Neural Techniques for German Dependency Parsing

Syntactic parsing is the task of analyzing the structure of a sentence based on some predefined formal assumption. It is a key component in many natural language processing (NLP) pipelines and is of great benefit for natural language understanding (NLU) tasks such as information retrieval or sentiment analysis. Despite achieving very high results with neural network techniques, most syntactic parsing research pays attention to only a few prominent languages (such as English or Chinese) or language-agnostic settings. Thus, we still lack studies that focus on just one language and design specific parsing strategies for that language with regards to its linguistic properties. In this thesis, we take German as the language of interest and develop more accurate methods for German dependency parsing by combining state-of-the-art neural network methods with techniques that address the specific challenges posed by the language-specific properties of German. Compared to English, German has richer morphology, semi-free word order, and case syncretism. It is the combination of those characteristics that makes parsing German an interesting and challenging task. Because syntactic parsing is a task that requires many levels of language understanding, we propose to study and improve the knowledge of parsing models at each level in order to improve syntactic parsing for German. These levels are: (sub)word level, syntactic level, semantic level, and sentence level. At the (sub)word level, we look into a surge in out-of-vocabulary words in German data caused by compounding. We propose a new type of embeddings for compounds that is a compositional model of the embeddings of individual components. Our experiments show that character-based embeddings are superior to word and compound embeddings in dependency parsing, and compound embeddings only outperform word embeddings when the part-of-speech (POS) information is unavailable. Thus, we conclude that it is the morpho-syntactic information of unknown compounds, not the semantic one, that is crucial for parsing German. At the syntax level, we investigate challenges for local grammatical function labeler that are caused by case syncretism. In detail, we augment the grammatical function labeling component in a neural dependency parser that labels each head-dependent pair independently with a new labeler that includes a decision history, using Long Short-Term Memory networks (LSTMs). All our proposed models significantly outperformed the baseline on three languages: English, German and Czech. However, the impact of the new models is not the same for all languages: the improvement for English is smaller than for the non-configurational languages (German and Czech). Our analysis suggests that the success of the history-based models is not due to better handling of long dependencies but that they are better in dealing with the uncertainty in head direction. We study the interaction of syntactic parsing with the semantic level via the problem of PP attachment disambiguation. Our motivation is to provide a realistic evaluation of the task where gold information is not available and compare the results of disambiguation systems against the output of a strong neural parser. To our best knowledge, this is the first time that PP attachment disambiguation is evaluated and compared against neural dependency parsing on predicted information. In addition, we present a novel approach for PP attachment disambiguation that uses biaffine attention and utilizes pre-trained contextualized word embeddings as semantic knowledge. Our end-to-end system outperformed the previous pipeline approach on German by a large margin simply by avoiding error propagation caused by predicted information. In the end, we show that parsing systems (with the same semantic knowledge) are in general superior to systems specialized for PP attachment disambiguation. Lastly, we improve dependency parsing at the sentence level using reranking techniques. So far, previous work on neural reranking has been evaluated on English and Chinese only, both languages with a configurational word order and poor morphology. We re-assess the potential of successful neural reranking models from the literature on English and on two morphologically rich(er) languages, German and Czech. In addition, we introduce a new variation of a discriminative reranker based on graph convolutional networks (GCNs). Our proposed reranker not only outperforms previous models on English but is the only model that is able to improve results over the baselines on German and Czech. Our analysis points out that the failure is due to the lower quality of the k-best lists, where the gold tree ratio and the diversity of the list play an important role

Neural reranking for dependency parsing: An evaluation

Recent work has shown that neural rerankers can improve results for dependency parsing over the top k trees produced by a base parser. However, all neural rerankers so far have been evaluated on English and Chinese only, both languages with a configurational word order and poor morphology. In the paper, we re-assess the potential of successful neural reranking models from the literature on English and on two morphologically rich(er) languages, German and Czech. In addition, we introduce a new variation of a discriminative reranker based on graph convolutional networks (GCNs). We show that the GCN not only outperforms previous models on English but is the only model that is able to improve results over the baselines on German and Czech. We explain the differences in reranking performance based on an analysis of a) the gold tree ratio and b) the variety in the k-best lists

Parsers know best: German PP attachment revisited

In the paper, we revisit the PP attachment problem which has been identified as one of the major sources for parser errors and discuss shortcomings of recent work. In particular, we show that using gold information for the extraction of attachment candidates as well as a missing comparison of the system's output to the output of a full syntactic parser leads to an overly optimistic assessment of the results. We address these issues by presenting a realistic evaluation of the potential of different PP attachment systems, using fully predicted information as system input. We compare our results against the output of a strong neural parser and show that the full parsing approach is superior to modeling PP attachment disambiguation as a separate task

