A convolution BiLSTM neural network model for Chinese event extraction

Chinese event extraction is a challenging task in information extraction. Previous approaches highly depend on sophisticated feature engineering and complicated natural language processing (NLP) tools. In this paper, we first come up with the language specific issue in Chinese event extraction, and then propose a convolution bidirectional LSTM neural network that combines LSTM and CNN to capture both sentence-level and lexical information without any hand-craft features. Experiments on ACE 2005 dataset show that our approaches can achieve competitive performances in both trigger labeling and argument role labeling

An Evaluation Methodology of Named Entities Recognition in Spanish Language: ECU 911 Case Study

The importance of the gathered information in Integrated Security Services as ECU911 in Ecuador is evidenced in terms of its quality and availability in order to perform decision-making tasks. It is a priority to avoid the loss of relevant information such as event address, places references, names, etc. In this context it is present Named Entity Recognition (NER) analysis for discovering information into informal texts. Unlike structured corpus and labeled for NER analysis like CONLL2002 or ANCORA, informal texts generated from emergency call dialogues have a very wide linguistic variety; in addition, there is a strong tending to lose important information in their processing. A relevant aspect to considerate is the identification of texts that denotes entities such as the physical address where emergency events occurred. This study aims to extract the locations in which an emergency event has been issued. A set of experiments was performed with NER models based on Convolutional Neural Network (CNN). The performance of models was evaluated according to parameters such as training dataset size, dropout rate, location dictionary, and denoting location. An experimentation methodology was proposed, with it follows the next steps: i) Data preprocessing, ii) Dataset labeling, iii) Model structuring, and iv) Model evaluating. Results revealed that the performance of a model improves when having more training data, an adequate dropout rate to control overfitting problems, and a combination of a dictionary of locations and replacing words denoting entities

Label Enhanced Event Detection with Heterogeneous Graph Attention Networks

Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Different from English ED, Chinese ED suffers from the problem of word-trigger mismatch due to the uncertain word boundaries. Existing approaches injecting word information into character-level models have achieved promising progress to alleviate this problem, but they are limited by two issues. First, the interaction between characters and lexicon words is not fully exploited. Second, they ignore the semantic information provided by event labels. We thus propose a novel architecture named Label enhanced Heterogeneous Graph Attention Networks (L-HGAT). Specifically, we transform each sentence into a graph, where character nodes and word nodes are connected with different types of edges, so that the interaction between words and characters is fully reserved. A heterogeneous graph attention networks is then introduced to propagate relational message and enrich information interaction. Furthermore, we convert each label into a trigger-prototype-based embedding, and design a margin loss to guide the model distinguish confusing event labels. Experiments on two benchmark datasets show that our model achieves significant improvement over a range of competitive baseline methods

A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification

Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM) for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs) is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM) neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM), and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall