Dependency Analysis

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Linear Time Dependency Analysis for Japanese

Linear Time Dependency Analysis for Japanese

The main purpose of this study is to propose an efficient algorithm to analyze dependency structures of head final languages such as Japanese and to prove its efficiency both theoretically and empiri- cally. In this paper, we present a novel efficient al- gorithm for Japanese dependency analysis. The al- gorithm allows us to analyze dependency structures of a sentence in linear-time while keeping a state- of-the-art accuracy. We show a formal description of the algorithm and discuss it theoretically with respect to time complexity. In addition to this, we evaluate its efficiency and performance empir- ically against the Kyoto University Corpus (Kuro- hashi and Nagao, 1998), which is a parsed corpus of news paper articles in Japanese.

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Goal-oriented Dependency Analysis for Service Identification

Goal-oriented Dependency Analysis for Service Identification

From the definition of a branch, this equation contains information about the requirements parent and child goals. A branch always points from the leaves to the root. There are two kinds of branches: “AND” branches and “OR” branches. Both branches signify the logical relation between sub-goals and goals. The logical relationship between goals helps iden- tify dependencies between sub goals. The rules are defined in the dependency analysis section. The satisfaction coefficient of a dependency branch is given by the dependency relation between the goal and its sub-goal. To obtain the satisfaction level of a goal, we work from the bottom of the dependency tree. The satisfaction level is classified as “sat- isfied,” “weak,” or “unsatisfied.” A requirement with all of its scenarios satisfied by the business process will have the state, “satisfied.” If only some scenarios are satisfied, the relation is “weak.” Otherwise, the requirement is “unsatisfied.” This satisfaction relation occurs between the parent goal and a sub-goal, and it can be translated as another form of dependency for the destination goal.

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Japanese Dependency Analysis using Cascaded Chunking

Japanese Dependency Analysis using Cascaded Chunking

Most of the previous statistical approaches for Japanese dependency analysis (Fujio and Mat- sumoto, 1998; Haruno et al., 1999; Uchimoto et al., 1999; Kanayama et al., 2000; Uchimoto et al., 2000; Kudo and Matsumoto, 2000) are based on a probabilistic model consisting of the following two steps. First, they estimate modification prob- abilities, in other words, how probable one segment tends to modify another. Second the optimal combi- nation of dependencies is searched from the all can- didates dependencies. Such a probabilistic model is not always efficient since it needs to calculate the probabilities for all possible dependencies and cre- ates n˙(n −1)/2 (where n is the number of segments in a sentence) training examples per sentence. In addition, the probabilistic model assumes that each pairs of dependency structure is independent.

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Multi Model and Crosslingual Dependency Analysis

Multi Model and Crosslingual Dependency Analysis

As expected, Upper Sorbian and Norther Sami give quite acceptable results using models trained on Czech and Finnish respectively. Due to the fact that the provided treebanks for Kazakh and Uyghur are both very small we tried to apply the same approach of using the training corpus of a ty- pologically close language (here Turkish). How- ever, the results were disappointing. Thus, we continue to use the models trained on very small corpora for these two languages in the shared task. Possibly the fact that the raw text corpus used to calculate word embeddings for Kazakh and Uyghur are much bigger than those of the sur- prise languages allowed to produce usable word embeddings. If so, this would mean that word em- beddings play a very prominent role in data driven dependency parsing.

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Integration of Dependency Analysis with Semantic Analysis Referring to the Context

Integration of Dependency Analysis with Semantic Analysis Referring to the Context

Japanese has the property of free word order and it raises the number of possible candidates in parsing a given sentence. If parsing and semantic analysis are performed independently, it is difficult to efficiently reduce the number of the candidates using the semantic/contextual information. If semantic analysis applies to every candidate, the calculation cost may be too high. To reduce the number of candidates using the contextual information, the system process an input word by word and, in each cycle of parsing, it gives a score to each candidate based on grammatical rules and the conformity to the context. In each cycle of parsing, the system generates possible semantic representations by referring to the context. Each representation receives a score and low- scored candidates are filtered out. To use contextual information in the process, the contextual information has to be stored in such a way that it can be formally compared to the semantic content of an input. For example, the system should realize whether an input contains something that has already been denoted in the context. If it does, the system should realize which part in the input has appeared in the context and how that part has been mentioned.

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A. Algorithms using Dependency Analysis

A. Algorithms using Dependency Analysis

Many algorithms have been developed which induce the structure of Bayesian Networks (BNs). In general, learning the structure from a dataset is regarded as a NP-hard problem [1]. Reference [2] shows through complexity analysis the extent of difficulty with the task. The underlying challenge in deriving an efficient network relates to the large cardinality of the search space. Some algorithms attempt to reduce cardinality by assuming knowledge about the ordering of nodes in a network [3, 4]. However, in a domain where such expertise is unavailable, or the number of domain variables is large, defining the ordering may not be possible.

