Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis PMC

Having a semantic representation allows us to generalize away from the specific words and draw insights over the concepts to which they correspond. This makes it easier to store information in databases, which have a fixed structure. It also allows the reader or listener to connect what the language says with what they already know or believe. Powered by machine learning algorithms and natural language processing, semantic analysis systems can understand the context of natural language, detect emotions and sarcasm, and extract valuable information from unstructured data, achieving human-level accuracy.

The underlying NLP methods were mostly based on term mapping, but also included negation handling and context to filter out incorrect matches. The meaning representation can be used to reason for verifying what is correct in the world as well as to extract the knowledge with the help of semantic representation. As we discussed, the most important task of semantic analysis is to find the proper meaning of the sentence. This article is part of an ongoing blog series on Natural Language Processing (NLP).

Natural Language Processing for the Semantic Web

This approach minimized manual workload with significant improvements in inter-annotator agreement and F1 (89% F1 for assisted annotation compared to 85%). In contrast, a study by South et al. [14] applied cue-based dictionaries coupled with predictions from a de-identification system, BoB (Best-of-Breed), to pre-annotate protected health information (PHI) from synthetic clinical texts for annotator review. They found that annotators produce higher recall in less time when annotating without pre-annotation (from 66-92%). This chapter will consider how to capture the meanings that words and structures express, which is called semantics. A reason to do semantic processing is that people can use a variety of expressions to describe the same situation.

The development and maturity of NLP systems has also led to advancements in the employment of NLP methods in clinical research contexts. The advantages that graphs offer over logics are that the mapping of natural language sentences to graphs can be more direct and structure sharing can be used to make it clear when the interpretation of two expressions correspond to the same entity, which allows quantifiers to span multiple clauses. Graphs can also be more expressive, while preserving the sound inference of logic. One can distinguish the name of a concept or instance from the words that were used in an utterance. This book introduces core natural language processing (NLP) technologies to non-experts in an easily accessible way, as a series of building blocks that lead the user to understand key technologies, why they are required, and how to integrate them into Semantic Web applications.

Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing

The Conceptual Graph shown in Figure 5.18 shows how to capture a resolved ambiguity about the existence of “a sailor”, which might be in the real world, or possibly just one agent’s belief context. The graph and its CGIF equivalent express that it is in both Tom and Mary’s belief context, but not necessarily the real world. Procedural semantics are possible for very restricted domains, but quickly become cumbersome and hard to maintain.

Deleger et al. [32] showed that automated de-identification models perform at least as well as human annotators, and also scales well on millions of texts. This study was based on a large and diverse set of clinical notes, where CRF models together with post-processing rules performed best (93% recall, 96% precision). Moreover, they showed that the task of extracting medication names on de-identified data did not decrease performance compared with non-anonymized data. Additionally, the lack of resources developed for languages other than English has been a limitation in clinical semantic analysis in natural language processing NLP progress. Enter statistical NLP, which combines computer algorithms with machine learning and deep learning models to automatically extract, classify, and label elements of text and voice data and then assign a statistical likelihood to each possible meaning of those elements. Today, deep learning models and learning techniques based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) enable NLP systems that ‘learn’ as they work and extract ever more accurate meaning from huge volumes of raw, unstructured, and unlabeled text and voice data sets.

The accuracy of the summary depends on a machine’s ability to understand language data. For instance, an approach based on keywords, computational linguistics or statistical NLP (perhaps even pure machine learning) likely uses a matching or frequency technique with clues as to what a text is “about.” These methods can only go so far because they are not looking to understand the meaning. This is a key concern for NLP practitioners responsible for the ROI and accuracy of their NLP programs. You can proactively get ahead of NLP problems by improving machine language understanding. Natural language processing brings together linguistics and algorithmic models to analyze written and spoken human language. Based on the content, speaker sentiment and possible intentions, NLP generates an appropriate response.

• Now, we can understand that meaning representation shows how to put together the building blocks of semantic systems.
• Hence, under Compositional Semantics Analysis, we try to understand how combinations of individual words form the meaning of the text.
• Clinical NLP is the application of text processing approaches on documents written by healthcare professionals in clinical settings, such as notes and reports in health records.
• With the help of meaning representation, we can link linguistic elements to non-linguistic elements.

NLP can also analyze customer surveys and feedback, allowing teams to gather timely intel on how customers feel about a brand and steps they can take to improve customer sentiment. MonkeyLearn makes it simple for you to get started with automated semantic analysis tools. Using a low-code UI, you can create models to automatically analyze your text for semantics and perform techniques like sentiment and topic analysis, or keyword extraction, in just a few simple steps. A further level of semantic analysis is text summarization, where, in the clinical setting, information about a patient is gathered to produce a coherent summary of her clinical status. This is a challenging NLP problem that involves removing redundant information, correctly handling time information, accounting for missing data, and other complex issues.

Of course, there is a total lack of uniformity across implementations, as it depends on how the software application has been defined. Figure 5.6 shows two possible procedural semantics for the query, “Find all customers with last name of Smith.”, one as a database query in the Structured Query Language (SQL), and one implemented as a user-defined function in Python. These correspond to individuals or sets of individuals in the real world, that are specified using (possibly complex) quantifiers. There we can identify two named entities as “Michael Jordan”, a person and “Berkeley”, a location. There are real world categories for these entities, such as ‘Person’, ‘City’, ‘Organization’ and so on.

