thinking,provides us with a more comprehensive way to understand the structure,semantics and cognitive processing of noun predicate sentences.Therefore,under the framework of cognitive grammar,this paper tries to analyze the semantic connection and cognitive process in noun predicate sentences from the semantic perspective and the method of example theory,and discusses the motivation of the formation of this construction,so as to provide references for in-depth analysis of the cognitive laws behind noun predicate sentences.

DEBRA: On the Unsupervised Learning of Concept Hierarchies from (Literary) Text

With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between Computer Science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.

Prediction of compressive strength of cement mortars with fly ash and activated coal gangue

The pozzolanic activity of coal gangue, which is calcining at 500 to 1 000 ℃, differs distinctly. The simplex-centroid design with upper and lower bounds of component proportion is adopted to study the compressive strength of mortars made with ternary blends of cement, activated coal gangue and fly ash. Based on the results of a minimum of seven design points, three special cubic polynomial models are used to establish the strength predicating equations at different ages for mortars. Five experimental checkpoints were also designed to verify the precision of the equations. The most frequent errors of the predicted values are within 3%. A simple and practical way is provided for determining the optimal proportion of two admixtures when they are used in concrete.

A Syntactic Contrast of English and Chinese——Subject-Prominent vs.Topic-Prominent

English and Chinese belong to different language families,employing two distinct syntactic systems.English is subject-prominent,following the pattern of subject first,then predicate;while Chinese is topic-prominent,showing much flexibility in word arrangement as well as the necessity of subject and predicate.

Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of manufacturing.These technologies enable collecting,storing,and retrieving essential information from the manufacturing stage.Data collected at sites are shared with others where execution automatedly occurs.The obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing process.However,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major concern.The current research validates the information optimally to offer a minimum set of activities to complete the disassembly process.An optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in manufacturing.However,finding an optimal DSP is complex because of its combinatorial nature.The genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP problem.The converging nature at local optima is a limitation in the traditional GA.This study improvised the GA workability by integrating with the proposed priori crossover operator.An optimality function is defined to reduce disassembly effort by considering directional changes as parameters.The enhanced GA method is tested on a real-time product to evaluate the performance.The obtained results reveal that diversity control depends on the operators employed in the disassembly attributes.The proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the product.The effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.

英藏“谓语”比较与英文教学中Predicate一词藏译略考

我区实施"藏汉英三语"教育可谓喜忧参半,拉萨等城市虽取得显著成效,而偏远农牧区的藏族学生却因作为英文课堂媒介语的汉语基础薄弱,英文学习效果欠佳。因此,英藏双语的直接对比研究的缺乏,是导致农牧区英文教育处境艰难的重要原因。该文以英文教学为主体,旨在通过对英藏基础句法中"Predicate"一词的直接对比和语法术语的藏译探讨,望能解决农牧区藏族学生英文教学中的理解困扰。

Integrated Expert Analytical System for Assessment of Oil-and-Gas Saturation of Strata

The present research deals with the problem of development of an integrated expert-analytical system for optimum selection of calculated oil-field-geophysical parameters of oil and gas deposits with the purpose of increasing the accuracy of assessment of the reserves of oil and gas deposits. The purpose of the system is to make current adequate decisions on determining of oil-and-gas saturation of strata and future identification of the most significant methods for that, with these methods forming the foundation of knowledge bases for oil-and-gas deposits of the Apsheron peninsula of Azerbaijan. The system architecture allows for expanding the system with its subsequent transformation into a cluster of expert-analytical systems. A logical model of the proposed system is presented. The paper contains a detailed description of the mechanism of operation of the system as a whole and of its individual blocks. Mathematical and formal-logical bases of the intelligent system are explained. The system is equipped with a tool for dynamic statistical analysis of decisions made by it, with representation of the results in real-time mode. The results of the system testing on specific oil-and-gas deposit of the Apsheron peninsula of Azerbaijan in 2013 are given.

Optimal Approximation Algorithms for Reoptimization of Constraint Satisfaction Problems

The purpose of reoptimization using approximation methods—application of knowledge about the solution of the initial instance I, provided to achieve a better quality of approximation (approximation ratio) of an algorithm for determining optimal or close to it solutions of some “minor” changes of instance I. To solve the problem Ins-Max-EkCSP-P (reoptimization of Max-EkCSP-P with the addition of one constraint) with approximation resistant predicate P exists a polynomial threshold (optimal) -approximation algorithm, where the threshold “random” approximation ratio of P). When the unique games conjecture (UGC) is hold there exists a polynomial threshold (optimal) -approximation algorithm (where and the integrality gap of semidefinite relaxation of Max-EkCSP-P problem Z) to solve the problem Ins-Max-EkCSP-P.

On the Usage of the Subjunctive Mood in English

The subjunctive mood is one of the most difficult English grammar items for most English learners. In this thesis,the author discusses the usage of the subjunctive mood in English,especially its usage in English subordinate clauses. At the same time,the author provides a lot of examples. In the author's opinion,as long as the English learners find out the rules of it and practice it more,they will definitely grasp the subjunctive mood and use it correctly and skillfully.

