5 Use Cases of Semantic Analysis in Natural Language Processing

The identification of the predicate and the arguments for that predicate is known as semantic role labeling. As in any area where theory meets practice, we were forced to stretch our initial formulations to accommodate many variations we had not first anticipated. Although its coverage of English vocabulary is not complete, it does include over 6,600 verb senses. We were not allowed to cherry-pick examples for our semantic patterns; they had to apply to every verb and every syntactic variation in all VerbNet classes.

For instance, in Korea, recent law enactments have been implemented to prevent the unauthorized use of medical information – but without specifying what constitutes PHI, in which case the HIPAA definitions have been proven useful [23]. As illustrated earlier, the word “ring” is ambiguous, as it can refer to both a piece of jewelry worn on the finger and the sound of a bell. To disambiguate the word and select the most appropriate meaning based on the given context, we used the NLTK libraries and the Lesk algorithm. Analyzing the provided sentence, the most suitable interpretation of “ring” is a piece of jewelry worn on the finger. Now, let’s examine the output of the aforementioned code to verify if it correctly identified the intended meaning.

Meaning Representation

In this first stage, we decided on our system of subevent sequencing and developed new predicates to relate them. We also defined our event variable e and the variations that expressed aspect and temporal sequencing. At this point, we only worked with the most prototypical examples of changes of location, state and possession and that involved a minimum of participants, usually Agents, Patients, and Themes. Semantic analysis is defined as a process of understanding natural language (text) by extracting insightful information such as context, emotions, and sentiments from unstructured data. This article explains the fundamentals of semantic analysis, how it works, examples, and the top five semantic analysis applications in 2022.

By comprehending the intricate semantic relationships between words and phrases, we can unlock a wealth of information and significantly enhance a wide range of NLP applications. In this comprehensive article, we will embark on a captivating journey into the realm of semantic analysis. We will delve into its core concepts, explore powerful semantic analysis nlp techniques, and demonstrate their practical implementation through illuminating code examples using the Python programming language. Get ready to unravel the power of semantic analysis and unlock the true potential of your text data. There we can identify two named entities as “Michael Jordan”, a person and “Berkeley”, a location.

Deep Learning and Natural Language Processing

This theme of analyzing neural networks has connections to the broader work on interpretability in machine learning, along with specific characteristics of the NLP field. 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.

Thanks to that, we can obtain a numerical vector, which tells us how many times a particular word has appeared in a given text. The classes using the organizational role cluster of semantic predicates, showing the Classic VN vs. VN-GL representations. State changes with a notable transition or cause take the form we used for changes in location, with multiple temporal phases in the event. The similarity can be seen in 14 from the Tape-22.4 class, as can the predicate we use for Instrument roles.

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. 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. 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].

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. Cdiscount, an online retailer of goods and services, uses semantic analysis to analyze and understand online customer reviews. When a user purchases an item on the ecommerce site, they can potentially give post-purchase feedback for their activity.

Based on the content, speaker sentiment and possible intentions, NLP generates an appropriate response. In machine translation done by deep learning algorithms, language is translated by starting with a sentence and generating vector representations that represent it. Then it starts to generate words in another language that entail the same information.

• In some of these systems, features are more easily understood by humans—they can be morphological properties, lexical classes, syntactic categories, semantic relations, etc.
• This study also highlights the future prospects of semantic analysis domain and finally the study is concluded with the result section where areas of improvement are highlighted and the recommendations are made for the future research.
• Their experiments revealed interesting differences between word embedding models, where in some models information is more focused in individual dimensions.
• 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.
• These models are especially successful at capturing frequent properties, while some rare properties are more difficult to learn.

We believe VerbNet is unique in its integration of semantic roles, syntactic patterns, and first-order-logic representations for wide-coverage classes of verbs. This survey attempted to review and summarize as much of the current research as possible, while organizing it along several prominent themes. We have emphasized aspects in analysis that are specific to language—namely, what linguistic information is captured in neural networks, which phenomena they are successful at capturing, and where they fail. Many of the analysis methods are general techniques from the larger machine learning community, such as visualization via saliency measures or evaluation by adversarial examples.

The need for deeper semantic processing of human language by our natural language processing systems is evidenced by their still-unreliable performance on inferencing tasks, even using deep learning techniques. These tasks require the detection of subtle interactions between participants in events, of sequencing of subevents that are often not explicitly mentioned, and of changes to various participants across an event. Human beings can perform this detection even when sparse lexical items are involved, suggesting that linguistic insights into these abilities could improve NLP performance. In this article, we describe new, hand-crafted semantic representations for the lexical resource VerbNet that draw heavily on the linguistic theories about subevent semantics in the Generative Lexicon (GL). VerbNet defines classes of verbs based on both their semantic and syntactic similarities, paying particular attention to shared diathesis alternations. For each class of verbs, VerbNet provides common semantic roles and typical syntactic patterns.

• Methods for generating targeted attacks in NLP could possibly take more inspiration from adversarial attacks in other fields.
• A lexicon- and regular-expression based system (TTK/GUTIME [67]) developed for general NLP was adapted for the clinical domain.
• In FrameNet, this is done with a prose description naming the semantic roles and their contribution to the frame.
• NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models.
• Gulordava et al. (2018) extended this to other agreement phenomena, but they relied on syntactic information available in treebanks, resulting in a smaller dataset.