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Recognizing TimeML events and identifying their attributes, are important tasks in natural language processing (NLP). Several NLP applications like question answering, information retrieval, summarization, and temporal information extraction need to have some knowledge about events of the input documents. Existing methods developed for this task are restricted to limited number of languages, and for many other languages including Persian, there has not been any effort yet. In this paper, we introduce two different approaches for automatic event recognition and classification in Persian. For this purpose, a corpus of events has been built based on a specific version of ISO-TimeML for Persian.... 

Automatic extraction of temporal relations between event pairs is an important task for several natural language processing applications such as Question Answering, Information Extraction, and Summarization. Since most existing methods are supervised and require large corpora, which for many languages do not exist, we have concentrated our efforts to reduce the need for annotated data as much as possible. This paper presents two different algorithms towards this goal. The first algorithm is a weakly supervised machine learning approach for classification of temporal relations between events. In the first stage, the algorithm learns a general classifier from an annotated corpus. Then,... 

The ability to accurately determine temporal relations between events is an important task for several natural language processing applications such as Question Answering, Summarization, and Information Extraction. Since current supervised methods require large corpora, which for many languages do not exist, we have focused our attention on approaches with less supervision as much as possible. This paper presents a fully generative model for temporal relation extraction based on the expectation maximization (EM) algorithm. Our experiments show that the performance of the proposed algorithm, regarding its little supervision, is considerable in temporal relation learning  

The ability to accurately classify temporal relation between events is an important task for a large number of natural language processing applications such as Question Answering (QA), Summarization, and Information Extraction. This paper presents a weakly-supervised machine learning approach for classification of temporal relation between events. In the first stage, the algorithm learns a general classifier from an annotated corpus. Then, it applies the hypothesis of "one type of temporal relation per discourse" and expands the scope of "discourse" from a single document to a cluster of topically-related documents. By combining the global information of such a cluster with local decisions... 

Grammar induction is one of attractive research areas of natural language processing. Since both supervised and to some extent semi-supervised grammar induction methods require large treebanks, and for many languages, such treebanks do not currently exist, we focused our attention on unsupervised approaches. Constituent Context Model (CCM) seems to be the state of the art in unsupervised grammar induction. In this paper, we show that the performance of CCM in free word order languages (FWOLs) such as Persian is inferior to that of fixed order languages such as English. We also introduce a novel approach, called parent-based constituent context model (PCCM), and show that by using some... 

Contemporary question answering (QA) systems, including Transformer-based architectures, suffer from increasing computational and model complexity which render them inefficient for real-world applications with limited resources. Furthermore, training or even fine-tuning such models requires a vast amount of labeled data which is often not available for the task at hand. In this manuscript, we conduct a comprehensive analysis of the mentioned challenges and introduce suitable countermeasures. We propose a novel knowledge distillation (KD) approach to reduce the parameter and model complexity of a pre-trained bidirectional encoder representations from transformer (BERT) system and utilize... 

Temporal relation classification is one of the contemporary demanding tasks in natural language processing. This task can be used in various applications such as question answering, summarization, and language specific information retrieval. In this paper, we propose an improved algorithm for classifying temporal relations between events and times, using support vector machines (SVM). Along with gold-standard corpus features, the proposed method aims at exploiting useful syntactic features, which are automatically generated, to improve accuracy of the classification. Accordingly, a number of novel kernel functions are introduced and evaluated for temporal relation classification. The result... 

Temporal relation classification is one of contemporary demanding tasks of natural language processing. This task can be used in various applications such as question answering, summarization, and language specific information retrieval. In this paper, we propose an improved algorithm for classifying temporal relations, between events or between events and time, using support vector machines (SVM). Along with gold-standard corpus features, the proposed method aims at exploiting some useful automatically generated syntactic features to improve the accuracy of classification. Accordingly, a number of novel kernel functions are introduced and evaluated. Our evaluations clearly demonstrate that... 

This paper introduces the first pattern-based Persian Temporal Relation Classifier (PTRC) that finds the type of temporal relations between pairs of events in the Persian texts. The proposed system uses support vector machines (SVMs) equipped by combinations of simple, convolution tree, and string subsequence kernels (SSK). In order to evaluate the algorithm, we have developed a Persian TimeBank (PTB) corpus. PTRC not only increases the performance of the classification by applying new features and SSK, but also alleviates the probable adverse effects of the Free Word Orderness (FWO) of Persian on temporal relation classification. We have also applied our proposed algorithm to two standard... 

