Semantic Analysis in Natural Language Processing by Hemal Kithulagoda Voice Tech Podcast

As discussed in previous articles, NLP cannot decipher ambiguous words, which are words that can have more than one meaning in different contexts. Semantic analysis is key to contextualization that helps disambiguate language data so text-based NLP applications can be more accurate. Natural Language Processing APIs allow developers to integrate human-to-machine communications and complete several useful tasks such as speech recognition, chatbots, spelling correction, sentiment analysis, etc. Similarly, the European Commission emphasizes the importance of eHealth innovations for improved healthcare in its Action Plan [106].

The clinical NLP community is actively benchmarking new and applications using these shared corpora. 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.

How is Semantic Analysis different from Lexical Analysis?

In fact, the data available in the real world in textual format are quite noisy and contain several issues. This makes the analysis of texts much more complicated than analyzing the structured tabular data. This tutorial will try to focus on one of the many methods available to tame textual data.

In that sense, SVD is free from any normality assumption of data (covariance calculation assumes a normal distribution of data). The U matrix is the document-aspect matrix, V is the word-aspect matrix, and ∑ is the diagonal matrix of the singular values. Similar to PCA, SVD also combines columns of the original matrix linearly to arrive at the U matrix. To arrive at the V matrix, SVD combines the rows of the original matrix linearly.

Stages in Natural Language Processing:

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. 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]. In recent years, the clinical NLP community has made considerable efforts to overcome these barriers by releasing and sharing resources, e.g., de-identified clinical corpora, annotation guidelines, and NLP tools, in a multitude of languages [6]. The development and maturity of NLP systems has also led to advancements in the employment of NLP methods in clinical research contexts.

With lexical semantics, the study of word meanings, semantic analysis provides a deeper understanding of unstructured text. 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 notes. 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.

Finally, it analyzes the surrounding text and text structure to accurately determine the proper meaning of the words in context. However, many organizations struggle to capitalize on it because of their inability to analyze unstructured data. This challenge is a frequent roadblock for artificial intelligence (AI) initiatives that tackle language-intensive processes. Information extraction is one of the most important applications of NLP. It is used for extracting structured information from unstructured or semi-structured machine-readable documents. Although there has been great progress in the development of new, shareable and richly-annotated resources leading to state-of-the-art performance in developed NLP tools, there is still room for further improvements.

• The translations obtained by this model were defined by the organizers as “superhuman” and considered highly superior to the ones performed by human experts.
• Hence, under Compositional Semantics Analysis, we try to understand how combinations of individual words form the meaning of the text.
• Now, we will fit the data into the grid search and view the best parameter using the “best_params_” attribute of GridSearchCV.
• In that case it would be the example of homonym because the meanings are unrelated to each other.

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