Semantic Analysis Guide to Master Natural Language Processing Part 9

The Python programing language provides a wide range of tools and libraries for attacking specific NLP tasks. Many of these are found in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs. In clinical practice, there is a growing curiosity and demand for NLP applications.

Gundlapalli et al. [20] assessed the usefulness of pre-processing by applying v3NLP, a UIMA-AS-based framework, on the entire Veterans Affairs (VA) data repository, to reduce the review of texts containing social determinants of health, with a focus on homelessness. Specifically, they studied which note titles had the highest yield (‘hit rate’) for extracting psychosocial concepts per document, and of those, which resulted in high precision. This approach resulted in an overall precision for all concept categories of 80% on a high-yield set of note titles. They conclude that it is not necessary to involve an entire document corpus for phenotyping using NLP, and that semantic attributes such as negation and context are the main source of false positives.

Design of Computer Intelligent Translation System Based on Natural Language Processing

Figure 5.15 includes examples of DL expressions for some complex concept definitions. This path of natural language processing focuses on identification of named entities such as persons, locations, organisations which are denoted by proper nouns. This degree of language understanding can help companies automate even the most complex language-intensive processes and, in doing so, transform the way they do business. So the question is, why settle for an educated guess when you can rely on actual knowledge? In machine translation done by deep learning algorithms, language is translated by starting with a sentence and generating vector representations that represent it.

The organization of shared tasks, or community challenges, has also been an influential part of the recent advancements in clinical NLP not only in corpus creation and release, annotation guideline development and schema modeling, but also in defining semantically-related tasks. Furthermore, NLP method development has been enabled by the release of these corpora, producing state-of-the-art results [17]. Several standards and corpora that exist in the general domain, e.g. the Brown Corpus and Penn Treebank tag sets for POS-tagging, have been adapted for the clinical domain. Fan et al. [34] adapted the Penn Treebank II guidelines [35] for annotating clinical sentences from the 2010 i2B2/VA challenge notes with high inter-annotator agreement (93% F1). This adaptation resulted in the discovery of clinical-specific linguistic features.

The clinical NLP community is actively benchmarking new approaches and applications using these shared corpora. In real-world clinical use cases, rich semantic and temporal modeling may prove useful for generating patient timelines and medical record visualizations, but may not always be worth the computational runtime and complexity to support knowledge discovery efforts from a large-scale clinical repository. For some real-world clinical use cases on higher-level tasks such as medical diagnosing and medication error detection, deep semantic analysis is not always necessary – instead, statistical language models based on word frequency information have proven successful. There still remains a gap between the development of complex NLP resources and the utility of these tools and applications in clinical settings. Utility of clinical texts can be affected when clinical eponyms such as disease names, treatments, and tests are spuriously redacted, thus reducing the sensitivity of semantic queries for a given use case. For example, if mentions of Huntington’s disease are spuriously redacted from a corpus to understand treatment efficacy in Huntington’s patients, knowledge may not be gained because disease/treatment concepts and their causal relationships are not extracted accurately.

Natural Language Processing Techniques for Understanding Text

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. And if we want to know the relationship of or between sentences, we train a neural network to make those decisions for us.

Named entity recognition (NER) concentrates on determining which items in a text (i.e. the “named entities”) can be located and classified into predefined categories. These categories can range from the names of persons, organizations and locations to monetary values and percentages. Noun phrases are one or more words that contain a noun and maybe some descriptors, verbs or adverbs. Furthermore, with growing internet and social media use, social networking sites such as Facebook and Twitter have become a new medium for individuals to report their health status among family and friends. These sites provide an unprecedented opportunity to monitor population-level health and well-being, e.g., detecting infectious disease outbreaks, monitoring depressive mood and suicide in high-risk populations, etc. Additionally, blog data is becoming an important tool for helping patients and their families cope and understand life-changing illness.

What is natural language processing used for?

Most studies on temporal relation classification focus on relations within one document. Cross-narrative temporal event ordering was addressed in a recent study with promising results by employing a finite state transducer approach [73]. The idea of entity extraction is to identify named entities in text, such as names of people, companies, places, etc. 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. With the help of meaning representation, we can represent unambiguously, canonical forms at the lexical level. With the help of meaning representation, unambiguous, canonical forms can be represented at the lexical level.

Experiencer and temporality attributes were also studied as a classification task on a corpus of History and Physical Examination reports, where the ConText algorithm was compared to three machine learning (ML) algorithms (Naive Bayes, k-Nearest Neighbours and Random Forest). There were no statistically significant differences in results for classifying experiencer between these approaches, but the ML approach (specifically, Random Forest) outperformed ConText on classifying temporality (historical or recent), resulting in 87% F1 compared to 69% [56]. An ensemble machine learning approach leveraging MetaMap and word embeddings from unlabeled data for disorder identification, a vector space model for disorder normalization, and SVM approaches for modifier classification achieved the highest performance (combined F1 and weighted accuracy of 81%) [50]. For accurate information extraction, contextual analysis is also crucial, particularly for including or excluding patient cases from semantic queries, e.g., including only patients with a family history of breast cancer for further study. Contextual modifiers include distinguishing asserted concepts (patient suffered a heart attack) from negated (not a heart attack) or speculative (possibly a heart attack). Other contextual aspects are equally important, such as severity (mild vs severe heart attack) or subject (patient or relative).