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A Scenario-Driven Approach to Trace Dependency Analysis

A Scenario-Driven Approach to Trace Dependency Analysis

The behavior of a software system can be observed using kinds of test scenarios that are typically defined during its development (e.g., acceptance test scenarios and module test cases). By executing those scenarios in the running system, the internal activities of that system can be observed and recorded. This leads to observable traces (type a) that link scenarios to implementation classes, methods, and lines of code (called the footprint). Our trace analysis approach relies on monitoring tools used for spying into software systems during their execution or simulation. Those tools are readily available. For instance, we used a commercial tool from Rational Software called Rational PureCoverage 1 in order to monitor the running Inter-Library Loan (ILL) system. The tool monitored the footprint in terms of which lines of code were executed, how many times each line was executed, and when in the execution the line was covered. Testing a system or some of its components and observing the footprints is a straightforward activity. Table 4 shows the summary of observing the footprints of the 10 test scenarios from Table 1 using the Rational PureCoverage tool. The numbers in Table 4 indicate how many methods of each implementation class were used. For instance, scenario “A” used 10 methods of the class CAboutDlg and three methods of the class CMainWin. To reduce the complexity of this example, Table 4 does not display the actual methods (the information in the footprint graph shown later would be too crowded). Nevertheless, by only using classes as the finest granularity, the generated traces will still be useful. The approach remains the same if methods or lines of code are used.

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Catena Operations for Unified Dependency Analysis

Catena Operations for Unified Dependency Analysis

Catenae were introduced initially to handle lin- guistic expressions with non-constituent structure and idiosyncratic semantics. It was shown in a number of publications that this unit is appropri- ate for both - the analysis of syntactic (for exam- ple, ellipsis, idioms) and morphological phenom- ena (for example, compounds). One of the impor- tant questions in NLP is how to establish a connec- tion between the lexicon and the text dimension in an operable way. At the moment most investiga- tions focus on the representation and analysis of the text dimension.

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Empty Categories in Hindi Dependency Treebank: Analysis and Recovery

Empty Categories in Hindi Dependency Treebank: Analysis and Recovery

Empty categories play a crucial role in the annota- tion framework of the Hindi dependency treebank 1 (Begum et al., 2008; Bharati et al., 2009b). They are inserted in a sentence in case the dependency analysis does not lead to a fully connected tree. In the Hindi treebank, an empty category (denoted by a NULL node) always has at least one child. These elements have essentially the same properties (e.g. case-marking, agreement, etc.) as an overtly real- ized element and they provide valuable information (such as predicate-argument structure, etc.). A dif- ferent kind of motivation for postulating empty cate- gories comes from the demands of natural lan- guage processing, in particular parsing. There are several types of empty categories in the Hindi dependency

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Dependency-based Analysis for Tagalog Sentences

Dependency-based Analysis for Tagalog Sentences

Dependency analysis creates a link from a word to its dependents. When two words are con- nected by a dependency relation, one takes the role of the head and the other is the dependent (Covington, 2001). The straightforwardness of dependency analysis has been used in other NLP tasks such as word alignment (Ma et al., 2008) and semantic role labeling (Hacioglu, 2004). De- pendency parsers developed were either graph-based (McDonald and Pereira, 2006) or transition- based parsers (Nivre and Scholz, 2004; Yamada and Matsumoto, 2003). Syntactic analysis for different languages also use dependency parsers such as in Japanese (Kudo and Matsumoto, 2001; Iwatate et al., 2008), English (Nivre and Scholz, 2004), Chinese (Chen et al., 2009; Yu et al., 2008) to mention some.

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Dependency and Constituency in Translation Shift Analysis

Dependency and Constituency in Translation Shift Analysis

Our hypothesis is based on the fact that certain equivalence relations, despite divergences in trans- lations, can be detected using dependency trees. This hypothesis is supported in literature by some previous work on the alignment of deep syntactic structures. For example Ding et al. (2003) devel- oped an algorithm that uses parallel dependency structures to iteratively add constraints to possi- ble alignments; an extension of such work is that of Ding and Palmer (2004), who used a statisti- cal approach to learn dependency structure map- pings from parallel corpora, assuming at first a free word mapping, then gradually adding constraints to word level alignments by breaking down the parallel dependency structures into smaller pieces. Mareˇcek et al. (2008) proposed an alignment sys- tem of the tectogrammatical layer of texts from the Prague Czech-English Dependency Treebank 1