An investigation the main internal brand crisis antecedents

To enrich literature of brand crisis causes regards internal perspective, this paper investigates internal brand shortage as crisis antecedents provoking brand fire consequently. Phenomenological approach is adopted using in-depth interviews, key-note seminar and validating by case studies analysis, internal brand crises antecedents were explored based on insights taken from experts in marketing and branding industry. Drafting from the phenomenological research, there are six problems leading to crisis found as follows: lack of human-centred strategy, lack of crisis prevention, lack of market understanding, lack of leadership and management skill, lack of innovation, and lack of quality assurance. These internal antecedents which accumulate to both performance-related and value-related brand crisis. This paper can have explicit implications for marketer, branders and managers, understanding these drivers and its occurrence, business managers are able to scan and analyses crisis situation faster to form timely response to crisis

A METHOD TO IMPROVE THE TIME OF COMPUTING BETWEENNESS CENTRALITY IN SOCIAL NETWORK GRAPH

The Betweenness centrality is an important metric in the graph theory and can be applied in the analyzing social network. The main researches about Betweenness centrality often focus on reducing the complexity. Nowadays, the number of users in the social networks is huge. Thus, improving the computing time of Betweenness centrality to apply in the social network is neccessary. In this paper, we propose the algorithm of computing Betweenness centrality by reduce the similar nodes in the graph in order to reducing computing time. Our experiments with graph networks result shows that the computing time of the proposed algorithm is less than Brandes algorithm. The proposed algorithm is compared with the Brandes algorithm [3] in term of execution time

Biocontrol of Alternaria alternata YZU, a causal of stem end rot disease on pitaya, with soil phosphate solubilizing bacteria

Stem end rot is the most destructive disease caused by Alternaria alternata YZU in pitaya-growing regions of Vietnam. This study was conducted to characterize antagonistic phosphate-solubilizing bacteria (PSB) from rhizosphere soil for their biocontrol activities against A. alternata YZU and evaluate the effect of temperature, pH, and water activity on that antagonism. Among seven PSB isolated from 45 rhizosphere soil samples, PSB31 (identified as Bacillus sp. strain IMAU61039, Accession number: MF803700.1) exhibited the highest antagonistic activity against A. alternata YZU with an average inhibition diameter of 0.65 ± 0.05 cm. The results also show that the strain PSB31 controlled the mycelial growth of A. alternata YZU by secreting antifungal metabolites. The most potent inhibitory activity was identified under in vitro conditions of 25 °C, pH 7, and aw 1. The isolated PSB31 could be a potential biological control agent against A. alternata YZU

The Combined Use of Pediococcus pentosaceus and Fructooligosaccharide Improves Growth Performance, Immune Response, and Resistance of Whiteleg Shrimp Litopenaeus vannamei Against Vibrio parahaemolyticus

In this study, we evaluated the effect of probiotic bacteria Pediococcus pentosaceus supplemented at different inclusion levels in a control diet [basal diet containing 0.5% fructooligosaccharide (FOS)] on the growth performance, feed conversion ratio, immune response, and the disease resistance of whiteleg shrimp Litopenaeus vannamei juveniles against Vibrio parahaemolyticus. A control diet with 0.5% FOS but without P. pentosaceus supplementation (Control) was prepared. In addition, three other test diets were also formulated: control diet supplemented with P. pentosaceus at (i) 1 x 10(6) cfu g(-1) diet (P1), (ii) 1 x 10(7) cfu g(-1) diet (P2), or (iii) 1 x 10(8) cfu g(-1) diet (P3). After a 60-day feeding trial, the experimental shrimps were challenged with V. parahaemolyticus. The results showed that dietary supplementation of P. pentosaceus significantly improved the growth performance and immune responses of L. vannamei juveniles. The juveniles that were fed with a P2 or P3 diet recorded the maximum increase in the final body weight, final length, weight gain, and survival rate. The total hemocyte counts, phenoloxidase, and lysozyme activity of shrimp fed with either of these two diets were significantly enhanced. The results also showed that juveniles fed with a P2 or P3 diet exhibited significantly lower mortality when challenged with V. parahaemolyticus. Overall results suggested that a combination of P. pentosaceus at the inclusion level of 1 x 10(7) cfu g(-1) diet (P2) and 0.5% FOS could be considered as a potential synbiotic formulation for improving the growth, health, and robustness of L. vannamei

Calculation of Temperature-Dependent Thermal Expansion Coefficient of Metal Crystals Based on Anharmonic Correlated Debye Model

