Natural Language Processing for the Semantic Web SpringerLink

The arguments of each predicate are represented using the thematic roles for the class. These roles provide the link between the syntax and the semantic representation. Each participant mentioned in the syntax, as well as necessary but unmentioned participants, are accounted for in the semantics. For example, the second component of the first has_location semantic predicate above includes an unidentified Initial_Location. That role is expressed overtly in other syntactic alternations in the class (e.g., The horse ran from the barn), but in this frame its absence is indicated with a question mark in front of the role. Temporal sequencing is indicated with subevent numbering on the event variable e.

From readers cognitive enhancement perspective, this approach can significantly improve readers’ understanding and reading fluency, thus enhancing reading efficiency. This study employs natural language processing (NLP) algorithms to analyze semantic similarities among five English translations of The Analects. To achieve this, a corpus is constructed from these translations, and three algorithms—Word2Vec, GloVe, and BERT—are applied to assess the semantic congruence of corresponding sentences among the different translations. Analysis reveals that core concepts, and personal names substantially shape the semantic portrayal in the translations. In conclusion, this study presents critical findings and provides insightful recommendations to enhance readers’ comprehension and to improve the translation accuracy of The Analects for all translators.

Title:Semantic Representation and Inference for NLP

According to Chris Manning, a machine learning professor at Stanford, it is a discrete, symbolic, categorical signaling system. This means we can convey the same meaning in different ways (i.e., speech, gesture, signs, etc.) The encoding by the human brain is a continuous pattern of activation by which the symbols are transmitted via continuous signals of sound and vision. In simple words, we can say that lexical semantics represents the relationship between lexical items, the meaning of sentences, and the syntax of the sentence.

To delve deeper into these disparities and their foundational causes, a more comprehensive and meticulous analysis is slated for the subsequent sections. Recent years have brought a revolution in the ability of computers to understand human languages, programming languages, and even biological and chemical sequences, such as DNA and protein structures, that resemble language. The latest AI models are unlocking these areas to analyze the meanings of input text and generate meaningful, expressive output.

Benefits of Natural Language Processing

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. Often compared to the lexical resources FrameNet and PropBank, which also provide semantic roles, VerbNet actually differs from these in several key ways, not least of which is its semantic representations. Both FrameNet and VerbNet group verbs semantically, although VerbNet takes into consideration the syntactic regularities of the verbs as well. Both resources define semantic roles for these verb groupings, with VerbNet roles being fewer, more coarse-grained, and restricted to central participants in the events.

In other words, given we found a predicate, which words or phrases connected to it. It is essentially the same as semantic role labeling [6], who did what to whom. The main difference is semantic role labeling assumes that all predicates are verbs [7], while in semantic frame parsing it has no such assumption. The five translators examined in this study have effectively achieved a balance between being faithful to the original text and being easy for readers to accept by utilizing apt vocabulary and providing essential para-textual information. As English translations of The Analects continue to evolve, future translators can further enhance this work by summarizing and supplementing paratextual information, thereby building on the foundations established by their predecessors.

Applying NLP in Semantic Web Projects

There are multiple stemming algorithms, and the most popular is the Porter Stemming Algorithm, which has been around since the 1980s. Stemming breaks a word down to its “stem,” or other variants of the word it is based on. German speakers, for example, can merge words (more accurately “morphemes,” but close enough) together to form a larger word. The German word for “dog house” is “Hundehütte,” which contains the words for both “dog” (“Hund”) and “house” (“Hütte”). Separating on spaces alone means that the phrase “Let’s break up this phrase!

This step is necessary because word order does not need to be exactly the same between the query and the document text, except when a searcher wraps the query in quotes. The next normalization challenge is breaking down the text the searcher has typed in the search bar and the text in the nlp semantic document. Computers seem advanced because they can do a lot of actions in a short period of time. Copyright © 2022 Brown, Bonn, Kazeminejad, Zaenen, Pustejovsky and Palmer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).

