WME : Sense, Polarity and Affinity based Concept Resource for Medical Events (2016)

Abstract: In order to overcome the lack of medical corpora, we have developed a WordNet for Medical Events (WME) for identifying medical terms and their sense related information using a seed list. The initial WME resource contains 1654 medical terms or concepts. In the present research, we have reported the enhancement of WME with 6415 number of medical concepts along with their conceptual features viz. Parts-of-Speech (POS), gloss, semantics, polarity, sense and affinity. Several polarity lexicons viz. SentiWordNet, SenticNet, Bing Liu’s subjectivity list and Taboda’s adjective list were introduced with WordNet synonyms and hyponyms for expansion. The semantics feature guided us to build a semantic co-reference relation based network between the related medical concepts. These features help to prepare a medical concept network for better sense relation based visualization. Finally, we evaluated with respect to Adaptive Lesk Algorithm and conducted an agreement analysis for validating the expanded WME resource.

## introduction in the domain of clinical text processing, sense-based information extraction is considered as a challenging task due to the unstructured nature of the corpus. the difficulty in preparing structured corpora from unstructured corpora in clinical domain is apparent due to the lack of involvement from the domain experts (e.g. medical practitioners) @xcite . several lexicons or systems were developed and used by researchers to overcome the above-mentioned difficulty in the conventional natural language processing (nlp) domain @xcite . in contrast, the researchers in the medical domain have introduced some resources e.g., medical wordnet to overcome such a problem @xcite . the wme resource was developed along with sense-based medical information for the use of experts like medical practitioners and non-experts like patients @xcite . in the present attempt, we have expanded the wme resource with new features like semantics and affinity. semantics feature helps to extract the relative sense-based words of the medical concepts from different knowledge bases and assign the medical words to their appropriate categories (e.g. treatment, disease, etc.). affinity feature helps to develop a medical concept network (conceptnet) for visualizing the concept relations @xcite . starting with an initial seed list of medical concepts, the synonyms and hyponyms of the concepts in the wordnet along with several polarity lexicons were extracted to enrich the present version of wme. the polarity lexicons in sentiwordnet foot_0 , senticnet foot_1 , bing liu's subjectivity list foot_2 and taboda's adjective list foot_3 were applied on the extracted synonyms and hyponyms so as to identify the proper sense of the retrieved medical concepts. section 2 provides an overview of our research. section 3 illustrates the related work associated to preparation of lexical resources in the clinical domain. section 4 discusses wme expansion techniques along with related tabulations needed for wme building. section 5 provides important features selection and identification approaches of wme. section 6 describes the process taken to evaluate the expanded wme resource along with agreement studies. finally section 7 gives a conclusion to our research and mentions the future plans of our study. ## overview sentiment-oriented information extraction system or lexicon preparation is treated as a contributory research in nlp due to lack of involvement from domain expert (e.g. medical practitioners). specifically in the domain of biomedical natural language processing, the extraction of medical concepts and their related sense, polarity and semantic feature is difficult due to the unstructured nature of the medical or clinical corpora. in this research, we aim to overcome these above-mentioned challenges by expanding the wordnet of medical event (wme) through including knowledge-based features. semantic and affinity features help us to prepare an affinity relationbased network, known as semantic and concept networks, for the wme. to enhance the wme resource, we have primarily concerned with the following subsections: ## related work in the context of biomedical corpora, the medical concepts (events) and their related information extraction can help to develop an annotation system, which is essential to build structured medical corpora (uzzaman and allen, 2010; @xcite . the polarity, sense and concept related features are crucial for preparing the structured corpus in this domain. several taxonomies were designed by researchers to allow non-experts (e.g. patients) to better understand medical concepts and their related information @xcite . in this concern, @xcite developed a medical information system by compiling a list of medical vocabulary and provide the context of the medical words as understood by experts and non-experts. fellbaum and smith developed medical wordnet (men) along with two sub networks namely, medical factnet (mfn) and medical beliefnet (mbn), which serves as a source of consumer health information that provides medical information explanation to patients (smith and @xcite . men were developed under the formal architecture of the princeton wordnet @xcite . mfn helps the non-expert group to extract and comprehend generic medical information, whereas the mbn identifies the fraction of the beliefs about medical phenomenon @xcite . their primary motivation was to develop a medical information retrieval system with visualization effects. the extraction of medical terms from the clinical corpus is an ambiguous task @xcite . therefore, a group of researchers have introduced sense selection and pruning strategies to expand the ontology of the medical domain @xcite . wordnet of medical event (wme) was introduced as a lexical resource to identify medical events and their related features viz. pos, gloss, polarity and sense from the corpora @xcite . the pos of the medical concepts signifies the lexical categories of the medical events, whereas their gloss, polarity and sense features help to provide the semantics and knowledge-based information relating to the medical events. ## wme1.0 building sense-based keyword extraction is essential for context sense identification (e.g. in the sentence "a supplementary component that improves capability", the keywords "improves" and "capability" keywords denote the positive sense of the sentence). it is a tedious job in biomedical natural language processing domain, and it is because knowledge-based meaning identification along with the pos, synonyms, hyponyms and definition of the words has to be extracted from the conventional wordnet. but such an approach is not adequate to provide appropriate knowledge and sense-based information needed for the medical concepts (terms). to identify the syntactic and semantic features of the medical concepts, we have developed wme1.0 that provides the pos, gloss and sense of the medical terms. the seed list of wme1.0 resource was prepared from the trial and training datasets of the semeval-2015 task-6 5 . in addition to the conventional wordnet, we have also used english medical dictionary to develop the initial wme resource. semeval 2015 task-6 datasets have extracted 2479 medical events along with their attributes such as type, span-context, sense (positive/negative) from the provided datasets (e.g., <tumor>, <event>, <an abnormal new mass of tissue that serves no purpose.>, <@xmath0. the pos, synonyms and definition of the seed list were then added from the wordnet (e.g., <abdomen>, <noun>, <1. abdomen 2. abdominal cavity>, <1. "the region of the body is vertebrate between the thorax and the pelvis." 2."the cavity containing the major viscera; in mammals it is separated from the thorax by the diaphragm.>). meanwhile, an english medical dictionary 6 identifies the pos and word related gloss of these medical concepts. from the above-mentioned dictionary, we have to perform manual editing for preprocessing on 11750 medical words in english along with their pos and gloss (e.g., <adenoma>, <noun>, <a benign tumor of a @xmath1. in order to identify the proper sense-based gloss of the seed list, we have used senticnet, sentiwordnet, bing liu subjective list and taboda's adjective list sentiment lexicons. after extracting various sense-based glosses from different resources, we chose the knowledgebased gloss that is most appropriate to the medical concepts by introducing @xcite sequential and combined word sense disambiguation (wsd) @xcite . ## wme2.0 building the inclusion of semantic and knowledgebased features is crucial for preparing an expanded version of the wme. the semantic, polarity, sense and affinity features have been introduced to identify and extract medical concepts (events) from the clinical corpora. in the following subsections, we have discussed in details the steps taken for features selection and their related statistical observations.