Named Entity Recognition for Cancer Immunology Research Using Distant Supervision (2022)

Abstract: Cancer immunology research involves several important cell and protein factors. Extracting the information of such cells and proteins and the interactions between them from text are crucial in text mining for cancer immunology research. However, there are few available datasets for these entities, and the amount of annotated documents is not sufficient compared with other major named entity types. In this work, we introduce our automatically annotated dataset of key named entities, i.e., T-cells, cytokines, and transcription factors, which engages the recent cancer immunotherapy. The entities are annotated based on the UniProtKB knowledge base using dictionary matching. We build a neural named entity recognition (NER) model to be trained on this dataset and evaluate it on a manually-annotated data. Experimental results show that we can achieve a promising NER performance even though our data is automatically annotated. Our dataset also enhances the NER performance when combined with existing data, especially gaining improvement in yet investigated named entities such as cytokines and transcription factors.

## introduction cancer immunology research has a central focus on t lymphocytes (t-cells), which engage the immune system in fighting against cancer @xcite @xcite . the development of t-cells can be guided by cytokines and transcription factors @xcite . transcription factors (tf) are nuclear proteins that bind specific gene sequences and involved in decision-making processes during t-cell differentiation @xcite . meanwhile, cytokines are signaling molecules secreted and sensed by immune and other cell types @xcite . extracting t-cell, cytokine, and tf entities and the interactions between them can be crucial for text mining in cancer immunology research. however, there are few existing datasets containing these entities to train text mining models. at the core of text mining tasks, the named entity recognition (ner) task also lacks such datasets for training ner models to detect these named entities, which may limit the development of text mining systems in this cancer immunology research field. there is an existing t-cell related named entity dataset called tcre @xcite , but the amount of annotated data is also limited to only 89 documents. several knowledge bases related to immune system have been proposed such as immunexpresso @xcite and @xcite , which contain cell type and cytokine information, but they lack utilizing and evaluating with modern ner models on these named entities. in this paper, as a step to fill these gaps and promote the development of text mining systems on these named entities in cancer immunology research articles, we present our automatically annotated dataset containing named entities of t-cell, cytokine and tf, which are important for mining and understanding cancer immunology research articles. the entities in the dataset are automatically annotated using dictionary matching based on the uniprotkb (uniprot-consortium, 2021), a knowledgebase of protein sequences with functional information. 1 from the annotations of cytokine and tf entries in uniprotkb, a dictionary is constructed to annotate cytokine and tf named entities in their referenced pubmed articles. additionally, we utilized the existing jnlpba corpus, which contains manually annotated protein named entities, to annotate cytokine and tf entities. we build a ner model based on the span-based model with pre-trained bert. we trained the ner model on our automatically annotated dataset and evaluated the model on an existing manually annotated t-cell related named entity tcre dataset (czech item cytokine tf # uniprotkb entries 1,001 3,418 # dictionary size 6,859 20,055 # collected articles 585 1,903 ## approach we present our datasets containing three named entity types: cell_type, cytokine, and transcription factor (tf). the datasets are automatically annotated using dictionary matching with the entries in the uniprotkb in two different ways. ## ner model we explain the ner model to be trained on the annotated datasets. we build a neural-based ner model using a span-based method @xcite and finetuned pre-trained bert @xcite . specifically, each sentence is split into sub-word sequences, which are passed through the bert layer for contextual representations. then, for each span (i.e., a sequence of continuous words in a sentence), its representation is calculated by concatenating the representations of the first, last, and averaged sub-words of the span, which follows (sohrab and miwa, 2018a; @xcite . finally, each span representation is passed to classifiers to predict named entity types for each span. ## related work distant supervision methods for ner have been investigated in several previous works. @xcite revised the lstm-crf ner model @xcite and utilized the mesh database for chemical and disease entities. some methods are proposed to reduce noisy annotations for chinese ner @xcite , or general domain ontonotes @xcite . the span-based method has been used to build our ner model in this work. the method was proposed and employed in previous work @xcite @xcite , which have shown the advantages in extracting nested or continuous text sequences and successful in many sequence labeling tasks such as ner or coreference resolution. immunotherapy has achieved remarkable advances in recent years and can be important cancer treatment in future @xcite @xcite . however, there are few related work or annotated datasets in text mining on this domain. immunexpresso @xcite is a text mining engine related to mammalian immune system, and ner is evaluated on cells and cytokine using dictionary matching. @xcite is a knowledgebase containing concepts of t-cell and other types of drugs, diseases, genes, etc in pubmed documents. however, it lacks utilizing novel text mining methods in the creation and evaluation the extracted data including ner tasks. for the datasets used in our work, tcre is manually annotated by @xcite containing cell_type, cytokine, and tf entities, which are closed to our goal, and we used for our evaluation. a limitation of the tcre is that it contains only 89 documents, which is insufficient to train powerful ner models. therefore, our annotation method in this work can advance the task in extracting t-cell named entities. jnlpba @xcite ) contains manually annotated cell_type and protein entities. meanwhile, unipro-tkb (uniprot-consortium, 2021) is a large and useful knowledgebase containing protein sequences annotated by experts with corresponding pubmed references. the uniprotkb and jnlpba are leveraged to build our corpus. ## conclusion we introduce our automatically annotated dataset for ner containing cell_type, cytokine, and tf entities, which are important in cancer immunology research, using a distant supervision method. the dataset is automatically annotated based on the entries in the uniprotkb knowledge base. we built a dictionary of the protein and gene names of cytokines and tf from the uniprotkb annotations. we then collected referenced pubmed articles and annotated these names in the texts using text matching with the dictionary entries. additionally, we utilized the large manually annotated jnlpba dataset, which contains cell_type and protein named entities to build our dataset. we trained ner models on our automatically annotated dataset and evaluated them on a manually annotated t-cell corpus. the results show that our automatically annotated dataset helps to improve the ner performance by extracting more named entities of cytokines and tf accurately. for future work, we plan to improve and extend our dataset to extract interactions or events related to these entities for text mining in cancer immunology research.