From Jaccard to OpenAI, implement the best NLP algorithm for your semantic textual similarity projects

Recently, Kazeminejad et al. (2022) has added verb-specific features to many of the VerbNet classes, offering an opportunity to capture this information in the semantic representations. These features, which attach specific values to verbs in a class, essentially subdivide the classes into more specific, semantically coherent subclasses. For example, verbs in the admire-31.2 class, which range from loathe and dread to adore and exalt, have been assigned a +negative_feeling or +positive_feeling attribute, as applicable.

An example is in the sentence “The water over the years carves through the rock,” for which ProPara human annotators have indicated that the entity “space” has been CREATED. This is extra-linguistic information that is derived through world knowledge only. Lexis, and any system that relies on linguistic cues only, is not expected to be able to make this type of analysis.

Meaning Representation

Several factors, such as the differing dimensions of semantic word vectors used by each algorithm, could contribute to these dissimilarities. Figure 1 primarily illustrates the performance of three distinct NLP algorithms in quantifying semantic similarity. 1, although there are variations in the absolute values among the algorithms, they consistently reflect a similar trend in semantic similarity across sentence pairs.

Therefore it is a natural language processing problem where text needs to be understood in order to predict the underlying intent. The sentiment is mostly categorized into positive, negative and neutral categories. Syntactic analysis (syntax) and semantic analysis (semantic) are the two primary techniques that lead to the understanding of natural language. This analysis gives the power to computers to understand and interpret sentences, paragraphs, or whole documents, by analyzing their grammatical structure, and identifying the relationships between individual words of the sentence in a particular context. This is a key concern for NLP practitioners responsible for the ROI and accuracy of their NLP programs.

Understanding Semantic Analysis – NLP

This suggests that while the selection of a specific NLP algorithm in practical applications may hinge on particular scenarios and requirements, in terms of overall semantic similarity judgments, their reliability remains consistent. For example, a sentence that exhibits low similarity according to the Word2Vec algorithm tends to also score lower on the similarity results in the GloVe and BERT algorithms, although it may not necessarily be the lowest. In contrast, sentences garnering high similarity via the Word2Vec algorithm typically correspond with elevated scores when evaluated by the GloVe and BERT algorithms.

Insurance companies can assess claims with natural language processing since this technology can handle both structured and unstructured data. NLP can also be trained to pick out unusual information, allowing teams to spot fraudulent claims. While NLP-powered chatbots and callbots are most common in customer service contexts, companies have also relied on natural language processing to power virtual assistants.

However, it falls short for phenomena involving lower frequency vocabulary or less common language constructions, as well as in domains without vast amounts of data. In terms of real language understanding, many have begun to question these systems’ abilities to actually interpret meaning from language (Bender and Koller, 2020; Emerson, 2020b). Several studies have shown that neural networks with high performance on natural language inferencing tasks are actually exploiting spurious regularities in the data they are trained on rather than exhibiting understanding of the text. Once the data sets are corrected/expanded to include more representative language patterns, performance by these systems plummets (Glockner et al., 2018; Gururangan et al., 2018; McCoy et al., 2019). VerbNet is also somewhat similar to PropBank and Abstract Meaning Representations (AMRs). PropBank defines semantic roles for individual verbs and eventive nouns, and these are used as a base for AMRs, which are semantic graphs for individual sentences.

• They further provide valuable insights into the characteristics of different translations and aid in identifying potential errors.
• This article aims to give a broad understanding of the Frame Semantic Parsing task in layman terms.
• NLP is used for a wide variety of language-related tasks, including answering questions, classifying text in a variety of ways, and conversing with users.
• With its ability to process large amounts of data, NLP can inform manufacturers on how to improve production workflows, when to perform machine maintenance and what issues need to be fixed in products.

Experimental results demonstrate that semantics-aware neural models give better accuracy than those without semantics information. On average of the three strong models, our semantic-aware approach improves natural language inference in different languages. Argument identification is not probably what “argument” some of you may think, but rather refer to the predicate-argument structure [5].