An Introduction to Natural Language Processing NLP

One thing that we skipped over before is that words may not only have typos when a user types it into a search bar. If you decide not to include lemmatization or stemming in your search engine, there is still one normalization technique that you should consider. Nearly all search engines tokenize text, but there are further steps an engine can take to normalize the tokens. Of course, we know that sometimes capitalization does change the meaning of a word or phrase.

Given the current findings, achieving a comprehensive understanding of The Analects’ translations requires considering both readers’ and translators’ perspectives. The table presented above reveals marked differences in the translation of these terms among the five translators. These disparities can be attributed to a variety of factors, including the translators’ intended audience, the cultural context at the time of translation, and the unique strategies each translator employed to convey the essence of the original text. The term “君子 Jun Zi,” often translated as “gentleman” or “superior man,” serves as a typical example to further illustrate this point regarding the translation of core conceptual terms. Table 7 provides a representation that delineates the ranked order of the high-frequency words extracted from the text.

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Here, as well as in subevent-subevent relation predicates, the subevent variable in the first argument slot is not a time stamp; rather, it is one of the related parties. In_reaction_to(e1, Stimulus) should be understood to mean that subevent e1 occurs as a response to a Stimulus. Subevent modifier predicates also include monovalent predicates such as irrealis(e1), which conveys that the subevent described through other predicates with the e1 time stamp may or may not be realized. This includes making explicit any predicative opposition denoted by the verb.

A clear example of that utility of VerbNet semantic representations in uncovering implicit information is in a sentence with a verb such as “carry” (or any verb in the VerbNet carry-11.4 class for that matter). If we have ◂ X carried Y to Z▸, we know that by the end of this event, both Y and X have changed their location state to Z. This is not recoverable even if we know that “carry” is a motion event (and therefore has a theme, source, and destination). This is in contrast to a “throw” event where only the theme moves to the destination and the agent remains in the original location. Such semantic nuances have been captured in the new GL-VerbNet semantic representations, and Lexis, the system introduced by Kazeminejad et al., 2021, has harnessed the power of these predicates in its knowledge-based approach to entity state tracking. Despite impressive advances in NLU using deep learning techniques, human-like semantic abilities in AI remain out of reach.

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The earliest decision trees, producing systems of hard if–then rules, were still very similar to the old rule-based approaches. Only the introduction of hidden Markov models, applied to part-of-speech tagging, announced the end of the old rule-based approach. The typical pipeline to solve this task is to identify targets, classify which frame, and identify arguments.

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). When there are multiple content types, federated search can perform admirably by showing multiple search results in a single UI at the same time. While NLP is all about processing text and natural language, NLU is about understanding that text. They need the information to be structured in specific ways to build upon it.

This research aims to enrich readers’ holistic understanding of The Analects by providing valuable insights. Additionally, this research offers pragmatic recommendations and strategies to future translators embarking on this seminal work. Lexis relies first and foremost on the GL-VerbNet semantic representations instantiated with the extracted events and arguments from a given sentence, which are part of the SemParse output (Gung, 2020)—the state-of-the-art VerbNet neural semantic parser. In addition, it relies on the semantic role labels, which are also part of the SemParse output. The state change types Lexis was designed to predict include change of existence (created or destroyed), and change of location.

Syntactic analysis (syntax) and semantic analysis (semantic) are the two primary techniques that lead to the understanding of natural language. For translators, in the process of translating The Analects, it is crucial to accurately convey core conceptual terms and personal names, utilizing relevant vocabulary and providing pertinent supplementary information in the para-text. The author advocates for a compensatory nlp semantics approach in translating core conceptual words and personal names. 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.

By doing so, readers can greatly improve their cognitive abilities during the reading process. Furthermore, this study advises translators to provide comprehensive paratextual interpretations of core conceptual terms and personal names to more accurately mirror the context of the original text. Out of the entire corpus, 1,940 sentence pairs exhibit a semantic similarity of ≤ 80%, comprising 21.8% of the total sentence pairs. These low-similarity sentence pairs play a significant role in determining the overall similarity between the different translations. They further provide valuable insights into the characteristics of different translations and aid in identifying potential errors. By delving deeper into the reasons behind this substantial difference in semantic similarity, this study can enable readers to gain a better understanding of the text of The Analects.

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 (;). However, it is crucial to note that these subdivisions were not exclusively reliant on punctuation marks. Instead, this study followed the principle of dividing the text into lines to make sure that each segment fully expresses the original meaning. Finally, each translated English text was aligned with its corresponding original text. Although they are not situation predicates, subevent-subevent or subevent-modifying predicates may alter the Aktionsart of a subevent and are thus included at the end of this taxonomy.

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

Every type of communication — be it a tweet, LinkedIn post, or review in the comments section of a website — may contain potentially relevant and even valuable information that companies must capture and understand to stay ahead of their competition. Capturing the information is the easy part but understanding what is being said (and doing this at scale) is a whole different story. In the ever-expanding era of textual information, it is important for organizations to draw insights from such data to fuel businesses. Semantic Analysis helps machines interpret the meaning of texts and extract useful information, thus providing invaluable data while reducing manual efforts. Hence, under Compositional Semantics Analysis, we try to understand how combinations of individual words form the meaning of the text. Let me get you another shorter example, “Las Vegas” is a frame element of BECOMING_DRY frame.

We believe VerbNet is unique in its integration of semantic roles, syntactic patterns, and first-order-logic representations for wide-coverage classes of verbs. Natural language processing (NLP) is an interdisciplinary subfield of computer science and linguistics. It is primarily concerned with giving computers the ability to support and manipulate human language.

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