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<p>Inhibition of ISG15 Enhances the Anti-Cancer Effect of Trametinib in Colon Cancer Cells</p>

<p>Inhibition of ISG15 Enhances the Anti-Cancer Effect of Trametinib in Colon Cancer Cells</p>

Human colon cancer cell lines were obtained from the State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, including RKO, HCT116 and SW480. And all the cells were tested and authenticated by an AmpFlSTR Identi fi ler PCR assays in the year of 2019 in TSINGKE Biological Technology Company. The results showed that RKO and SW480 were 100% exact matched, and HCT116 was 98% matched. All the cell lines were con fi rmed to be mycoplasma negative via a PCR method and directly thawed from the liquid nitrogen jar. Cells were grown in Dulbecco ’ s Modi fi ed Eagle Medium supplemented with 10% fetal bovine serum (Gibco, USA), 100mU/mL penicillin, and 100 µg/mL streptomycin in a 5% CO2 atmosphere at 37°C. Cells (1x10 5 cells/well) were seeded into six-well plates and transfected with the indicated constructs using Lipofectamine 2000 (Invitrogen/Life Sciences) according to the manufacturer ’ s instructions. After 72 h, the transfected cells were harvested for further analysis. The sequence of the siRNA sense strand was as follows: si-ISG15#1: 5- ’ TC CTGGTGAGGAATAACAA-3 ʹ , si-ISG15#2: 5- ’ CCAUGU CGGUGUCAGAGCUTT-3 ʹ .

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Analysis of a Proposed Optical Burst Switching Core Node with Wavelength Converters and Deflection Routing

Analysis of a Proposed Optical Burst Switching Core Node with Wavelength Converters and Deflection Routing

The main commonly problem that arises in Optical Burst Switching (OBS) networks is a burst contention. Wavelength conversion and deflection routing are the most important switch fabric strategies to resolve this contention. In this paper, we study a mathematical model for a new proposal optical burst switching core node architecture. A performance measurement has been investigated by analytic the burst loss probability using steady-state occupancy probabilities and Poisson traffic model arrivals. Performance analysis results are presented at different values of the mean burst arrival rates with a core node design parameters such as wavelength conversion capability and deflection routing.

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Predicate Catenae: A Dependency Grammar Analysis of It Clefts

Predicate Catenae: A Dependency Grammar Analysis of It Clefts

embedded clause. It can do this because the matrix predicate reaches into the embedded clause in a manner that renders Susan and herself co-arguments, whereby Susan, as a subject, is ranked higher than herself, an object. Two key aspects of this analysis are worth restating: first, the copula is a function word and so the matrix predicate necessarily reaches below it to include (part of) a post-dependent, just as in examples (29- 32) above; and second, the words constituting the matrix predicate form a catena despite the fact they are discontinuous in the linear dimension and hence do not form a string.

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The Implementation of a Communication Deadlock Analysis Based on the Theory of Deadlock Analysis for the Actor Model

The Implementation of a Communication Deadlock Analysis Based on the Theory of Deadlock Analysis for the Actor Model

DOI: 10.4236/jsea.2019.1210024 394 Journal of Software Engineering and Applications trol-and-Communication Flow analysis to develop an algorithm that can detect communication deadlock in codes written in Ada, which is also a concurrency language. Their research, however, focuses on an algorithm that is capable of doing automated and efficient re-analysis after changes in the Ada code. J-L. Colaço et al. [3] present an analysis based on the type inference model for a pri- mitive actor calculus to handle the situation of “orphan messages”, the messages received by actors while never being processed. This analysis is important in de- tecting communication deadlock, since orphan message is one of the causes of communication deadlock. Maria Christakis and Konstantinos Sagonas [4] pro- pose a static analysis that could detect both Communication deadlock and Beha- vioral deadlock in the Erlang language. The analysis detects deadlocks by gene- rating a control flow graph and finds whether the graph is a closed figure or not. If the graph is not closed, the whole program is considered to have a deadlock. This approach is by far one of the best approaches for detecting deadlocks, and it has demonstrated its feasibility by finding communication deadlocks in some open source libraries.