Figure 5.12 shows some example mappings used for compositional semantics and the lambda  reductions used to reach the final form. For sentences that are not specific to any domain, the most common approach to semantics is to focus on the verbs and how they are used to describe events, with some attention to the use of quantifiers (such as “a few”, “many” or “all”) to specify the entities that participate in those events. These models follow from work in linguistics (e.g. case grammars and theta roles) and philosophy (e.g., Montague Semantics[5] and Generalized Quantifiers[6]). Four types of information are identified to represent the meaning of individual sentences. Semantic analysis is the process of understanding the meaning and interpretation of words, signs and sentence structure.

An alternative is to express the rules as human-readable guidelines for annotation by people, have people create a corpus of annotated structures using an authoring tool, and then train classifiers to automatically select annotations for similar unlabeled data. The classifier approach can be used for either shallow representations or for subtasks of a deeper semantic analysis (such as identifying the type and boundaries of named entities or semantic roles) that can be combined to build up more complex semantic representations. The first step in a temporal reasoning system is to detect expressions that denote specific times of different types, such as dates and durations. A lexicon- and regular-expression based system (TTK/GUTIME [67]) developed for general NLP was adapted for the clinical domain.

In the above sentence, the speaker is talking either about Lord Ram or about a person whose name is Ram. It may be defined as the words having same spelling or same form but having different and unrelated meaning. For example, the word “Bat” is a homonymy word because bat can be an implement to hit a ball or bat is a nocturnal flying mammal also. Besides, Semantics Analysis is also widely employed to facilitate the processes of automated answering systems such as chatbots – that answer user queries without any human interventions. Likewise, the word ‘rock’ may mean ‘a stone‘ or ‘a genre of music‘ – hence, the accurate meaning of the word is highly dependent upon its context and usage in the text. Hence, under Compositional Semantics Analysis, we try to understand how combinations of individual words form the meaning of the text.

Semantic Analysis Method Development – Information Models and Resources

Logic does not have a way of expressing the difference between statements and questions so logical frameworks for natural language sometimes add extra logical operators to describe the pragmatic force indicated by the syntax – such as ask, tell, or request. Logical notions of conjunction and quantification are also not always a good fit for natural language. These rules are for a constituency–based grammar, however, a similar approach could be used for creating a semantic representation by traversing a dependency parse. Figure 5.9 shows dependency structures for two similar queries about the cities in Canada.

For example, if we talk about the same word “Bank”, we can write the meaning ‘a financial institution’ or ‘a river bank’. In that case it would be the example of homonym because the meanings are unrelated to each other. In the second part, the individual words will be combined to provide meaning in sentences.

For example, there exists many possible semantics for a word (polysemy) and the synonym of the word; and also these techniques avoid considering the stop words in English which are critical for English phrase/word division, speech investigation, and meaningful comprehension. Our proposed work utilizes Term Frequency-based Inverse Document Frequency model and Glove algorithm-based word embeddings vector for determining the semantic similarity among the terms in the textual contents. Lemmatizer is utilized to reduce the terms to the most possible smallest lemmas. The outcomes demonstrate that the proposed methodology is more prominent than the TF-idf score in ranking the terms with respect to the search query terms. The Pearson correlation coefficient achieved for the semantic similarity model is 0.875.

Two of the most important first steps to enable semantic analysis of a clinical use case are the creation of a corpus of relevant clinical texts, and the annotation of that corpus with the semantic information of interest. Identifying the appropriate corpus and defining a representative, expressive, unambiguous semantic representation (schema) is critical for addressing each clinical use case. For SQL, we must assume that a database has been defined such that we can select columns from a table (called Customers) for rows where the Last_Name column (or relation) has ‘Smith’ for its value. For the Python expression we need to have an object with a defined member function that allows the keyword argument “last_name”.

• There have also been huge advancements in machine translation through the rise of recurrent neural networks, about which I also wrote a blog post.
• Automatically classifying tickets using semantic analysis tools alleviates agents from repetitive tasks and allows them to focus on tasks that provide more value while improving the whole customer experience.
• While NLP and other forms of AI aren’t perfect, natural language processing can bring objectivity to data analysis, providing more accurate and consistent results.

The schema extends the 2006 schema with instructions for annotating fine-grained PHI classes (e.g., relative names), pseudo-PHI instances or clinical eponyms (e.g., Addison’s disease) as well as co-reference relations between PHI names (e.g., John Doe COREFERS to Mr. Doe). The reference standard is annotated for these pseudo-PHI entities and relations. To date, few other efforts have been made to develop and release new corpora for developing and evaluating de-identification applications. In simple words, we can say that lexical semantics represents the relationship between lexical items, the meaning of sentences, and the syntax of the sentence. Now, we have a brief idea of meaning representation that shows how to put together the building blocks of semantic systems. In other words, it shows how to put together entities, concepts, relations, and predicates to describe a situation.