A Survey of Semantic Analysis Approaches SpringerLink

This study also highlights the future prospects of semantic analysis domain and finally the study is concluded with the result section where areas of improvement are highlighted and the recommendations are made for the future research. This study also highlights the weakness and the limitations of the study in the discussion (Sect. 4) and results (Sect. 5). Once these issues are addressed, semantic analysis can be used to extract concepts that contribute semantic analysis in natural language processing to our understanding of patient longitudinal care. For example, lexical and conceptual semantics can be applied to encode morphological aspects of words and syntactic aspects of phrases to represent the meaning of words in texts. However, clinical texts can be laden with medical jargon and can be composed with telegraphic constructions. Furthermore, sublanguages can exist within each of the various clinical sub-domains and note types [1-3].

For instance, Raghavan et al. [71] created a model to distinguish time-bins based on the relative temporal distance of a medical event from an admission date (way before admission, before admission, on admission, after admission, after discharge). The model was evaluated on a corpus of a variety of note types from Methicillin-Resistant S. Aureus (MRSA) cases, resulting in 89% precision and 79% recall using CRF and gold standard features. Other efforts systematically analyzed what resources, texts, and pre-processing are needed for corpus creation. Jucket [19] proposed a generalizable method using probability weighting to determine how many texts are needed to create a reference standard. The method was evaluated on a corpus of dictation letters from the Michigan Pain Consultant clinics.

Semantic Extraction Models

Chinese discharge summaries contained a slightly larger discussion of problems, but fewer treatment entities than the American notes. Morphological and syntactic preprocessing can be a useful step for subsequent semantic analysis. For example, prefixes in English can signify the negation of a concept, e.g., afebrile means without fever. Furthermore, a concept’s meaning can depend on its part of speech (POS), e.g., discharge as a noun can mean fluid from a wound; whereas a verb can mean to permit someone to vacate a care facility. Many of the most recent efforts in this area have addressed adaptability and portability of standards, applications, and approaches from the general domain to the clinical domain or from one language to another language.

One de-identification application that integrates both machine learning (Support Vector Machines (SVM), and Conditional Random Fields (CRF)) and lexical pattern matching (lexical variant generation and regular expressions) is BoB (Best-of-Breed) [25-26]. BoB applies the highest performing approaches from known de-identification systems for each PHI type, resulting in balanced recall and precision results (89%) for a configuration of individual classifiers, and best precision (95%) was obtained with a multi-class configuration. This system was also evaluated to understand the utility of texts by quantifying clinical information loss following PHI tagging i.e., medical concepts from the 2010 i2b2 Challenge corpus, in which less than 2% of the corpus concepts partially overlapped with the system [27]. The processing methods for mapping raw text to a target representation will depend on the overall processing framework and the target representations. A basic approach is to write machine-readable rules that specify all the intended mappings explicitly and then create an algorithm for performing the mappings.

Word Vectors

In particular, systems trained and tested on the same document type often yield better performance, but document type information is not always readily available. It is the first part of semantic analysis, in which we study the meaning of individual words. It involves words, sub-words, affixes (sub-units), compound words, and phrases also. We can do semantic analysis automatically works with the help of machine learning algorithms by feeding semantically enhanced machine learning algorithms with samples of text data, we can train machines to make accurate predictions based on their past results. It is the first part of the semantic analysis in which the study of the meaning of individual words is performed.

Then it starts to generate words in another language that entail the same information. Insurance companies can assess claims with natural language processing since this technology can handle both structured and unstructured data. NLP can also be trained to pick out unusual information, allowing teams to spot fraudulent claims. If you’re interested in using some of these techniques with Python, take a look at the Jupyter Notebook about Python’s natural language toolkit (NLTK) that I created. You can also check out my blog post about building neural networks with Keras where I train a neural network to perform sentiment analysis.

According to Chris Manning, a machine learning professor at Stanford, it is a discrete, symbolic, categorical signaling system. This means we can convey the same meaning in different ways (i.e., speech, gesture, signs, etc.) The encoding by the human brain is a continuous pattern of activation by which the symbols are transmitted via continuous signals of sound and vision. Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai™, a next generation enterprise studio for AI builders. Automated semantic analysis works with the help of machine learning algorithms. NLP has also been used for mining clinical documentation for cancer-related studies. A statistical parser originally developed for German was applied on Finnish nursing notes [38].