Knowledge Injection for Disease Names in Logical Inference between J apanese Clinical Texts (2023)

Abstract: In the medical field, there are many clinical texts such as electronic medical records, and research on Japanese natural language processing using these texts has been conducted. One such research involves Recognizing Textual Entailment (RTE) in clinical texts using a semantic analysis and logical inference system, ccg2lambda. However, it is difficult for existing inference systems to correctly determine the entailment relations , if the input sentence contains medical domain specific paraphrases such as disease names. In this study, we propose a method to supplement the equivalence relations of disease names as axioms by identifying candidates for paraphrases that lack in theorem proving. Candidates of paraphrases are identified by using a model for the NER task for disease names and a disease name dictionary. We also construct an inference test set that requires knowledge injection of disease names and evaluate our inference system. Experiments showed that our inference system was able to correctly infer for 106 out of 149 inference test sets.

## introduction in the medical field, there are many electronic texts, such as image detections and electronic medical records, and using such texts becomes more active in research on natural language processing (nlp) in japanese @xcite . however, many of these studies utilize machine learning approaches, and it is argued that the machine learning approaches have problems in dealing with challenging linguistic phenomena such as negation and quantification. logic-based approaches have been proposed to perform systematic inferences involving these challenging linguistic phenomena, and one task of these inferences can be referred to as recognizing textual entailment (rte). rte is the task of determining whether a hypothesis sentence h can be inferred from a premise sentence t. for example, the following example illustrates a case where t entails h. ## system overview the overview of our proposed system is shown in figure 1 . we provide our inference system by extending ishida et al. (2022)'s inference system. the system consists of syntactic analysis, compound word analysis, semantic analysis, and theorem proving. in this study, we add a knowledge injection module in the medical domain to the previous system. in the knowledge injection module, we first apply an ner model based on a pretrained language model to extract disease names from the input sentence. for the extracted disease names, we perform an exact match search for the surface form column of disease names in j-medic. if the disease name and the surface form match, we inject the knowledge about the disease name in j-medic as an axiom to the automated theorem prover coq @xcite . since the additional axioms vary depending on the semantic relations between morphemes, we check the semantic tags of compound words assigned by the compound word semantic tagger, derive the axiom using the semantic tag and the knowledge of j-medic, and then inject the knowledge. we describe the details to provide an ner model in section 4 and subsection 6.1 and the details of axiom injection in section 5. ## building an ner dataset for disease names to train the model for the ner task for disease names in our inference system, we constructed a dataset newly for ner using clinical texts. we use a corpus of case reports, j-medstd-cr foot_2 , which was extracted through ocr from case report papers in pdf format that are openly accessible on j-stage. in this study, we manually annotated the appearances of disease names from j-medic in the 2,626 sentences of the j-medstd-cr corpus. tag type example en entity dvt (deep vein thrombosis) m_en modifying words ryuukisei-byouhen elevated-lesion 隆起性 病変 (elevated lesions) pa body part nou-kousoku brain-infarction 脳 梗塞 (brain infarction) ga nominative kyousui-tyoryuu pleural effusion-retention 胸水 貯留 (pleural effusion) wo accusative kotuzui-yokusei bone marrow-suppression 骨髄 抑制 (myelosuppression) ni dative kotu-ten'i bone-metastasis 骨 転移 (bone metastasis) ev event kyousui-tyoryuu pleural effusion-suppression 胸水 貯留 (pleural effusion) ## axiom injection in the proposed method, after identifying candidate paraphrases of the disease names, the knowledge of paraphrases is injected as axioms. in axiom injection, it is necessary to generate the knowledge about paraphrases of disease names as axioms according to the semantic representations in ccg2lambda. for example, when the premise sentence is (1) and the hypothesis sentence is (2), the underlined disease name's semantic tag in (1) is "en" because "pe" is one word and entity. the semantic representation in this case is (1a). semantic tags for the disease name underlined in (2) are "pa en" because "pulmonary" in " pulmenary embolism" is a body part and "embolism" is an entity. the semantic representation is (2a). partof(e 1 , e 2 ) indicates hai(e 2 ) is a body part for sokusensyo(e 1 ). as in (1a) and (2a), semantic representations differ depending on the semantic relations between morphemes within the compound words. in this study, we realize axiom injection by defining axioms generated through the combination of semantic tag assigned to both the surface form and standard disease names. combination number example en 276 syuryuu 腫瘤 (neoplasm) m_en en 145 ryuukisei-byouhen elevated-lesion 隆起性 病変 (elevated lesions) pa en 45 nou-kousoku brain-infarction (brain infarction) 脳 梗塞 m_en m_en en 22 genpatusei-tanzyuseiprimary-biliarykankouhen liver cirrhosis 原発性 胆汁性 肝硬変 (primary biliary cholangitis) m_en pa en 22 ten'isei-kan-syuyou metastatic-liver-neoplasm 転移性 肝 腫瘍 (metastatic liver tumor) others 141 kyousui-choryuu pleural effusion-retention 転移性 肝 腫瘍 ga ev (metastatic liver tumor) (2) hai-sokusensyo ga zouaku-si-teita pulmonary-embolism nom worsen-euph-pst ## conclusion in this study, to flexibly perform inference involving knowledge for disease names, we extended the previous semantic analysis and logical inference system in the medical domain @xcite . specifically, we developed the knowledge injection module for the logical inference system, performing ner for disease names, searching for relevant knowledge using j-medic, and adding the resulting axioms to the theorem prover. we also constructed a dataset for the ner task of disease names and an inference test set that requires knowledge injection of disease names. we evaluated our inference system using the constructed test set and as a result, we were able to perform correct inference for 106 out of 149 inference test cases. the future challenges are to expand the ner dataset and the inference test set, and to improve the knowledge injection module to further enhance the performance of the inference system. furthermore, a comparison will be performed between the neural models trained on the expanded medical inference test set and the proposed method.