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

It’s an essential sub-task of Natural Language Processing (NLP) and the driving force behind machine learning tools like chatbots, search engines, and text analysis. While NLP and other forms of AI aren’t perfect, natural language processing can bring objectivity to data analysis, providing more accurate and consistent results. Consider the task of text summarization which is used to create digestible chunks of information from large quantities of text.

And if we want to know the relationship of or between sentences, we train a neural network to make those decisions for us. This is a key concern for NLP practitioners responsible for the ROI and accuracy of their NLP programs. You can proactively get ahead of NLP problems by improving machine language understanding.

Methods

In that case there is a risk that analysing the specific words without understanding the context may come wrong. It is possible because the terms “pain” and “killer” are likely to be classified as “negative”. Semantic analysis can be beneficial here because it is based on the whole context of the statement, not just the words used. The assignment of meaning to terms is based on what other words usually occur in their close vicinity. To create such representations, you need many texts as training data, usually Wikipedia articles, books and websites. As you can see, this approach does not take into account the meaning or order of the words appearing in the text.

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. Several companies are using the sentiment analysis functionality to understand the voice of their customers, extract sentiments and emotions from text, and, in turn, derive actionable data from them. It helps capture the tone of customers when they post reviews and opinions on social media posts or company websites. These chatbots act as semantic analysis tools that are enabled with keyword recognition and conversational capabilities.

The NLP Problem Solved by Semantic Analysis

The extra dimension that wasn’t available to us in our original matrix, the r dimension, is the amount of latent concepts. Generally we’re trying to represent our matrix as other matrices that have one of their axes being this set of components. You will also note that, based on dimensions, the multiplication of the 3 matrices (when V is transposed) will lead us back to the shape of our original matrix, the r dimension effectively disappearing. Well, suppose that actually, “reform” wasn’t really a salient topic across our articles, and the majority of the articles fit in far more comfortably in the “foreign policy” and “elections”. Thus “reform” would get a really low number in this set, lower than the other two.

Challenge sets are usually created either programmatically or manually, by handcrafting specific examples. Often, semi-automatic methods are used to compile an initial list of examples that is manually verified by annotators. The specific method semantic analysis nlp also affects the kind of language use and how natural or artificial/synthetic the examples are. We describe here some trends in dataset construction methods in the hope that they may be useful for researchers contemplating new datasets.

Syntactic and Semantic Analysis

Clinical NLP is the application of text processing approaches on documents written by healthcare professionals in clinical settings, such as notes and reports in health records. Clinical NLP can provide clinicians with critical patient case details, which are often locked within unstructured clinical texts and dispersed throughout a patient’s health record. Semantic analysis is one of the main goals of clinical NLP research and involves unlocking the meaning of these texts by identifying clinical entities (e.g., patients, clinicians) and events (e.g., diseases, treatments) and by representing relationships among them. There has been an increase of advances within key NLP subtasks that support semantic analysis.

In this case, AI algorithms based on semantic analysis can detect companies with positive reviews of articles or other mentions on the web. One of the most advanced translators on the market using semantic analysis is DeepL Translator, a machine translation system created by the German company DeepL. “Automatic entity state annotation using the verbnet semantic parser,” in Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop (Lausanne), 123–132. “Integrating generative lexicon event structures into verbnet,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (Miyazaki), 56–61. At present, despite the recognized importance for interpretability, our ability to explain predictions of neural networks in NLP is still limited. Systems are typically evaluated by their performance on the challenge set examples, either with the same metric used for evaluating the system in the first place, or via a proxy, as in the contrastive pairs evaluation of Sennrich (2017).

The negative end of concept 5’s axis seems to correlate very strongly with technological and scientific themes (‘space’, ‘science’, ‘computer’), but so does the positive end, albeit more focused on computer related terms (‘hard’, ‘drive’, ‘system’). TruncatedSVD will return it to as a numpy array of shape (num_documents, num_components), so we’ll turn it into a Pandas dataframe for ease of manipulation. Just for the purpose of visualisation and EDA of our decomposed data, let’s fit our LSA object (which in Sklearn is the TruncatedSVD class) to our train data and specifying only 20 components. The values in 𝚺 represent how much each latent concept explains the variance in our data. When these are multiplied by the u column vector for that latent concept, it will effectively weigh that vector.

A further level of semantic analysis is text summarization, where, in the clinical setting, information about a patient is gathered to produce a coherent summary of her clinical status. This is a challenging NLP problem that involves removing redundant information, correctly handling time information, accounting for missing data, and other complex issues. Pivovarov and Elhadad present a thorough review of recent advances in this area [79]. Furthermore, NLP method development has been enabled by the release of these corpora, producing state-of-the-art results [17]. Following the pivotal release of the 2006 de-identification schema and corpus by Uzuner et al. [24], a more-granular schema, an annotation guideline, and a reference standard for the heterogeneous MTSamples.com corpus of clinical texts were released [14]. The schema extends the 2006 schema with instructions for annotating fine-grained PHI classes (e.g., relative names), pseudo-PHI instances or clinical eponyms (e.g., Addison’s disease) as well as co-reference relations between PHI names (e.g., John Doe COREFERS to Mr. Doe).

Question Answering

All in all, semantic analysis enables chatbots to focus on user needs and address their queries in lesser time and lower cost. For example, semantic analysis can generate a repository of the most common customer inquiries and then decide how to address or respond to them. Semantic analysis helps fine-tune the search engine optimization (SEO) strategy by allowing companies to analyze and decode users’ searches. The approach helps deliver optimized and suitable content to the users, thereby boosting traffic and improving result relevance. The semantic analysis does throw better results, but it also requires substantially more training and computation.

Applying semantic analysis in natural language processing can bring many benefits to your business, regardless of its size or industry. If you wonder if it is the right solution for you, this article may come in handy. We strove to be as explicit in the semantic designations as possible while still ensuring that any entailments asserted by the representations applied to all verbs in a class. Occasionally this meant omitting nuances from the representation that would have reflected the meaning of most verbs in a class. It also meant that classes with a clear semantic characteristic, such as the type of emotion of the Experiencer in the admire-31.2 class, could only generically refer to this characteristic, leaving unexpressed the specific value of that characteristic for each verb. A second, non-hierarchical organization (Appendix C) groups together predicates that relate to the same semantic domain and defines, where applicable, the predicates’ relationships to one another.

Named Entity Recognition

We will also evaluate the effectiveness of this resource for NLP by reviewing efforts to use the semantic representations in NLP tasks. Early rule-based systems that depended on linguistic knowledge showed promise in highly constrained domains and tasks. Machine learning side-stepped the rules and made great progress on foundational NLP tasks such as syntactic parsing. When they hit a plateau, more linguistically oriented features were brought in to boost performance.