LEADER Program—An Inclusive or Selective Instrument for the Development of Rural Space in Romania?

The LEADER program is one of the European Union’s financing instruments dedicated to the development of the rural communities. The instrument was introduced in the Romanian rural territory in 2007, a territory characterised by a high level of local and regional economic inequalities. The main goal of the present research is to question whether the development level specific to the rural communities have influenced their performance in managing the LEADER program. In order to answer this question, the characteristics of the initial level of development were analysed in relation to the spatial distribution of LEADER funds. The indicators taken into consideration were the number of projects, funds per capita, funds per Local Action Group (LAG), and the percentage of employees from the total population. In order to assess the relation between the initial level of development and the LAG’s performance, the method used was the Ordinary Least Squares regression, which calculates a set of statistical parameters that highlight the presence, form, sense, and intensity of the dependency relation. The results highlight no correlation between the level of development and the spatial distribution of the LEADER funds. Analysing the data, the paper reveals that the LEADER program is an inclusive rather than a selective instrument for the development of Romanian rural communities, despite other examples researched in Western countries. The results show that the LEADER program can have an influence in reducing the rural disparities, but its effects are of low importance.

Invisible Images and Indeterminacy: Why We Need a Multi-stage Account of Photography

Some photographs show determinate features of a scene because the photographed scene had those features. This dependency relation is, rightly, a consensus in philosophy of photography. I seek to refute many long-established theories of photography by arguing that they are incompatible with this commitment. In Section II, I classify accounts of photography as either single-stage or multi-stage. In Section III, I analyze the historical basis for single-stage accounts. In Section IV, I explain why the single-stage view led scientists to postulate “latent” photographic images as a technical phenomenon in early chemical photography. In Section V, I discredit the notion of an invisible latent image in chemical photography and, in Section VI, extend this objection to the legacy of the latent image in digital photography. In Section VII, I appeal to the dependency relation to explain why the notion of a latent image makes the single-stage account untenable. Finally, I use the multi-stage account to advance debate about “new” versus “orthodox” theories of photography.

Assessing the impact of automatic dependency annotation on the measurement of phraseological complexity in L2 Dutch

The extraction of phraseological units operationalized in phraseological complexity measures (Paquot, 2019) relies on automatic dependency annotations, yet the suitability of annotation tools for learner language is often overlooked. In the present article, two Dutch dependency parsers, Alpino (van Noord, 2006) and Frog (van den Bosch et al., 2007), are evaluated for their performance in automatically annotating three types of dependency relations (verb + direct object, adjectival modifier, and adverbial modifier relations) across three proficiency levels of L2 Dutch. These observations then serve as the basis for an investigation into the impact of automatic dependency annotation on phraseological sophistication measures. Results indicate that both learner proficiency and the type of dependency relation function as moderating factors in parser performance. Phraseological complexity measures computed on the basis of both automatic and manual dependency annotations demonstrate moderate to high correlations, reflecting a moderate to low impact of automatic annotation on subsequent analyses.

ADPBC: Arabic Dependency Parsing Based Corpora for Information Extraction

There is a massive amount of different information and data in the World Wide Web, and the number of Arabic users and contents is widely increasing. Information extraction is an essential issue to access and sort the data on the web. In this regard, information extraction becomes a challenge, especially for languages, which have a complex morphology like Arabic. Consequently, the trend today is to build a new corpus that makes the information extraction easier and more precise. This paper presents Arabic linguistically analyzed corpus, including dependency relation. The collected data includes five fields; they are a sport, religious, weather, news and biomedical. The output is CoNLL universal lattice file format (CoNLL-UL). The corpus contains an index for the sentences and their linguistic meta-data to enable quick mining and search across the corpus. This corpus has seventeenth morphological annotations and eight features based on the identification of the textual structures help to recognize and understand the grammatical characteristics of the text and perform the dependency relation. The parsing and dependency process conducted by the universal dependency model and corrected manually. The results illustrated the enhancement in the dependency relation corpus. The designed Arabic corpus helps to quickly get linguistic annotations for a text and make the information Extraction techniques easy and clear to learn. The gotten results illustrated the average enhancement in the dependency relation corpus.

A supervised scheme for aspect extraction in sentiment analysis using the hybrid feature set of word dependency relations and lemmas

Due to the massive progression of the Web, people post their reviews for any product, movies and places they visit on social media. The reviews available on social media are helpful to customers as well as the product owners to evaluate their products based on different reviews. Analyzing structured data is easy as compared to unstructured data. The reviews are available in an unstructured format. Aspect-Based Sentiment Analysis mines the aspects of a product from the reviews and further determines sentiment for each aspect. In this work, two methods for aspect extraction are proposed. The datasets used for this work are SemEval restaurant review dataset, Yelp and Kaggle datasets. In the first method a multivariate filter-based approach for feature selection is proposed. This method support to select significant features and reduces redundancy among selected features. It shows improvement in F1-score compared to a method that uses only relevant features selected using Term Frequency weight. In another method, selective dependency relations are used to extract features. This is done using Stanford NLP parser. The results gained using features extracted by selective dependency rules are better as compared to features extracted by using all dependency rules. In the hybrid approach, both lemma features and selective dependency relation based features are extracted. Using the hybrid feature set, 94.78% accuracy and 85.24% F1-score is achieved in the aspect category prediction task.

Non-Causal Counterfactual Dependence and Intrinsicality

When counterfactuals hold concerning entities with properties that stand in some kind of loose existential dependency relation, counterfactual dependence only indicates a causal relationship if part of their corresponding minimal supervenience bases satisfies the analysis of causation. The idea has application even if properties are understood in ways proponents of a powers ontology recommend. An analysis of intrinsic properties in this chapter appeals to three features—External Independence, Duplication Characterization, and Maximizing Recombination—each of which, by itself, doesn’t quite work to demarcate what we have in mind. This provides a second way of approaching the issue as well as assisting with later analysis of varieties of Humean supervenience.

The Analysis of Causation

Causation is a dependency relation that need not be general. Consequently, we should focus on counterfactuals rather than regularities and laws for analysing causation. The plausibility of indeterministic cases of causation indicate that there should be appeal to probabilities, which is more successfully developed by appeal to counterfactuals rather than conditional probabilities. The analysis focuses on the nature of causation rather than our concept of it and will have reductive aims. The analysis draws upon everyday knowledge of causation in our lives and so should not be characterized as developed a priori and is not threatened by issues raised about analysis by experimental philosophers.