This study aims to calculate the anharmonic thermal expansion (TE) coefficient of metal crystals in the temperature dependence. The calculation model is derived from the anharmonic correlated Debye (ACD) model that is developed using the many-body perturbation approach and correlated Debye model based on the anharmonic effective potential. This potential has taken into account the influence on the absorbing and backscattering atoms of all their nearest neighbors in the crystal lattice. The numerical results for the crystalline zinc (Zn) and crystalline copper (Cu) are in agreement with those obtained by the other theoretical model and experiments at several temperatures. The analytical results show that the ACD model is useful and efficient in analyzing the TE of coefficient of metal crystals

The effect of polyamine 70000 (BT70) on the zinc plating process in the non-cyanide alkaline plating bath

The effect of polyamine having molecular weight 70,000u (BT70) in the non-cyanide alkaline plating bath on the morphological zinc electrodeposited coating was investigated. The obtained results from the reflectance of electrodeposited zinc coating by a suitable range of electro-current density and SEM images showed that BT70 had effect on the zinc plating in comparison with the zinc deposits from plating bath without BT70. The electrodeposited coating surface was rough and poor adhesion. However, adding BT70 into the plating bath led to the surface roughness of electrodeposited coating and grand size being reduced. Zinc electrodeposited coating from a plating bath containing BT70 at 0.5 g/L, respectively, experienced the highest reflectance, equal 46 % of the electrodeposited coating from the bath containing commercial additives. Hull method showed that the zinc deposited coating surface became smoother with the presence of polyamine in non-cyanide alkaline zinc plating solution. The zinc deposited coating had a semi-gloss scope at 10 A/dm2. If the BT70 content increased, the semi-gloss scop and the gloss of samples’ surfaces also grew up. Keywords. Polyamine, additive, non-cyanide alkaline zinc plating, zinc coating

The potential of combining UAV and remote sensing in supporting precision mapping of irrigation systems for paddy land in urban agricultural areas: study case in the Hoa Vang district, Danang city, Central Vietnam

This research was carried out to test the potential of combining unmanned aerial vehicle (UAV) and remote sensing (RS) to support precision mapping of irrigation systems for paddy land. The study area is an urban/agricultural area of Central Vietnam. The Sentinel-2A imagery acquired on 30 June 2018 was interpreted according an object-based classification method aiming to map paddy land and irrigation systems for the Hoa Vang district; the total accuracy was 91.33% with a Kappa coefficient of 0.87. However, with the spatial resolution from the Sentinel-2A images (20 meters x 20 meters) it was difficult to classify paddy land and water from other objects within small and scattered parcel areas. This research was designed on five experimental flying zones, collecting 2,085 images by the UAV. With the very high spatial resolution data of the UAV, it was possible to clearly identify the boundaries of paddy land parcels, water sources such as rivers and lakes, and other objects such as canals and concrete irrigation systems. This classification derived from the orthogonal images from the five experimental zones using an object-based classification method, correcting the interpretation results of the Sentinel 2A images. Outcomes indicate that, the combination of UAV and RS can be applied to support precision mapping of irrigation systems for paddy land in urban agricultural areas.Nghiên cứu này được thực hiện nhằm thử nghiệm khả năng kết hợp giữa UAV với viễn thám trong hỗ trợ độ chính xác của bản đồ hệ thống nước tưới cho đất trồng lúa ở vùng nông nghiệp đô thị tại Miền trung Việt Nam. Ảnh viễn thám Sentinel- 2A thu nhận vào 30/6/2018 đã được giải đoán bằng phương pháp định hướng đối hướng để thành lập bản đồ hệ thống nguồn nước tưới cho huyện Hòa Vang vào năm 2018, với kết quả độ chính xác tổng số là 91,33% và hệ số kappa là 0,87. Mặc dù với kết quả giải đoán có độ chính xác cao nhưng với độ phân giải không gian của ảnh Sentinel-2A là 20m x 20m rất khó để phân loại được các vùng đất lúa có diện tích nhỏ và phân bố phân tán. Nghiên cứu này đã thiết kế 5 khu vực bay thử nghiệm với 2.085 ảnh để thu thập dữ liệu từ UAV. Có thể thấy rằng dữ liệu ảnh từ UAV với độ phân giải siêu cao có thể nhận diện và phân biệt được một cách rõ ràng không chỉ ranh giới của các thửa đất lúa, hệ thống nguồn nước như sông hồ, mà còn cả những đối tượng kênh mương thủy lợi nhỏ. Kết quả giải đoán các ảnh bay chụp bằng UAV sử dụng dụng phương pháp định hướng đối tượng, nghiên cứu này đã hiệu chỉnh được kết quả giải đoán ảnh Sentinel 2A. Kết quả cho thấy việc kết hợp dữ liệu viễn thám với UAV là hoàn toàn có khả năng sử dụng để hỗ trợ độ chính xác thành lập bản đồ hệ thống nguồn nước cho đất trồng lúa ở vùng nông nghiệp đô thị