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

Semantic analysis is a crucial component of natural language processing (NLP) that concentrates on understanding the meaning, interpretation, and relationships between words, phrases, and sentences in a given context. It goes beyond merely analyzing a sentence’s syntax (structure and grammar) and delves into the intended meaning. In this survey, we outlined recent advances in clinical NLP for a multitude of languages with a focus on semantic analysis. Substantial progress has been made for key NLP sub-tasks that enable such analysis (i.e. methods for more efficient corpus construction and de-identification).

This trend continues today, with research into modern architectures and what formal languages they can learn (Weiss et al., 2018; Bernardy, 2018; Suzgun et al., 2019), or the formal properties they possess (Chen et al., 2018b). White-box attacks are difficult to adapt to the text world as they typically require computing gradients with respect to the input, which would be discrete in the text case. One option is to compute gradients with respect to the input word embeddings, and perturb the embeddings. Since this may result in a vector that does not correspond to any word, one could search for the closest word embedding in a given dictionary (Papernot et al., 2016b); Cheng et al. (2018) extended this idea to seq2seq models. Others computed gradients with respect to input word embeddings to identify and rank words to be modified (Samanta and Mehta, 2017; Liang et al., 2018).

This practice, known as “social listening,” involves gauging user satisfaction or dissatisfaction through social media channels. Semantic analysis aids in analyzing and understanding customer queries, helping to provide more accurate and efficient support. Semantic analysis aids search engines in comprehending user queries more effectively, consequently retrieving more relevant results by considering the meaning of words, phrases, and context. Semantic analysis employs various methods, but they all aim to comprehend the text’s meaning in a manner comparable to that of a human.

Semantic Analysis Techniques

Once these issues are addressed, semantic analysis can be used to extract concepts that contribute 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]. Therefore, when applying computational semantics, automatic processing of semantic meaning from texts, domain-specific methods and linguistic features for accurate parsing and information extraction should be considered. In the context of NLP, this question needs to be understood in light of earlier NLP work, often referred to as feature-rich or feature-engineered systems.

One can train machines to make near-accurate predictions by providing text samples as input to semantically-enhanced ML algorithms. Machine learning-based semantic analysis involves sub-tasks such as relationship extraction and word sense disambiguation. Semantic Analysis is a subfield of Natural Language Processing (NLP) that attempts to understand the meaning of Natural Language.

Word Senses

So, if we plotted these topics and these terms in a different table, where the rows are the terms, we would see scores plotted for each term according to which topic it most strongly belonged. IBM has launched a new open-source toolkit, PrimeQA, to spur progress in multilingual question-answering systems to make it easier for anyone to quickly find information on the web. Accelerate the business value of artificial intelligence with a powerful and flexible portfolio of libraries, services and applications.

Recent examples include clustering of sentence embeddings in an RNN encoder trained in a multitask learning scenario (Brunner et al., 2017), and phoneme clusters in a joint audio-visual RNN model (Alishahi et al., 2017). To summarize, natural language processing in combination with deep learning, is all about vectors that represent words, phrases, etc. and to some degree their meanings. By knowing the structure of sentences, we can start trying to understand the meaning of sentences. We start off with the meaning of words being vectors but we can also do this with whole phrases and sentences, where the meaning is also represented as vectors.

The NLP Problem Solved by Semantic Analysis

NeuroX (Dalvi et al., 2019b) is a tool for finding and analyzing individual neurons, focusing on machine translation. An instructive visualization technique is to cluster neural network activations and compare them to some linguistic property. Early work clustered RNN activations, showing that they organize in lexical categories (Elman, 1989, 1990).

Understanding Natural Language might seem a straightforward process to us as humans. However, due to the vast complexity and subjectivity involved in human language, interpreting it is quite a complicated task for machines. Semantic Analysis of Natural Language captures the meaning of the given text while taking into account context, logical structuring of sentences and grammar roles. An important aspect in improving patient care and healthcare processes is to better handle cases of adverse events (AE) and medication errors (ME). A study on Danish psychiatric hospital patient records [95] describes a rule- and dictionary-based approach to detect adverse drug effects (ADEs), resulting in 89% precision, and 75% recall. Another notable work reports an SVM and pattern matching study for detecting ADEs in Japanese discharge summaries [96].

