Unraveling the Power of Semantic Analysis: Uncovering Deeper Meaning and Insights in Natural Language Processing NLP with Python by TANIMU ABDULLAHI

The reference standard is annotated for these pseudo-PHI entities and relations. To date, few other efforts have been made to develop and release new corpora for developing and evaluating de-identification applications. I will explore a variety of commonly used techniques in semantic analysis and demonstrate their implementation in Python. By covering these techniques, you will gain a comprehensive understanding of how semantic analysis is conducted and learn how to apply these methods effectively using the Python programming language. One of the simplest and most popular methods of finding meaning in text used in semantic analysis is the so-called Bag-of-Words approach.

• The entities involved in this text, along with their relationships, are shown below.
• 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.
• Data science and machine learning are commonly used terms, but do you know the difference?
• To put results in perspective, one may compare model performance to human performance on the same task (Gulordava et al., 2018).
• Note that LSA is an unsupervised learning technique — there is no ground truth.

It is important to recognize the border between linguistic and extra-linguistic semantic information, and how well VerbNet semantic representations enable us to achieve an in-depth linguistic semantic analysis. Using the Generative Lexicon subevent structure to revise the existing VerbNet semantic representations resulted in several new standards in the representations’ form. As discussed in Section 2.2, applying the GL Dynamic Event Model to VerbNet temporal sequencing allowed us refine the event sequences by expanding the previous three-way division of start(E), during(E), and end(E) into a greater number of subevents if needed. These numbered subevents allow very precise tracking of participants across time and a nuanced representation of causation and action sequencing within a single event. We’ve further expanded the expressiveness of the temporal structure by introducing predicates that indicate temporal and causal relations between the subevents, such as cause(ei, ej) and co-temporal(ei, ej). In the rest of this article, we review the relevant background on Generative Lexicon (GL) and VerbNet, and explain our method for using GL’s theory of subevent structure to improve VerbNet’s semantic representations.

ML & Data Science

For this, we use a single subevent e1 with a subevent-modifying duration predicate to differentiate the representation from ones like (20) in which a single subevent process is unbounded. This also eliminates the need for the second-order logic of start(E), during(E), and end(E), allowing for more nuanced temporal relationships between subevents. The default assumption in this new schema is that e1 precedes e2, which precedes e3, and so on. When appropriate, however, more specific predicates can be used to specify other relationships, such as meets(e2, e3) to show that the end of e2 meets the beginning of e3, or co-temporal(e2, e3) to show that e2 and e3 occur simultaneously.

In some of these systems, features are more easily understood by humans—they can be morphological properties, lexical classes, syntactic categories, semantic relations, etc. Much of the analysis work thus aims to understand how linguistic concepts that were common as features in NLP systems are captured in neural networks. IBM’s Watson provides a conversation service that uses semantic analysis (natural language understanding) and deep learning to derive meaning from unstructured data. It analyzes text to reveal the type of sentiment, emotion, data category, and the relation between words based on the semantic role of the keywords used in the text.

Introduction to NLP

Another pair of classes shows how two identical state or process predicates may be placed in sequence to show that the state or process continues past a could-have-been boundary. In example 22 from the Continue-55.3 class, the semantic analysis nlp representation is divided into two phases, each containing the same process predicate. This predicate uses ë because, while the event is divided into two conceptually relevant phases, there is no functional bound between them.

Although VerbNet has been successfully used in NLP in many ways, its original semantic representations had rarely been incorporated into NLP systems (Zaenen et al., 2008; Narayan-Chen et al., 2017). We have described here our extensive revisions of those representations using the Dynamic Event Model of the Generative Lexicon, which we believe has made them more expressive and potentially more useful for natural language understanding. One of the downstream NLP tasks in which VerbNet semantic representations have been used is tracking entity states at the sentence level (Clark et al., 2018; Kazeminejad et al., 2021). Entity state tracking is a subset of the greater machine reading comprehension task. The goal is to track the changes in states of entities within a paragraph (or larger unit of discourse). This change could be in location, internal state, or physical state of the mentioned entities.