Modeling Trusted Computing

In this paper, a formal approach based on predicate logic is proposed for representing and reasoning of trusted computing models. Predicates are defined to represent the characteristics of the objects and the relationship among these objects in a trusted system according to trusted computing specifications. Inference rules of trusted relation are given too. With the semantics proposed, some trusted computing models are formalized and verified, which shows that Predicate calculus logic provides a general and effective method for modeling and reasoning trusted computing systems.

Theory of Approximate Reasoning in Two-Valued Predicate Logic Based on the Quasi-truth Degrees

Based on the theory of the quasi-truth degrees in two-valued predicate logic, some researches on approximate reasoning are studied in this paper. The relation of the pseudo-metric between first-order formulae and the quasi-truth degrees of first-order formulae is discussed, and it is proved that there is no isolated point in the logic metric space (F, ρ ). Thus the pseudo-metric between first-order formulae is well defined to develop the study about approximate reasoning in the logic metric space (F, ρ ). Then, three different types of approximate reasoning patterns are proposed, and their equivalence under some condition is proved. This work aims at filling in the blanks of approximate reasoning in quantitative predicate logic.

Predication of Fhhh potential in PTA wastewater treatment

Ebis is the intelligent environmental biotechnological informatics software developed for judging the effectiveness of the microorganism strain in the industrial wastewater treatment system(IWTS) at the optimal status. The parameter, as the objective function for the judgment, is the minimum reactor volume( V _ min ) calculated by Ebis for microorganism required in wastewater treatment. The rationality and the universality of Ebis were demonstrated in the domestic sewage treatment system(DSTS) with the data published in USA and China at first,then Fhhh strain's potential for treating the purified terephthalic acid(PTA) was proved. It suggests that Ebis would be useful and universal for predicating the technique effectiveness in both DSTS and IWTS.

Viscosity profile prediction of a heavy crude oil during lifting in two deep artesian wells

It has been known that the productivity of artesian wells is strongly dependent on the rheological properties of crude oils. This work targets two deep artesian wells(>5000 m) that are producing heavy crude oil. The impacts of well conditions including temperature, pressure and shear rate, on the crude oil rheology were comprehensively investigated and correlated using several empirical rheological models. The experimental data indicate that this heavy oil is very sensitive to temperature as result of microstructure change caused by hydrogen bonding. The rheological behavior of the heavy oil is also significantly impacted by the imposed pressure, i.e., the viscosity flow activation energy(Eμ) gently increases with the increasing pressure. The viscosity–shear rate data are well fitted to the power law model at low temperature. However, due to the transition of fluid feature at high temperature(Newtonian fluid), the measured viscosity was found to slightly deviate from the fitting data. Combining the evaluated correlations, the viscosity profile of the heavy crude oil in these two deep artesian wells as a function of well depth was predicted using the oilfield producing data.

Predication of plasma concentration of remifentanil based on Elman neural network

Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacokinetics parameters,nonlinear mixed effects model(NONMEM),has the abuses of tedious work and plenty of man-made jamming factors.The Elman feedback neural network was built.The relationships between the patients’plasma concentration of remifentanil and time,patient’age,gender,lean body mass,height,body surface area,sampling time,total dose,and injection rate through network training were obtained to predict the plasma concentration of remifentanil,and after that,it was compared with the results of NONMEM algorithm.In conclusion,the average error of Elman network is 6.34%,while that of NONMEM is 18.99%.The absolute average error of Elman network is 27.07%,while that of NONMEM is 38.09%.The experimental results indicate that Elman neural network could predict the plasma concentration of remifentanil rapidly and stably,with high accuracy and low error.For the characteristics of simple principle and fast computing speed,this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetic and pharmacodynamics.

Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery

In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic.

A Contrastive Analysis of Subject and Predicate in English and Chinese

Respectively belonging to different language families,English and Chinese naturally have many differences in morphology and syntax.This paper intends to give a contrastive analysis of subject and predicate in English and Chinese,focusing on differences of subject and predicate usages in the two languages,and the problems they cause for Chinese students in English learning and translation.

Location Prediction of Blind Ore bodies in Shuijingtun Gold Mine, Zhangjiakou, China

By analyzing the metallogenic conditions and prospecting marks of F 8 fault belt in Shiujingtun Gold Mine, the geochemical samples were collected along F 8 fault belt and prospecting profile normal to the F 8 fault belt. Gold and its indicator elements were tested with X ray fluorescence spectrometry and the content distribution diagram of Au, Ag, Hg and As along the F 8 fault belt was performed. The geochemical primary halo model and the Grey system model of F 8 fault belt are established. With these element distribution features and models, the blind ore bodies in the F 8 fault belt were predicted. Engineering prospect shows that the industrial orebodies have been discovered and the prediction results are dependable.

Predication of Atmospheric VLE for the Ternary System Dimethyl Carbonate-Methanol-Furfural by Using the Wilson Equation

The vapor-liquid equilibrium of Dimethyl Carbonate-Methanol-Furfural under atmospheric pressure from DMC-CH 3OH,DMCC 5H 4O 2,CH 3OH-C 5H 4O 2 binary systematic VLE data is calculated,by using C ++(VC6.0) programming language and Wilson equation.It provided important VLE data to set up mathematic models of extraction-rectifying separation of DMC and methanol by using furfural as extraction reagent.So the results can be used for chemical engineering calculation.