Understanding Semantic Analysis NLP

It may offer functionalities to extract keywords or themes from textual responses, thereby aiding in understanding the primary topics or concepts discussed within the provided text. Semantic analysis employs various methods, but they all aim to comprehend the text’s meaning in a manner comparable to that of a human. This can entail figuring out the text’s primary ideas and themes and their connections. It helps understand the true meaning of words, phrases, and sentences, leading to a more accurate interpretation of text. Indeed, discovering a chatbot capable of understanding emotional intent or a voice bot’s discerning tone might seem like a sci-fi concept.

The use of features based on WordNet has been applied with and without good results [55, 67–69]. Besides, WordNet can support the computation of semantic similarity [70, 71] and the evaluation of the discovered knowledge [72]. In this step, raw text is transformed into some data representation format that can be used as input for the knowledge extraction algorithms. The activities performed in the pre-processing step are crucial for the success of the whole text mining process.

Word

Nevertheless, we believe that our limitations do not have a crucial impact on the results, since our study has a broad coverage. Text classification and text clustering, as basic text mining tasks, are frequently applied in semantics-concerned text mining researches. Among other more specific tasks, sentiment analysis is a recent research field that is almost as applied as information retrieval and information extraction, which are more consolidated research areas. SentiWordNet, a lexical resource for sentiment analysis and opinion mining, is already among the most used external knowledge sources.

• To ensure adequate word context for generating representative semantic embeddings, we discard all synsets with fewer than 25 context words.
• For example, semantic analysis can generate a repository of the most common customer inquiries and then decide how to address or respond to them.
• Indeed, discovering a chatbot capable of understanding emotional intent or a voice bot’s discerning tone might seem like a sci-fi concept.
• This systematic mapping is a starting point, and surveys with a narrower focus should be conducted for reviewing the literature of specific subjects, according to one’s interests.
• Therefore, it is not a proper representation for all possible text mining applications.

The combined approach yields the best results for both datasets; however, (a) it uses handcrafted features for the representation of textual information and (b) it employs shallow methods for classification, and (c) it considers subsets of the two datasets. Early attempts produce shallow vector space features to represent text elements, such as words and documents, via histogram-based methods (Katz Reference Katz1987; Salton and Buckley Reference Salton and Buckley1988; Joachims Reference Joachims1998). In these cases, latent topics are inferred to form a new, efficient representation space for text. Regarding neural approaches, a neural language model applied on word sequences is used in Bengio et al. (Reference Bengio, Ducharme, Vincent and Jauvin2003) to jointly learn word embeddings and the probability function of the input word collection.

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Semantic analysis techniques involve extracting meaning from text through grammatical analysis and discerning connections between words in context. This process empowers computers to interpret words and entire passages or documents. Word sense disambiguation, a vital aspect, helps determine multiple meanings of words. This proficiency goes beyond comprehension; it drives data analysis, guides customer feedback strategies, shapes customer-centric approaches, automates processes, and deciphers unstructured text. In semantic analysis with machine learning, computers use word sense disambiguation to determine which meaning is correct in the given context. Only the 300-dimensional pre-trained word2vec surpasses the “embedding-only” baseline.

Grobelnik [14] states the importance of an integration of these research areas in order to reach a complete solution to the problem of text understanding. In AI and machine learning, semantic analysis helps in feature extraction, sentiment analysis, and understanding relationships in data, which enhances the performance of models. In semantic analysis, word sense disambiguation refers to an automated process of determining the sense or meaning of the word in a given context.

However, there is a lack of secondary studies that consolidate these researches. This paper reported a systematic mapping study conducted to overview semantics-concerned semantic text analysis text mining literature. Thus, due to limitations of time and resources, the mapping was mainly performed based on abstracts of papers.

Earlier, tools such as Google translate were suitable for word-to-word translations. However, with the advancement of natural language processing and deep learning, translator tools can determine a user’s intent and the meaning of input words, sentences, and context. Semantic analysis analyzes the grammatical format of sentences, including the arrangement of words, phrases, and clauses, to determine relationships between independent terms in a specific context. It is also a key component of several machine learning tools available today, such as search engines, chatbots, and text analysis software.

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. Now, we have a brief idea of meaning representation that shows how to put together the building blocks of semantic systems. In other words, it shows how to put together entities, concepts, relations, and predicates to describe a situation. As we discussed, the most important task of semantic analysis is to find the proper meaning of the sentence. Consider the task of text summarization which is used to create digestible chunks of information from large quantities of text. Text summarization extracts words, phrases, and sentences to form a text summary that can be more easily consumed.

With the help of meaning representation, we can link linguistic elements to non-linguistic elements. Both polysemy and homonymy words have the same syntax or spelling but 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. In other words, we can say that polysemy has the same spelling but different and related meanings. In the above sentence, the speaker is talking either about Lord Ram or about a person whose name is Ram. If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples. Semantic analysis enables these systems to comprehend user queries, leading to more accurate responses and better conversational experiences.

Cdiscount, an online retailer of goods and services, uses semantic analysis to analyze and understand online customer reviews. When a user purchases an item on the ecommerce site, they can potentially give post-purchase feedback for their activity. This allows Cdiscount to focus on improving by studying consumer reviews and detecting their satisfaction or dissatisfaction with the company’s products.

All in all, semantic analysis enables chatbots to focus on user needs and address their queries in lesser time and lower cost. 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. We now interpret the experimental findings in relation to the research questions posed in Section 1 and compare our approach with the state of the art in the field.

String Similarity

Jovanovic et al. [22] discuss the task of semantic tagging in their paper directed at IT practitioners. Semantic tagging can be seen as an expansion of named entity recognition task, in which the entities are identified, disambiguated, and linked to a real-world entity, normally using a ontology or knowledge base. The authors compare 12 semantic tagging tools and present some characteristics that should be considered when choosing such type of tools. Therefore, the goal of semantic analysis is to draw exact meaning or dictionary meaning from the text. Understanding these terms is crucial to NLP programs that seek to draw insight from textual information, extract information and provide data. It is also essential for automated processing and question-answer systems like chatbots.

• We enrich word2vec embeddings with the resulting semantic vector through concatenation or replacement and apply the semantically augmented word embeddings on the classification task via a DNN.
• Hence, it is critical to identify which meaning suits the word depending on its usage.
• In these cases, latent topics are inferred to form a new, efficient representation space for text.
• Semantic Analysis is a subfield of Natural Language Processing (NLP) that attempts to understand the meaning of Natural Language.
• It analyzes text to reveal the type of sentiment, emotion, data category, and the relation between words based on the semantic role of the keywords used in the text.
• Surprisingly enough, retrofitting the embeddings consistently results in inferior performance, both for the pre-trained ones and for those fitted from scratch.

Although our mapping study was planned by two researchers, the study selection and the information extraction phases were conducted by only one due to the resource constraints. In this process, the other researchers reviewed the execution of each systematic mapping phase and their results. Secondly, systematic reviews usually are done based on primary studies only, nevertheless we have also accepted secondary studies (reviews or surveys) as we want an overview of all publications related to the theme.