The ability to accurately classify temporal relations between events is an important task in a large number of natural language processing and text mining applications such as question answering, summarization, and language specific information retrieval. In this paper, we propose an improved way of classifying temporal relations, using support vector machines (SVM). Along with gold-standard corpus features, the proposed method aims at exploiting useful syntactic features, which are automatically generated, to improve accuracy of the SVM classification method. Accordingly, a number of novel kernel functions are introduced and evaluated for temporal relation classification. Our evaluations... 

Question answering (QA) systems have reached human-level accuracy; however, these systems are not robust enough and are vulnerable to adversarial examples. Recently, adversarial attacks have been widely investigated in text classification. However, there have been few research efforts on this topic in QA. In this article, we have modified the attack algorithms widely used in text classification to fit those algorithms for QA systems. We have evaluated the impact of various attack methods on QA systems at character, word, and sentence levels. Furthermore, we have developed a new framework, named RobustQA, as the first open-source toolkit for investigating textual adversarial attacks in QA... 

Treebanks are not large enough to reliably model precise lexical phenomena. This deficiency provokes attachment errors in the parsers trained on such data. We propose in this paper to compute lexical affinities, on large corpora, for specific lexico-syntactic configurations that are hard to disambiguate and introduce the new information in a parser. Experiments on the French Treebank showed a relative decrease of the error rate of 7.1% Labeled Accuracy Score yielding the best parsing results on this treebank  

Blood vessels, especially large vessels have a greater thermal effect on freezing tissue during cryosurgery. Vascular networks act as heat sources in tissue, and cause failure in cryosurgery and reappearance of cancer. The aim of this study is to numerically simulate the effect of probe location and multiprobe on heat transfer distribution. Furthermore, the effect of nanoparticles injection is studied. It is shown that the small probes location near large blood vessels could help to reduce the necessary time for tissue freezing. Nanoparticles injection shows that the thermal effect of blood vessel in tissue is improved. Using Au, Ag and diamond nanoparticles have the most growth of ice ball... 

In this work, the effects of the surface-modified nanodiamond particles (NDs) on the barrier, rheological, mechanical and thermal properties of ethylene vinyl acetate (EVA) composites for the packaging applications were investigated. Fourier transform infrared spectroscopy, as well as thermal gravimetric analysis were employed to study the grafting of vinyltriethoxy silane (VTS) on the surface of NDs. Afterwards, EVA samples containing 0, 0.1, 0.5, 1, 1.5 and 2 wt% of surface-modified NDs were prepared by a two-stage process including the solution and injection processes. In order to evaluate the physicochemical, rheological, mechanical and thermal properties of the EVA/NDs samples, field... 

The effect of temperature, pressure and bonding time on microstructure of temperature gradient transient liquid phase (TGTLP) diffusion bonded Al7075 alloy using liquid gallium interlayer was investigated. The selected bonding method relies on using the minimum amount of liquid gallium on faying surfaces, using a very fast heating rate to reach the joining temperature and imposing a temperature gradient across the bond region. The microstructure of the diffusion bonded joints was evaluated by light optical microscopy, scanning electron microscopy and energy dispersive spectroscopy (EDS). Results show that by increasing the temperature, pressure and time of joining, a more uniform... 

A major problem in real-time packet-based communication systems, is misrouted or delayed packets which results in degraded perceived voice quality. If packets are not available on time, the packets are considered as lost. The easiest solution in a network terminal receiver is to replace silence for the duration of lost speech segments. In a high quality communication system, to avoid degradation in speech quality due to packet loss, a suitable method or algorithm is needed to replace the missing segments of speech. In this paper, we introduce an adaptive filter for replacement of lost speech segment. In this method Kalman filter as a state-space based method will be used to predict the... 

In this study, the effects of the type and content of reactive diluents on the permeation/separation of carbon dioxide/nitrogen (CO2/N2) through acrylate-terminated polyurethane (APU)-acrylate/acrylic diluent (APUA) composite membranes was investigated. A series of APUs based on poly(ethylene glycol) (PEG)-1000 g mol−1, toluene diisocyanate, and 2-hydroxyethyl methacrylate was synthesized and then diluted with several reactive diluents. The results obtained from differential scanning calorimetry (DSC) and Fourier transform infrared analyses showed that the microphase interference of hard and soft segments increased with increasing reactive diluent content. Furthermore, with increasing alkene... 

Using two techniques of thermogravimetry and differential scanning calorimetry under O2 gas atmosphere from 25 to 600°C, the thermal behavior of laboratory-produced compound lead(IV) oxide α-PbO2 was investigated. The identity of products at different stages were confirmed by XRD technique. Both techniques produced similar results supporting the same decomposition stages for the compound. Three distinct energy changes were observed, namely, two endothermic and one exothermic in DSC. The amount of ΔH for each peak is also reported. © 2008 Springer Science+Business Media, LLC