morphological features
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Treebanks are valuable linguistic resources that include the syntactic structure of a language sentence in addition to part-of-speech tags and morphological features. They are mainly utilized in modeling statistical parsers. Although the statistical natural language parser has recently become more accurate for languages such as English, those for the Arabic language still have low accuracy. The purpose of this article is to construct a new Arabic dependency treebank based on the traditional Arabic grammatical theory and the characteristics of the Arabic language, to investigate their effects on the accuracy of statistical parsers. The proposed Arabic dependency treebank, called I3rab, contrasts with existing Arabic dependency treebanks in two main concepts. The first concept is the approach of determining the main word of the sentence, and the second concept is the representation of the joined and covert pronouns. To evaluate I3rab, we compared its performance against a subset of Prague Arabic Dependency Treebank that shares a comparable level of details. The conducted experiments show that the percentage improvement reached up to 10.24% in UAS and 18.42% in LAS.

Disjoined noun phrases, like indefinites, may introduce indeterminate discourse referents. Disjunction provides more flexibility in some respects than indefinites, though, as the two disjuncts may bear different morphological features, and a disjunctive discourse referent may have a split antecedent. Sign language, too, has been shown to bear on arguments pertaining to discourse anaphora. Notably, discourse referents may be established at locations in the signing space (loci), closely paralleling the use of variables in dynamic semantics. Here, we compare several theories of disjunctive anaphora and of space in sign language with new data from French Sign Language (LSF). We argue that loci must be mediated by a featural layer that iconically preserves mereological properties.

The Luciolinae genus Emeia Fu, Ballantyne & Lambkin, 2012 is reviewed. Firefly species tree is reconstructed based upon phylogenetic analysis of CO1 barcoding sequences from 42 fireflies and 2 outgroup species, including three main Lampyridae sub-families: Luciolinae, Photurinae and Lampyrinae. The genus Emeia belongs to Luciolinae based on morphological and DNA sequence level evidences, the diagnostic features for male adults include pink-red pronotum with a black median stripe, subparallel-sided pronotum, un-developed elytral humeral carina. In this study, a new Emeia species,Emeia pulchra sp. nov. is described and illustrated from the wetland of Lishui, Zhejiang, China. The new species is sister to E. pseudosauteri in the Emeia genus which is supported by characteristic morphological features and DNA barcoding data. The two species are separated geographically as shown on the distribution map. A key to the males of the species of Emeia is also provided.

Patchouli plants are main raw materials for essential oils in Indonesia. Patchouli leaves have a very varied physical form based on the area planted, making it difficult to recognize the variety. This condition makes it difficult for farmers to recognize these varieties and they need experts’ advice. As there are few experts in this field, a technology for identifying the types of patchouli varieties is required. In this study, the identification model is constructed using a combination of leaf morphological features, texture features extracted with Wavelet and shape features extracted with convex hull. The results of feature extraction are used as input data for training of classification algorithms. The effectiveness of the input features is tested using three classification methods in class artificial neural network algorithms: (1) feedforward neural networks with backpropagation algorithm for training, (2) learning vector quantization (LVQ), (3) extreme learning machine (ELM). Synthetic minority over-sampling technique (SMOTE) is applied to solve the problem of class imbalance in the patchouli variety dataset. The results of the patchouli variety identification system by combining these three features indicate the level of recognition with an average accuracy of 72.61%, accuracy with the combination of these three features is higher when compared to using only morphological features (58.68%) or using only Wavelet features (59.03 %) or both (67.25%). In this study also showed that the use of SMOTE in imbalance data increases the accuracy with the highest average accuracy of 88.56%.

The selection fund of Japanese henomeles differs in considerable variety. It was one of the most interesting and rare objects for acclimatization in order to enrich our country мwith plants useful for the national economy. This fruit crop has special genetic and morphological features, due to which it is used in various fields and directions. Therefore, the study of henomeles is quite relevant and requires the development and improvement of various agronomic techniques o cultivation, taking into account the conditions of the region of cultivation of this plant.