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Discovering Valuable Relationships in RDBMS Using Complements of Maximal Set

Discovering Valuable Relationships in RDBMS Using Complements of Maximal Set

The process of data mining mainly includes association rules, classification and prediction, and clustering. Data dependencies play very important roles in database design, data quality management, and knowledge representation. Functional dependency is a kind of data dependencies. Now-a-days there is a fast growing amount of data that are collected and stored in large databases. As a result, the databases may contain redundant or inconsistent data. Dependencies are very important in the case of database design, data quality management and knowledge representation. Dependencies in the case of database design, data quality management and knowledge representation are extracted from the application requirements and are used in the database normalization and implemented in the designed database to warranty the data quality. In case of knowledge discovery, dependencies are extracted from the existing data of the database. The extraction process is known as Dependency Discovery. The aim of dependency discovery is to find all dependencies which are satisfied by existing data. Conceptual schema and logical designs are two important steps regarding correctness and integrity of the database model. Data normalization is a common mechanism employed to support database designers to ensure the correctness of their design. Normalization transforms unstructured relation into separate relations, called normalized database. The main purpose of this separation is to eliminate redundant data and reduce data anomaly (i.e., data inconsistency as a result of insert, update, and delete operations). There are many different levels of normalization depending on the purpose of database designer. Most database applications are designed to be either in the third normal forms in which their dependency relations are sufficient for most organizational requirements . In recent years, the discovery of conditional functional dependencies (CFDs) has also seen some work. The aim of dependency discovery is to find important dependencies holding on the data of the database. These discovered dependencies represent domain knowledge and can be used to verify database design and assess data quality. Data dependencies play very important roles in database design, data quality management, and knowledge representation. Functional dependency is a kind of data dependencies. Now-a-days there is a fast growing amount of data that are collected and stored in large databases. As a result, the databases may contain redundant or inconsistent data. Dependencies are very important in the case of database design, data quality management and knowledge representation

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Predicting Opinion Dependency Relations for Opinion Analysis

Predicting Opinion Dependency Relations for Opinion Analysis

This paper focuses on extracting opinio- nated dependency relations from relations gen- erated by the Stanford parser. We design an annotation mechanism on the syntactic struc- tures on the sentence from Chinese Treebank to create an annotation environment with a lower entry barrier so that sufficient annota- tions can be labeled. Then these annotations are aligned to the relations in the correspond- ing dependency trees generated by the same parser from the same sentence as the gold standard for training the automatic annotator of the opinion dependency relations. We conduct experiments on the annotated opinion syntactic structures in parsing trees, and on the opinion dependency relations corresponding to them. The proposed process demonstrates a feasible direction toward the development of an opi- nion dependency parser.

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Towards Joint Morphological Analysis and Dependency Parsing of Turkish

Towards Joint Morphological Analysis and Dependency Parsing of Turkish

The major drawback of a pipeline system is to propagate the disambiguator’s mistakes to the parsing step. Moreover, the disambiguator cannot take advantage of syntactic information that could help disambiguate certain morphological analyses. In (1), the first word kahveleri means ‘the cof- fees (Acc)’, ‘his/her coffees’, ‘their (one) coffee’, ‘their coffees’ from (1a) to (1d). When the first two words come together, they make a sentence meaning ‘His/her/their coffees are at my place’. kahveleri is still ambiguous but its dependency re- lation is clear; bende, with morphological analysis (1e), behaves as a copular predicate with no overt marker and kahveleri is dependent on bende as a subject.

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Strategies for Contiguous Multiword Expression Analysis and Dependency Parsing

Strategies for Contiguous Multiword Expression Analysis and Dependency Parsing

MWE lexicons are exploited as sources of fea- tures for both the dependency parser and the ex- ternal MWE analyzer. In particular, two large- coverage general-language lexicons are used: the Lefff 6 lexicon (Sagot, 2010), which contains ap- proximately half a million inflected word forms, among which approx. 25, 000 are MWEs; and the DELA 7 (Courtois, 2009; Courtois et al., 1997) lexicon, which contains approx. one million in- flected forms, among which about 110, 000 are MWEs. These resources are completed with spe- cific lexicons freely available in the platform Uni- tex 8 : the toponym dictionary Prolex (Piton et al., 1999) and a dictionary of first names. Note that the lexicons do not include any information on the ir- regular or the regular status of the MWEs. In order to compare the MWEs present in the lexicons and those encoded in the French treebank, we applied the following procedure (hereafter called lexicon

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Analysis of the Hindi Proposition Bank using Dependency Structure

Analysis of the Hindi Proposition Bank using Dependency Structure

We need to find a threshold that gives a high preci- sion (so annotators do not get confused by the au- tomatic output) while maintaining a good recall (so annotations can go faster). With a threshold of 0.93 using features (lemma, voice, dependency label), we get a precision of 90.37%, a recall of 44.52%, and an F1-score of 59.65%. Table 7 shows accuracies for all PropBank labels achieved by a threshold of 0.92 using roleset ID ’s instead of predicate’s lem- mas. Although the overall precision stays about the same, we get a noticeable improvement in the over- all recall using roleset ID ’s. Note that some labels are missing in Table 7. This is because either they do not occur in our current data ( ARGC and ARGA ) or we have not started annotating them properly yet (ARGM-MOD and ARGM-NEG).

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