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

Healthcare professionals can develop more efficient workflows with the help of natural language processing. During procedures, doctors can dictate their actions and notes to an app, which produces an accurate transcription. NLP can also scan patient documents to identify patients who would be best suited for certain clinical trials.

However, semantic analysis has challenges, including the complexities of language ambiguity, cross-cultural differences, and ethical considerations. As the field continues to evolve, researchers and practitioners are actively working to overcome these challenges and make semantic analysis more robust, honest, and efficient. Semantic analysis extends beyond text to encompass multiple modalities, including images, videos, and audio. Integrating these modalities will provide a more comprehensive and nuanced semantic understanding. In the next section, we’ll explore future trends and emerging directions in semantic analysis.

Relational semantics (semantics of individual sentences)

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.

However, despite their recurrent appearance, these words are considered to have minimal practical significance within the scope of our analysis. This is primarily due to their ubiquity and the negligible unique semantic contribution they make. For these reasons, this study excludes these two types of words-stop words and high-frequency yet semantically non-contributing words from our word frequency statistics. All these models aim to provide numerical representations of words that capture their meanings.

Types of Semantics

Thus, machines tend to represent the text in specific formats in order to interpret its meaning. This formal structure that is used to understand the meaning of a text is called meaning representation. Lexical and syntactic processing doesn’t suffice when it comes to building advanced NLP applications such as language translation, chatbots, etc.

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. As mentioned earlier, the factors contributing to these differences can be multi-faceted and are worth exploring further. The x-axis represents the sentence numbers from the corpus, with sentences taken as an example due to space limitations. For each sentence number on the x-axis, a corresponding semantic similarity value is generated by each algorithm.

It goes beyond the surface-level analysis of words and their grammatical structure (syntactic analysis) and focuses on deciphering the deeper layers of language comprehension. The first part of semantic analysis, studying the meaning of individual words is called lexical semantics. It includes words, sub-words, affixes (sub-units), compound words and phrases also. In other words, we can say that lexical semantics is the relationship between lexical items, meaning of sentences and syntax of sentence.

That is why the job, to get the proper meaning of the sentence, of semantic analyzer is important. Keeping the advantages of natural language processing in mind, let’s explore how different industries are applying this technology. We can any of the below two semantic analysis techniques depending on the type of information you would like to obtain from the given data. As we discussed, the most important task of semantic analysis is to find the proper meaning of the sentence. The earliest decision trees, producing systems of hard if–then rules, were still very similar to the old rule-based approaches.

By analyzing the words and phrases that users type into the search box the search engines are able to figure out what people want and deliver more relevant responses. We import all the required libraries and tokenize the sample text contained in the text variable, into individual words which are stored in a list. The main difference between them is that in polysemy, the meanings of the words are related but in homonymy, the meanings of the words are not related. For example, if we talk about the same word “Bank”, we can write the meaning ‘a financial institution’ or ‘a river bank’. In that case it would be the example of homonym because the meanings are unrelated to each other.

Lemmatization is a more sophisticated or intelligent technique as it doesn’t just chop off the suffix of a word. Instead, it takes an input word and searches for its base word by going recursively through all the variations of dictionary words. In our example we have considered four text messages that’s why we see four rows in the dataset and columns represent the vocabulary of the four text messages. After having the words with us we need to generate useful features on which we can train the model, as the models need numerical columns to predict the results for our example of text classification. As you can see in the example above nltk has a stopword collection list in the English language that we are using to remove the stopwords from the corpus. It has been observed that when we have a text corpus and frequency distribution is plotted, it follows Zipf’s Law also called Zipf’s Distribution.

Representing variety at the lexical level

There is a better representation called TF-IDF representation where TF stands for Term Frequency and IDF stands for Inverse Document Frequency. In our spam detector example, we will break each message into different words, it’s called word tokenization. Similarly, we have other types of tokenization techniques such as character tokenization, sentence tokenization, etc. The following section will explore the practical tools and libraries available for semantic analysis in NLP.

• However, translations by Jennings present fewer instances in the highly similar intervals of 95–100% (1%) and 90–95% (14%).
• As semantic analysis evolves, it holds the potential to transform the way we interact with machines and leverage the power of language understanding across diverse applications.
• The first category consists of core conceptual words in the text, which embody cultural meanings that are influenced by a society’s customs, behaviors, and thought processes, and may vary across different cultures.
• Researchers and practitioners are working to create more robust, context-aware, and culturally sensitive systems that tackle human language’s intricacies.
• Let us look at the TF-IDF representation of the same text message example that we have seen earlier.

Semantic analysis is the process of understanding the meaning and interpretation of words, signs and sentence structure. I say this partly because semantic analysis is one of the toughest parts of natural language processing and it’s not fully solved yet. By leveraging these techniques, NLP systems can gain a deeper understanding of human language, making them more versatile and capable of handling various tasks, from sentiment analysis to machine translation and question answering.

The semantic analysis creates a representation of the meaning of a sentence. But before deep dive into the concept and approaches related to meaning representation, firstly we have to understand the building blocks of the semantic system. Semantics, the study of meaning, is central to research in Natural Language Processing (NLP) and many other fields connected to Artificial Intelligence. We review the state of computational semantics in NLP and investigate how different lines of inquiry reflect distinct understandings of semantics and prioritize different layers of linguistic meaning.

• Whether translations adopt a simplified or literal approach, readers stand to benefit from understanding the structure and significance of ancient Chinese names prior to engaging with the text.
• For example, “run” and “jog” are synonyms, as are “happy” and “joyful.” Using synonyms is an important tool for NLP applications, as it can help determine the intended meaning of a sentence, even if the words used are not exact.
• NLP can also be trained to pick out unusual information, allowing teams to spot fraudulent claims.
• For instance, the word “bat” can mean a flying mammal or sports equipment.
• By leveraging these techniques, NLP systems can gain a deeper understanding of human language, making them more versatile and capable of handling various tasks, from sentiment analysis to machine translation and question answering.
• He is an innovative team leader with data wrangling out-of-the-box capabilities such as outlier treatment, data discovery, data transformation with a focus on yielding high-quality results.

Semantics is the study of meaning, but it’s also the study of how words connect to other aspects of language. For example, when someone says, “I’m going to the store,” the word “store” is the main piece of information; it tells us where the person is going. The word “going” tells us how the person gets there (by walking, riding in a car, or other means). 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. And if companies need to find the best price for specific materials, natural language processing can review various websites and locate the optimal price.

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