# Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning
## 2 Literature Review
### Evolution of NER  
With years of training in natural languages, humans can easily and intuitively distinguish named entities; the same is not valid for machines [10]. Earlier NER techniques were mostly rule-based approaches. By collecting relevant data extensively to build an extensive dictionary database, rule-based approaches are solutions with relatively few technical barriers. In our previous work, we attempted to adopt a rule-based method and found that only named entities covered by the dictionary extracted successfully. Although the performance of rule-based approaches could improve by extending the grammatical rules, they still lack robustness and portability [10]. Whenever a new corpus appears, the rules need to be modified to maintain the best performance; this requirement increases the maintenance cost accordingly [11]. In practice, dialogues come in various combinations, and new named entities emerge over time. Marking these named entities is not realistic manually. In other words, building a time-invariant dictionary that contains all named entities is not feasible [10]. As a solution to the shortcomings of purely rule-based solutions, association rule mining developed. Association rule mining establishes recognition rules based on the attributes of target entities and the characteristics of neighbors [12]. However, this method still performs poorly outside the rules.  
With the advancement of technology and the improvement of computation, recognizing named entities is now possible with machine learning. Machine learning-based NER transforms a given problem into classification tasks. When we use statistical models and algorithms, machine learning-based NER methods attempt to find patterns and relationships in sentences and identify the named entities [10]. Such techniques have adopted to address some problems in disaster management [13]. They have also aided decision-makers in retrieving static documents and real-time information by integrating semantic and temporal types of terms into NER tasks to achieve improved questions analysis performance. However, existing studies that used such techniques focused on information retrieval in limited target documents; such a process is different from damage information gathering and analysis, which involves data collection and analysis. Besides, these studies did not maximize the NER techniques. Named entities that did not cover in training data can still recognize according to sentence patterns and contexts by adopting advanced machine learning techniques into NER problems. By maintaining a memory based on historical information, recurrent neural network (RNN) models can predict current output on long-distance features. For sequence tagging tasks, long short-term memory (LSTM) models heavily used. The network of the LSTM resembles the framework of RNNs. The crucial difference is that in capturing long-range dependencies, the LSTM substitutes the purpose-built memory cells with the hidden layer updates in the RNN. Also, the LSTM can easily access past and future input features for a specific time during sequence tagging [14]. Through the use of bidirectional LSTM (Bi-LSTM) networks and the forward and backward statesfor two-way passing, the omissions caused by relying on only one direction resolved. Meanwhile, the conditional random field (CRF) focuses on the sentence level instead of individual positions and is capable of producing high tagging accuracy in general. Relative to other algorithms, Bi-LSTM-CRF limits its dependency on word embedding [14]. When introducing Bi-LSTM-CRF to other models, word embeddings need not be organized further, and specific training data can import directly.