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Event argument extraction, which aims to identify arguments of specific events and label their roles, is a challenging subtask of event extraction. Previous approaches solve this problem in a two-stage manner that first extracts named entities as argument candidates and then determines their roles. However, many nested entities may be missed or wrongly predicted during the argument candidate extraction procedure, which substantially affects the performance of the downstream classifier. In this paper, we propose a novel one-step question answering based framework, which performs argument candidate extraction and argument role classification simultaneously to mitigate the error propagation problem in conventional two-stage methods. Since the conventional question answering based framework cannot be applied directly to this task, we design a Question Answering based Sequence Labeling (QA-SL) model to tackle inexistent argument roles and multiple argument token spans. Moreover, considering the overwhelming number of parameters in question answering based neural network models and the relatively small size of event extraction corpus, we fine-tune the pre-trained model from BERT to mitigate the data scarcity problem. Extensive experiments demonstrate the benefits of the proposed method, leading to a competitive performance compared with state-of-the-art methods. To the best of our knowledge, this is the first work to cast event argument extraction as a question answering task.

Knowledge Base Question Answering (KBQA) is a promising approach for users to access substantial knowledge and has become a research focus in recent years. Our paper focuses on relation detection, a subtask of KBQA and proposes an adversarial training improved multi-path multi-scale relation detector (AdvT-MMRD) to improve the performance of a common KBQA system. To solve the problem of matching the casual form of a question with the logical form of a predicate, we use question pattern-relation matching, in which an attention-based bidirectional recurrent neural network with gated recurrent units (Bi-GRUs) is used to match semantic similarity and a convolutional neural network (CNN) is used to learn literal similarity between question and relation. We also explore two ways to measure the relevance of entity type-relation pairs through several level representations. Additionally, an adversarial training strategy is conducted to enhance our model. The experimental results demonstrate that our approach not only achieves a state-of-the-art accuracy of 93.8% on relation detection task, but contributes our KBQA system to reaching an outstanding accuracy of 79.0% on the SimpleQuestions benchmark.

Network embedding (NE) aims to represent network information appropriately by learning low-dimensional and dense vectors for the nodes and edges of information network. Actually, the real world is almost full of heterogeneous information networks, which stimulates the emergence of heterogeneous information networks (HINs) embedding models. However, parts of existing HIN embedding models like meta-path-based methods only capture limited and aggregated information of relations, whereas some models based on metric or distance learning are usually of high computational complexity and slow training speed. In this paper, we present a novel heterogeneous information network embedding model, which applies dynamic projection metrics and translation mechanisms to the complicated heterogeneous information networks including multiple nodes and different relations. In order to overcome the imbalance of the distribution of relations in HIN and optimize the training process, we introduce an adaptive loss function for model optimization. Further more, we propose a hybrid model with baseline method as the initialization of the model. Experiments have been implemented on some real-world HIN datasets. And empirical results show that our model significantly outperforms the state-of-the-art representation learning models.

Aspect-based sentiment analysis, which aims to predict the sentiment polarities for the given aspects or targets, is a broad-spectrum and challenging research area. Recently, pre-trained models, such as BERT, have been used in aspect-based sentiment analysis. This fine-grained task needs auxiliary information to distinguish each aspect. But the input form of BERT is only a words sequence which can not provide extra contextual information. To address this problem, we introduce a new method named GBCN which uses a gating mechanism with context-aware aspect embeddings to enhance and control the BERT representation for aspect-based sentiment analysis. Firstly, the input texts are fed into BERT and context-aware embedding layer to generate BERT representation and refined context-aware embeddings separately. These refined embeddings contain the most correlated information selected in the context. Then, we employ a gating mechanism to control the propagation of sentiment features from BERT output with context-aware embeddings. The experiments of our model obtain new state-of-the-art results on the SentiHood and SemEval-2014 datasets, achieving a test F1 of 88.0 and 92.9 respectively.

Aspect-level sentiment analysis is a fine-grained task in sentiment analysis that aims at detecting sentiment polarity towards a specific target in a sentence. Previous studies focus on using global attention mechanism that attends to all words in the context to model the interaction between target and sentence. However, global attention suffers from assigning high-attention score to irrelevant sentiment words in the cases where sentence contains noisy words or multiple targets. To address this problem, we propose a novel syntax-directed hybrid attention network (SHAN). In SHAN, a global attention is employed to capture coarse information about the target, and a syntax-directed local attention is used to take a look at words syntactically close to the target. An information gate is then utilized to synthesize the information from local and global attention results and adaptively generate a less-noisy and more sentiment-oriented representation. The experimental results on SemEval 2014 Datasets demonstrate the effectiveness of the proposed method.

In the electromagnetic space, a single channel radar receiver will often intercept several periodic pulse trains radiating from the surrounding emitters simultaneously. The aim of radar pulse deinterleaving is to sort out the pulses coming from different emitters. Most traditional pulse repetition interval (PRI) deinterleaving methods are easy to sort out the pulses with small PRI fluctuations but difficult in dealing with relatively bigger fluctuation or staggered PRIs. In addition, this searching procedure can easily cause pulse omission phenomenon and even generate false pulses. In this paper, we propose an improved histogram method for PRI deinterleaving based on pulse correlation to overcome the above-mentioned shortcomings. After calculating the multi-level time difference histogram, we introduce the mean filter and interquartile range algorithm to optimize the estimated PRI values. Our method extracts the pulse pairs based on pulse correlation directly instead of searching for the pulses and then determines whether the PRI is staggered or not. The experiments on simulation data show that our method can achieve better performance on both the pulse trains of jittered PRIs and the staggered PRIs.

Graph layout investigates the structure of the graph in order to better obtain the information implied in the graph. To solve the shortcomings of dimension reduction layouts on local adjustment and the insufficiency of energy models to maintain the overall structure of the graphs, this paper proposes a new graph layout framework called “tNEM” that layouts graphs by combining t-distributed neighbor retrieval visualizer (t-NeRV) and energy models. In the process of layout, our algorithm considers global and local structures at the same time. The layout results are more conform to aesthetic standards, meanwhile, maintain the structural information of the graph. We evaluate our algorithm on a wide variety of datasets and compare it with many other methods. We produce better visualization results than tsNET and tsNET* methods by reducing the tendency to crowd points together, and can better capture the global structure of the graph.

Named entity recognition (NER) is one of the basic techniques in natural language processing tasks. Chinese NER is complicated and difficult which remains a major challenge. One of the main reasons is that the boundaries of entities in Chinese are blurred and closely related to word segmentation results. Previous studies for this task have broadly divided into two categories, word-based, and character-based methods. However, the former class suffers from the word segmentation errors, and the latter cannot make full use of the information on multiple granularities. To address these problems, we investigate a new dynamic meta-embeddings method and apply it to Chinese NER task, which utilizes attention mechanism to combine features of both character and word granularity in embedding layer. The meta-embeddings created by our method are dynamic, data-specific, and task-specific, since the meta-embeddings for same characters in different sentence sequences are distinct. The experiments on MSRA and LiteratureNER datasets validate the effectiveness of our model, and this method achieves the state-of-the-art results on LiteratureNER.

Chinese Named Entity Recognition (Chinese NER) is an important task in Chinese natural language processing field. It is difficult to identify the boundary of entities because Chinese texts lack natural delimiters to separate words. For this task, two major methods can be distinguished by the model inputs, i.e., word-based model and character-based model. However, the word-based model relies on the result of the Chinese Word Segmentation (CWS), and the character-based model cannot utilize enough word-level information. In this paper, we propose a multi-granularity information fusion model (MIFM) for the Chinese NER task. We introduce a novel multi-granularity embedding layer that utilizes the attention mechanism and an information gate to fuse the character and word level features. The results of this embedding method are dynamic and data-specific because they are calculated based on different contexts. Moreover, we apply the reverse stacked LSTM layer to gain deep semantic information for a sequence. Experiments on two benchmark datasets, MSRA and ResumeNER, show that our approach can effectively improve the performance of Chinese NER.

Multiple-object tracking (MOT) has received an increasing attention due to the rapid development of autonomous driving. However, the MOT problem is still challenging mainly due to the occlusion and scale variation. Motivated by the fact that the discriminative correlation filters-based (DCFB) tracking algorithms can tackle these problems and significantly improve the accuracy of single object tracking, how to exploit the DCFB tracking algorithms for MOT is worthy studying. Moreover, the corrupted training samples due to the occlusion make DCFB tracking methods to update the appearance model of target uncorrected and result in tracking drift. In this paper, we exploit Markov decision process to integrate the DCFB tracking method into our MOT framework and address the update problem of the appearance model in DCFB tracking method. Moreover, in order to overcome the challenges of occlusion and scale variation, to prevent target drift during tracking, we use two DCFB trackers with different update frequencies and a novel update strategy to predict the location of targets. The part-based method is used to extract robust features to tackling the challenges of occlusion and scale change. In order to verify the efficiency of our algorithm, experiments are performed based on KITTI tracking benchmark. The results demonstrate that our method achieves state-of-the-art performance and outperforms the state-of-the-art algorithms in road scenarios.

Unlike single geospatial objects extraction from high-resolution remote sensing images, the task of road extraction faces more challenges, including its narrowness, sparsity, diversity, multiscale characteristics, and class imbalance. Focusing on these challenges, this paper proposes an end-to-end framework called the multiple feature pyramid network (MFPN). In MFPN, we design an effective feature pyramid and a tailored pyramid pooling module, taking advantage of multilevel semantic features of high-resolution remote sensing images. In the optimization stage, a weighted balance loss function is presented to solve the class imbalance problem caused by the sparseness of roads. The proposed novel loss function is more sensitive to the misclassified and the sparse real labeled pixels and helps to focus on the spare set of hard pixels in the training stage. Compared with the cross-entropy loss function, the weighted balance loss can reduce training time dramatically for the same precision. Experiments on two challenging datasets of high-resolution remote sensing images which illustrate the performance of the proposed algorithm have achieved significant improvements, especially for narrow rural roads.

Community detection plays a vital role in network analysis, simplification, and compression, which reveals the network structure by dividing a network into several internally dense modules. Among plenty of methods, those based on statistical inference are widely used because they are theoretically sound and consistent. However, in many of them, the number of communities needs to be provided in advance or computed in a time-consuming way and parameters are usually initialized randomly, resulting in unstable accuracy and low convergence rate. In this paper, we present a community detection method based on modified PageRank and stochastic block model, which is able to compute the number of communities by finding community centers and initialize community assignments according to the centers and distance. Experiments on both synthetic and real-world networks prove that our method can intuitively give the number of communities, steadily get results of high NMI and modularity and efficiently speed up the convergence of optimizing likelihood probability.

Relation detection plays a crucial role in knowledge base question answering, and it is challenging because of the high variance of relation expression in real-world questions. Traditional relation detection models based on deep learning follow an encoding-comparing paradigm, where the question and the candidate relation are represented as vectors to compare their semantic similarity. Max- or average-pooling operation, which is used to compress the sequence of words into fixed-dimensional vectors, becomes the bottleneck of information flow. In this paper, we propose an attention-based word-level interaction model (ABWIM) to alleviate the information loss issue caused by aggregating the sequence into a fixed-dimensional vector before the comparison. First, attention mechanism is adopted to learn the soft alignments between words from the question and the relation. Then, fine-grained comparisons are performed on the aligned words. Finally, the comparison results are merged with a simple recurrent layer to estimate the semantic similarity. Besides, a dynamic sample selection strategy is proposed to accelerate the training procedure without decreasing the performance. Experimental results of relation detection on both SimpleQuestions and WebQuestions datasets show that ABWIM achieves the state-of-the-art accuracy, demonstrating its effectiveness.

In the above article [1], the authors’ units need to be changed because of the requirements of the school.