Semantic Analysis Guide to Master Natural Language Processing Part 9

The adjusted odds of response to the open-ended question for each of the respective response groups are displayed in Table 2. Increased adjusted odds of response to the open-ended question were found in personnel with service in the Army, Navy/Coast Guard, and the Marine Corps in comparison with Air Force members. Cohort members who were older, serving on active duty and in combat specialties were significantly more likely to respond to the open-ended question across all panels. Black non-Hispanic participants were significantly less likely to respond than white non-Hispanic participants.

In semantic analysis, word sense disambiguation refers to an automated process of determining the sense or meaning of the word in a given context. As natural language consists of words with several meanings (polysemic), the objective here is to recognize the correct meaning based on its use. 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 helps in processing customer queries and understanding their meaning, thereby allowing an organization to understand the customer’s inclination.

Relationship Extraction

Semantic analysis helps to quickly and efficiently identify the reasons for satisfaction or dissatisfaction with the customer experience in-store. Today, many retailers still act on intuition, due to a lack of resources and expertise to analyse all customer feedback. While semantic analysis is more modern and sophisticated, it is also expensive to implement. At AtScale, we provide a universal semantic layer that fits seamlessly into a modern data stack. We aim to make analytics accessible to everyone while making lives easier for data teams. To avoid semantic sprawl, organizations can standardize on a single semantic layer as a standalone component in the modern data stack—a universal “translator” that brings consistency to metrics to every corner of the company.

Relationship extraction is a procedure used to determine the semantic relationship between words in a text. In semantic analysis, relationships include various entities, such as an individual’s name, place, company, designation, etc. Moreover, semantic categories such as, ‘is the chairman of,’ ‘main branch located a’’, ‘stays at,’ and others connect the above entities. Semantic analysis helps fine-tune the search engine optimization (SEO) strategy by allowing companies to analyze and decode users’ searches.

Lexical Semantics

Advances in NLP have led to breakthrough innovations such as chatbots, automated content creators, summarizers, and sentiment analyzers. The field’s ultimate goal is to ensure that computers understand and process language as well as humans. Like many semantic analysis tools, YourTextGuru provides a list of secondary keywords and phrases or entities to use in your content. However, reaching this goal can be complicated and semantic analysis will allow you to determine the intent of the queries, that is to say, the sequences of words and keywords typed by users in the search engines.

Computerized text-parsing tools such as LSA allow an objective review of text responses that would be otherwise impossible to standardize. LSA may be used to define health concerns with related context, and identify whether they represent large-scale concerns of a few individuals or common concerns of a great many individuals. Results will continue to help drive directions of future research and survey content.

This challenge is a frequent roadblock for artificial intelligence (AI) initiatives that tackle language-intensive processes. Inspired by the latest findings on how the human brain processes language, this Austria-based startup worked out a fundamentally new approach to mining large volumes of texts to create the first language-agnostic semantic engine. Fueled with hierarchical temporal memory (HTM) algorithms, this text mining software generates semantic fingerprints textual information, promising virtually unlimited text mining use cases and a massive market opportunity.

Once the initial semantic space is created, LSA is fully automatic, permitting rapid analysis of large sets of responses. Because knowledge of word meaning is not derived from thesauri, ontologies, or hand-coding of relationships among words or among responses, bias from human coders and interpretation error is minimized. LSA can evaluate a word whose meaning is determined contextually (e.g., «we moved back,» is differentiated from «hurt my back»). Furthermore, it can determine similarity among responses without accounting for word order or even if passages share no words in common [22]. LSA is a fully automatic mathematical/statistical technique for extracting and inferring meaningful relations from the contextual usage of words [8, 9]. Using LSA software developed by Pearson Knowledge Technologies, lexical analysis was performed on the responses to the final question, which asks participants to share any other health concerns not covered in the structured instrument.

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