Toward Recognizing More Entity Types in NER : An Efficient Implementation using Only Entity Lexicons (2020)

Abstract: In this work, we explore the way to quickly adjust an existing named entity recognition (NER) system to make it capable of recognizing entity types not defined in the system. As an illustrative example, consider the case that a NER system has been built to recognize person and organization names, and now it requires to additionally recognize job titles. Such a situation is common in the industrial areas, where the entity types required to recognize vary a lot in different products and keep changing. To avoid laborious data labeling and achieve fast adaptation, we propose to adjust the existing NER system using the previously labeled data and entity lexicons of the newly introduced entity types. We formulate such a task as a partially supervised learning problem and accordingly propose an effective algorithm to solve the problem. Comprehensive experimental studies on several public NER datasets validate the effectiveness of our method.

## introduction named entity recognition (ner) is a type of information extraction task that seeks to identify entity names from unstructured text and categorize them into a predefined list of types. it plays an important role in many downstream tasks such as knowledge base construction @xcite , machine translation @xcite , and search @xcite , etc. in this field, the supervised methods, ranging from the conventional graph models @xcite @xcite to the dominant deep neural methods @xcite @xcite @xcite @xcite , have achieved great success. however, these supervised methods usually require large scale labeled data to achieve good performance, while the annotation of ner data is often laborious and time-consuming. in the real world, there are many, or more strictly speaking, infinite numbers of entity types. it is impossible for a ner system to cover all entity types @xcite . therefore, in the industrial area, it often happens that some entity types required to recognize by the clients are not defined in the previously designed ner system. in such a case, we need to quickly adjust the existing ner system to make it capable of recognizing the new entity types required by the clients. in this literature, we refer to the existing ner system as the source system, and refer to the adjusted system as the target system. the ner tasks defined in the two systems are referred to as the source task and the target task, respectively. the goal of this work is quickly transferring from the source task to the target task. suppose the new entity types defined in the target task are classified into class k (e.g, gpe and non-gpe are all annotated as the location type) in the source task. a common practice to build the target system is sampling some examples from the training data of the source task and asking the annotators to re-annotate words of class k in these examples. then, it finetunes the model pretrained on the source task (with the output layer being replaced) using the re-annotated data to perform the target task. however, it is worth noting that the ner labels of words are contextdependent. to re-annotate the words of class k, the annotators need to read the whole sentence rather than the fragmental words of class k. this is still laborious and time-consuming, making it not an ideal choice when fast adaptation is required or the required entity types by the clients vary a lot and keep changing. bobick works at google as a program support specialist. per u u org u u u u job u org per ps l l l l + + = per o o org o o job job job per o o org o o o o o predict input source label partial label partially supervised learning data labeling using the job title lexicon figure 1 : applying of our method to an illustrative sentence for additionally introducing job title in the target task. here, words in blue color constitute a job title. "partial label" corresponds to the automatically obtained partial labels using the job title lexicon, where "u" means the label of the word is unknown (can be "o" or "job"). "predict" denotes the expected labels predicted by our model. in some cases, we can even ask the clients of the target system to provide the lexicons, and usually, they are more willing to do so than annotate data. to perform the transfer task using the entity lexicons, we formulate the task as a partially supervised learning problem. figure 1 depicts the general process of our method, where the target task needs to additionally recognize job titles, which are annotated as the other (o) class in the source task. specifically, for the job title type, an entity lexicon of the type is collected. the lexicon is used to automatically re-annotate the training words of the other class in the source task ("o" of the "source label" in figure 1 ), obtaining some labeled data of the new entity type ("job" of the "partial label" in figure 1 ). the rest words of the other class of the source task not being annotated by the lexicons form the unlabeled data ("u" of the "partial label" in figure 1 ). note that, the unlabeled data contains both words of the new entity types and words not belonging to any entity type in the target task, and there is no labeled data for the other class in the target task. based on the obtained labeled and unlabeled data, a multi-class classifier is trained to perform the target task using a partially supervised (ps) learning algorithm. in this classifier, the constituted words of an entity mention correspond to the same class label without distinction of their positions in the mention, and words not belonging to any entity type are grouped into a single class ("o" of "predict" in figure 1 ). the contribution of this work is threefold: 1) we explore fast transferring from a source ner system (task) to a target ner system (task). this setting has a wide range of applications in the real world but has been rarely studied. 2) we propose to perform the task using only labeled data of the source task and entity lexicons of the newly introduced entity types, avoiding laborious and time-consuming data labeling. 3) we formulate the task as a partially supervised learning problem and accordingly propose an effective algorithm to address it. ## related work ner is a well studied natural language processing (nlp) task. once a time, many ner systems are knowledge-based @xcite @xcite . they do not require annotated training data but heavily rely on background knowledge (rules) and lexicon resources. they work well when the lexicon is exhaustive, but fail when the lexicon is incomplete. precision is generally high for these systems, but recall is often low due to incomplete lexicons. current state-of-the-art ner systems are mainly based on annotated data and machine learning approaches. the lexicons introduced in some of these systems are mainly for extracting some external features @xcite @xcite . this field has been previously dominated by the graph models like hidden markov models (hmm) @xcite , maximum entropy markov models (memm) @xcite , and conditional random field (crf) @xcite . starting with @xcite , neural network ner systems with minimal feature engineering have become popular. such models do not require exhausted feature engineering. various neural architectures have been proposed, like the bidirectional long short-term memory network (lstm) plus a crf layer @xcite , the convolutional neural network (cnn) plus a crf layer, the combination of lstm and cnn @xcite , and the bert based lstm+crf model @xcite . one of the most related works is @xcite . this compared work proposes to perform ner using entity lexicons and unlabeled data. for this purpose, a distinct binary classifier is trained for each entity type using the unbiased positiveunlabeled (pu) learning algorithm @xcite . at the inference time, the recognition results of the binary classifiers for different entity types are combined to make the final decision. the difference between the compared work and our work is that, in the compared work, the mention recognition for one entity type is performed independently to the other types through a binary classifier. consequently, it has to resolve the conflict between the recognition results of different binary classifiers for different entity types using a heuristic method at the inference time. while, in this work, the mention recognition for different entity types are performed simultaneously using a single model. this way, the recognition for different different entity types can enhance each other, and it can also avoid heuristically resolving the recognition conflict at the inference time. ## conclusion in this work, we address the task to introduce one or more new entity types to an existing ner system, for which a dataset has been previously labeled. to avoid laborious and time-consuming data labeling, we propose a partially supervised learning algorithm to perform the task using only the labeled data of the existing ner system and entity lexicons of the new entity types. experimental studies on four public ner datasets show that our method can achieve quite comparable performance with the fully supervised methods using some easily collected lexicons. this makes our method a good choice for fast entity type introduction.