Natural Language Processing for the Semantic Web SpringerLink

Gathering market intelligence becomes much easier with natural language processing, which can analyze online reviews, social media posts and web forums. Compiling this data can help marketing teams understand what consumers care about and how they perceive a business’ brand. If you’re interested in using some of these techniques with Python, take a look at the Jupyter Notebook about Python’s natural language toolkit (NLTK) that I created. You can also check out my blog post about building neural networks with Keras where I train a neural network to perform sentiment analysis.

Introducing consistency in the predicate structure was a major goal in this aspect of the revisions. In Classic VerbNet, the basic predicate structure consisted of a time stamp (Start, During, or End of E) and an often inconsistent number of semantic roles. Some predicates could appear with or without a time stamp, and the order of semantic roles was not fixed. For example, the Battle-36.4 class included the predicate manner(MANNER, Agent), where a constant that describes the manner of the Agent fills in for MANNER. While manner did not appear with a time stamp in this class, it did in others, such as Bully-59.5 where it was given as manner(E, MANNER, Agent).

Corpus building

This strategy enables the translator to maintain consistency with the original text while providing additional information about the meanings and backgrounds. This approach ensures simplicity and naturalness in expression, mirrors the original text as closely as possible, and maximizes comprehension and contextual impact with minimal cognitive effort. The data displayed in Table 5 and Attachment 3 underscore significant discrepancies in semantic similarity (values ≤ 80%) among specific sentence pairs across the five translations, with a particular emphasis on variances in word choice. As mentioned earlier, the factors contributing to these differences can be multi-faceted and are worth exploring further. Conversely, the outcomes of semantic similarity calculations falling below 80% constitute 1,973 sentence pairs, approximating 22% of the aggregate number of sentence pairs. Although this subset of sentence pairs represents a relatively minor proportion, it holds pivotal significance in impacting semantic representation amongst the varied translations, unveiling considerable semantic variances therein.

These structures allow us to demonstrate external relationships between predicates, such as granularity and valency differences, and in turn, we can now demonstrate inter-class relationships that were previously only implicit. Like the classic VerbNet representations, we use E to indicate a state that holds throughout an event. For this reason, many of the representations for state verbs needed no revision, including the representation from the Long-32.2 class. In contrast, in revised GL-VerbNet, “events cause events.” Thus, something an agent does [e.g., do(e2, Agent)] causes a state change or another event [e.g., motion(e3, Theme)], which would be indicated with cause(e2, e3). NLP-powered apps can check for spelling errors, highlight unnecessary or misapplied grammar and even suggest simpler ways to organize sentences. Natural language processing can also translate text into other languages, aiding students in learning a new language.

Title:Semantic Representation and Inference for NLP

This integration establishes a new paradigm in translation research and broadens the scope of translation studies. The above discussion has focused on the identification and encoding of subevent structure for predicative expressions in language. Starting with the view that subevents of a complex event can be modeled as a sequence of states (containing formulae), a dynamic event structure explicitly labels the transitions that move an event from state to state (i.e., programs). Semantic Analysis is a subfield of Natural Language Processing (NLP) that attempts to understand the meaning of Natural Language. Understanding Natural Language might seem a straightforward process to us as humans.

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. In addition to substantially revising the representation of subevents, we increased the informativeness of the semantic predicates themselves and improved their consistency across classes. This effort included defining each predicate and its arguments and, where possible, relating them hierarchically in order for users to chose the appropriate level of meaning granularity for their needs. We also strove to connect classes that shared semantic aspects by reusing predicates wherever possible. In some cases this meant creating new predicates that expressed these shared meanings, and in others, replacing a single predicate with a combination of more primitive predicates.

Studying the meaning of the Individual Word

” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics), 7436–7453. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

I hope after reading that article you can understand the power of NLP in Artificial Intelligence. So, in this part of this series, we will start our discussion on Semantic analysis, which is a level of the NLP tasks, and see all the important terminologies or concepts in this analysis. However, many organizations struggle to capitalize on it because of their inability to analyze nlp semantic unstructured data. This challenge is a frequent roadblock for artificial intelligence (AI) initiatives that tackle language-intensive processes. For comparative analysis, this study has compiled various interpretations of certain core conceptual terms across five translations of The Analects. Most search engines only have a single content type on which to search at a time.

Dissecting The Analects: an NLP-based exploration of semantic similarities and differences across English translations

The first step entailed establishing preprocessing parameters, which included eliminating special symbols, converting capitalized words to lowercase, and sequentially reading the PDF file whilst preserving the English text. Subsequently, this study aligned the cleaned texts of the translations by Lau, Legge, Jennings, Slingerland, and Watson at the sentence level to construct a parallel corpus. The original text of The Analects was segmented using a method that divided it into 503 sections based on natural section divisions. This study further subdivided these segments using punctuation marks, such as periods (.), question marks (?), and semicolons (;).

• The overall results of the study were that semantics is paramount in processing natural languages and aid in machine learning.
• This facilitates a quantitative discourse on the similarities and disparities present among the translations.
• Despite impressive advances in NLU using deep learning techniques, human-like semantic abilities in AI remain out of reach.
• In multi-subevent representations, ë conveys that the subevent it heads is unambiguously a process for all verbs in the class.