In cross-lingual text classification, it is required that task-specific training data in high-resource source languages are available, where the task is identical to that of a low-resource target language. However, collecting such training data can b
e infeasible because of the labeling cost, task characteristics, and privacy concerns. This paper proposes an alternative solution that uses only task-independent word embeddings of high-resource languages and bilingual dictionaries. First, we construct a dictionary-based heterogeneous graph (DHG) from bilingual dictionaries. This opens the possibility to use graph neural networks for cross-lingual transfer. The remaining challenge is the heterogeneity of DHG because multiple languages are considered. To address this challenge, we propose dictionary-based heterogeneous graph neural network (DHGNet) that effectively handles the heterogeneity of DHG by two-step aggregations, which are word-level and language-level aggregations. Experimental results demonstrate that our method outperforms pretrained models even though it does not access to large corpora. Furthermore, it can perform well even though dictionaries contain many incorrect translations. Its robustness allows the usage of a wider range of dictionaries such as an automatically constructed dictionary and crowdsourced dictionary, which are convenient for real-world applications.
In this paper, we present our systems submitted to SemEval-2021 Task 1 on lexical complexity prediction.The aim of this shared task was to create systems able to predict the lexical complexity of word tokens and bigram multiword expressions within a
given sentence context, a continuous value indicating the difficulty in understanding a respective utterance. Our approach relies on gradient boosted regression tree ensembles fitted using a heterogeneous feature set combining linguistic features, static and contextualized word embeddings, psycholinguistic norm lexica, WordNet, word- and character bigram frequencies and inclusion in wordlists to create a model able to assign a word or multiword expression a context-dependent complexity score. We can show that especially contextualised string embeddings can help with predicting lexical complexity.
Prerequisite relations among concepts are crucial for educational applications, such as curriculum planning and intelligent tutoring. In this paper, we propose a novel concept prerequisite relation learning approach, named CPRL, which combines both c
oncept representation learned from a heterogeneous graph and concept pairwise features. Furthermore, we extend CPRL under weakly supervised settings to make our method more practical, including learning prerequisite relations from learning object dependencies and generating training data with data programming. Our experiments on four datasets show that the proposed approach achieves the state-of-the-art results comparing with existing methods.
This paper presents our findings from participating in the SMM4H Shared Task 2021. We addressed Named Entity Recognition (NER) and Text Classification. To address NER we explored BiLSTM-CRF with Stacked Heterogeneous embeddings and linguistic feature
s. We investigated various machine learning algorithms (logistic regression, SVM and Neural Networks) to address text classification. Our proposed approaches can be generalized to different languages and we have shown its effectiveness for English and Spanish. Our text classification submissions have achieved competitive performance with F1-score of 0.46 and 0.90 on ADE Classification (Task 1a) and Profession Classification (Task 7a) respectively. In the case of NER, our submissions scored F1-score of 0.50 and 0.82 on ADE Span Detection (Task 1b) and Profession span detection (Task 7b) respectively.
In this research two Indomethacin derivatives were
synthesized, Indomethacin ethyl ester was synthesized by
The Direct Esterification method by reacting indomethacin
with ethanol within different conditions of time, molar
ratios and solvents in a
cidic medium using homogeneous
acid catalysts such as sulfuric acid and methane sulfuric
acid and heterogeneous acid catalysts such as amberlyst-
15. Then indomethacin acid hydrazide was synthesized by
condensing the previous ester with hydrazine hydrate and
the best conditions were studied of time, molar ratios,
solvents and different temperatures without using any
catalyst.
In this paper, we propose modifications to TCP by
introducing adaptive delay and loss response (TCP-ADaLR) to
mitigate the adverse effects of satellite link characteristics.
We introduce a taxonomic study of parallel programming models
on High-Performance architectures. We review the parallel
architectures(shared and distributed memory), and then the
development of the architectures through the emergence of the
heter
ogeneous and hybrid parallel architectures.
We review important parallel programming model as the
Partitioned Global Address Space (PGAS) model, as model for
distributed memory architectures and the Data Flow model as
model to heterogeneous and hybrid parallel programming. Finally
we present several scenarios for the use of this taxonomic study.
The Reaction of Glycerol carried out with tow aryl halides
under basic conditions (K2CO3) using 5 mol% of Pd(II)Complex as
catalyst, Pd(PPh3)2Cl2. The typical reaction has been performed
between glycerol and bromobenzene. This reaction is achieved
in
water as solvent. However, the catalyst complex does not dissolve
in aqueous solutions rather it acts as heterogeneous catalyst.
Therefore, it is filtrated at the end of the reaction and reused several
times. Accordingly, new compound were prepared by reacting
glycerol with 1-bromo-2,6-dichlorobenzene. It is anticipated that the
synthesized compounds may have pharmaceutical application.