Clinical Utility – Applying NLP Applications to Clinical Use Cases

Natural language processing and Semantic Web technologies have different, but complementary roles in data management. Combining these two technologies enables structured and unstructured data to merge seamlessly. Speech recognition, for example, has gotten very good and works almost flawlessly, but we still lack this kind of proficiency in natural language understanding. Your phone basically understands what you have said, but often can’t do anything with it because it doesn’t understand the meaning behind it. Also, some of the technologies out there only make you think they understand the meaning of a text. An approach based on keywords or statistics or even pure machine learning may be using a matching or frequency technique for clues as to what the text is “about.” But, because they don’t understand the deeper relationships within the text, these methods are limited.

You can find out what a group of clustered words mean by doing principal component analysis (PCA) or dimensionality reduction with T-SNE, but this can sometimes be misleading because they oversimplify and leave a lot of information on the side. It’s a good way to get started (like logistic or linear regression in data science), but it isn’t cutting edge and it is possible to do it way better. Now, imagine all the English words in the vocabulary with all their different fixations at the end of them. To store them all would require a huge database containing many words that actually have the same meaning.

Syntactic analysis (syntax) and semantic analysis (semantic) are the two primary techniques that lead to the understanding of natural language. Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. In semantic analysis with machine learning, computers use word sense disambiguation to determine which meaning is correct in the given context. But before getting into the concept and approaches related to meaning representation, we need to understand the building blocks of semantic system. Another logical language that captures many aspects of frames is CycL, the language used in the Cyc ontology and knowledge base. While early versions of CycL were described as being a frame language, more recent versions are described as a logic that supports frame-like structures and inferences.

• Both polysemy and homonymy words have the same syntax or spelling but the main difference between them is that in polysemy, the meanings of the words are related but in homonymy, the meanings of the words are not related.
• One can distinguish the name of a concept or instance from the words that were used in an utterance.
• In the example shown in the below image, you can see that different words or phrases are used to refer the same entity.

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. In finance, NLP can be paired with machine learning to generate financial reports based on invoices, statements and other documents. Financial analysts can also employ natural language processing to predict stock market trends by analyzing news articles, social media posts and other online sources for market sentiments. 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.

To fully represent meaning from texts, several additional layers of information can be useful. Such layers can be complex and comprehensive, or focused on specific semantic problems. A challenging issue related to concept detection and classification is coreference resolution, e.g. correctly identifying that it refers to heart attack in the example “She suffered from a heart attack two years ago. It was severe.” NLP approaches applied on the 2011 i2b2 challenge corpus included using external knowledge sources and document structure features to augment machine learning or rule-based approaches [57]. For instance, the MCORES system employs a rich feature set with a decision tree algorithm, outperforming unweighted average F1 results compared to existing open-domain systems on the semantic types Test (84%), Persons (84%), Problems (85%) and Treatments (89%) [58].

I say this partly because semantic analysis is one of the toughest parts of natural language processing and it’s not fully solved yet. Understanding human language is considered a difficult task due to its complexity. For example, there are an infinite number of different ways to arrange words in a sentence. Also, words can have several meanings and contextual information is necessary to correctly interpret sentences. Just take a look at the following newspaper headline “The Pope’s baby steps on gays.” This sentence clearly has two very different interpretations, which is a pretty good example of the challenges in natural language processing. It allows computers to understand and interpret sentences, paragraphs, or whole documents, by analyzing their grammatical structure, and identifying relationships between individual words in a particular context.

Pivovarov and Elhadad present a thorough review of recent advances in this area [79]. This dataset is unique in its integration of existing semantic models from both the general and clinical NLP communities. Several types of textual or linguistic information layers and processing – morphological, syntactic, and semantic – can support semantic analysis. In this paper, we review the state of the art of clinical NLP to support semantic analysis for the genre of clinical texts. Description logics separate the knowledge one wants to represent from the implementation of underlying inference.

But before deep dive into the concept and approaches related to meaning representation, firstly we have to understand the building blocks of the semantic system. Now, we can understand that meaning semantic analysis in natural language processing representation shows how to put together the building blocks of semantic systems. In other words, it shows how to put together entities, concepts, relation and predicates to describe a situation.

In other words, we can say that lexical semantics is the relationship between lexical items, meaning of sentences and syntax of sentence. Determining the similarity among the sentences is a predominant task in natural language processing. The semantic determining task is one of the important research area in today’s applications related to text analytics.