We can any of the below two semantic analysis techniques depending on the type of information you would like to obtain from the given data. 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. This article assumes some understanding of basic NLP preprocessing and of word vectorisation (specifically tf-idf vectorisation). NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time.

Sentiment analysis is widely applied to reviews, surveys, documents and much more. Syntactic analysis, also referred to as syntax analysis or parsing, is the process of analyzing natural language with the rules of a formal grammar. Grammatical rules are applied to categories and groups of words, not individual words.

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It saves a lot of time for the users as they can simply click on one of the search queries provided by the engine and get the desired result. For example, ‘Raspberry Pi’ can refer to a fruit, a single-board computer, or even a company (UK-based foundation). Hence, it is critical to identify which meaning suits the word depending on its usage. To know the meaning of Orange in a sentence, we need to know the words around it. Semantic analysis allows for a deeper understanding of user preferences, enabling personalized recommendations in e-commerce, content curation, and more. The idea of entity extraction is to identify named entities in text, such as names of people, companies, places, etc.

To enable cross-lingual semantic analysis of clinical documentation, a first important step is to understand differences and similarities between clinical texts from different countries, written in different languages. Wu et al. [78], perform a qualitative and statistical comparison of discharge summaries from China and three different US-institutions. Chinese discharge summaries contained a slightly larger discussion of problems, but fewer treatment entities than the American semantic analysis nlp notes. Generalizability is a challenge when creating systems based on machine learning. In particular, systems trained and tested on the same document type often yield better performance, but document type information is not always readily available. 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.

Advantages of Syntactic Analysis

An alternative is that maybe all three numbers are actually quite low and we actually should have had four or more topics — we find out later that a lot of our articles were actually concerned with economics! By sticking to just three topics we’ve been denying ourselves the chance to get a more detailed and precise look at our data. Let’s say that there are articles strongly belonging to each category, some that are in two and some that belong to all 3 categories. We could plot a table where each row is a different document (a news article) and each column is a different topic.

For each syntactic pattern in a class, VerbNet defines a detailed semantic representation that traces the event participants from their initial states, through any changes and into their resulting states. We applied that model to VerbNet semantic representations, using a class’s semantic roles and a set of predicates defined across classes as components in each subevent. We will describe in detail the structure of these representations, the underlying theory that guides them, and the definition and use of the predicates.

• Finally, with the rise of the internet and of online marketing of non-traditional therapies, patients are looking to cheaper, alternative methods to more traditional medical therapies for disease management.
• To know the meaning of Orange in a sentence, we need to know the words around it.
• The methodology follows earlier work on evaluating the interpretability of probabilistic topic models with intrusion tasks (Chang et al., 2009).

Privacy protection regulations that aim to ensure confidentiality pertain to a different type of information that can, for instance, be the cause of discrimination (such as HIV status, drug or alcohol abuse) and is required to be redacted before data release. This type of information is inherently semantically complex, as semantic inference can reveal a lot about the redacted information (e.g. The patient suffers from XXX (AIDS) that was transmitted because of an unprotected sexual intercourse). This path of natural language processing focuses on identification of named entities such as persons, locations, organisations which are denoted by proper nouns. However, the challenge is to understand the entire context of a statement to categorise it properly.

Many of these classes had used unique predicates that applied to only one class. We attempted to replace these with combinations of predicates we had developed for other classes or to reuse these predicates in related classes we found. Other classes, such as Other Change of State-45.4, contain widely diverse member verbs (e.g., dry, gentrify, renew, whiten). A class’s semantic representations capture generalizations about the semantic behavior of the member verbs as a group.

Based on them, the classification model can learn to generalise the classification to words that have not previously occurred in the training set. Therefore, they need to be taught the correct interpretation of sentences depending on the context. As an example, for the sentence “The water forms a stream,”2, SemParse automatically generated the semantic representation in (27). In this case, SemParse has incorrectly identified the water as the Agent rather than the Material, but, crucially for our purposes, the Result is correctly identified as the stream. The fact that a Result argument changes from not being (¬be) to being (be) enables us to infer that at the end of this event, the result argument, i.e., “a stream,” has been created.