Semantic, Pragmatic and Discourse Analysis SpringerLink

With the help of meaning representation, we can represent unambiguously, canonical forms at the lexical level. After the selection phase, 1693 studies were accepted for the information extraction phase. In this phase, information about each study was extracted mainly based on the abstracts, although some information was extracted from the full text.

Finally, we augment the word embedding output representations by the semantic vector, feeding the resulting enriched, hybrid representation to a deep neural network (DNN) classifier. A text classifier is expected to label textual documents with pre-determined classes with an obvious assumption that each class consist of similar documents, usually talking about a particular topic that is different from the topics of other classes. However, vector space demonstration of texts usually results in high dimensionality and consequently high sparsity. This is a big difficulty especially when there are numerous class labels but inadequate training data for each of them. Obtaining labeled quality data for training is usually very expensive in real world applications. Accordingly, an accurate text classifier should have the capability of using this semantic information.

Search Engines:

We can note that the most common approach deals with latent semantics through Latent Semantic Indexing (LSI) [2, 120], a method that can be used for data dimension reduction and that is also known as latent semantic analysis. In this semantic space, alternative forms expressing the same concept are projected to a common representation. It reduces the noise caused by synonymy and polysemy; thus, it latently deals with text semantics. Another technique in this direction that is commonly used for topic modeling is latent Dirichlet allocation (LDA) [121]. The topic model obtained by LDA has been used for representing text collections as in [58, 122, 123]. 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.

Similarly to the 20-Newsgroups dataset case, we move on to the error analysis, with Figure 9(a) depicting the confusion matrix with the misclassified instances (i.e., diagonal entries are omitted). For better visualization, it illustrates only the 26 classes with at least 20 samples, due to the large number of classes in the Reuters dataset. We observe that the misclassification occurrences are more frequent, but less intense than those in the 20-Newsgroup dataset. Additionally, Figure 9(b) depicts the label-wise performance of our best configuration.

An analysis of topical coverage of Wikipedia

Example of the disambiguation phase of the context-embedding disambiguation strategy. A candidate word is mapped to its embedding representation and compared to the list of available synset vectors. The synset with the vector representation closest to the word embedding is selected.

Google incorporated ‘semantic analysis’ into its framework by developing its tool to understand and improve user searches. The Hummingbird algorithm was formed in 2013 and helps analyze user intentions as and when they use the google search engine. As a result of Hummingbird, results are shortlisted based on the ‘semantic’ relevance of the keywords. Moreover, it also plays a crucial role in offering SEO benefits to the company. Automated semantic analysis works with the help of machine learning algorithms.

Text Representation

The protocol is developed when planning the systematic review, and it is mainly composed by the research questions, the strategies and criteria for searching for primary studies, study selection, and data extraction. The protocol is a documentation of the review process and must have all the information needed to perform the literature review in a systematic way. The analysis of selected studies, which is performed in the data extraction phase, will provide the answers to the research questions that motivated the literature review.

• Additionally, Figure 8(b) depicts the label-wise performance for the best-performing configuration.
• 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.
• Adding more preprocessing steps would help us cleave through the noise that words like “say” and “said” are creating, but we’ll press on for now.
• Calculating the outer product of two vectors with shapes (m,) and (n,) would give us a matrix with a shape (m,n).
• The results of the systematic mapping study is presented in the following subsections.
• Wimalasuriya and Dou [17], Bharathi and Venkatesan [18], and Reshadat and Feizi-Derakhshi [19] consider the use of external knowledge sources (e.g., ontology or thesaurus) in the text mining process, each one dealing with a specific task.

To ensure adequate word context for generating representative semantic embeddings, we discard all synsets with fewer than 25 context words. The synset vector computation process from the whole WordNet, which is illustrated in Figure 4, results in 753 adequately represented synsets. A bulk of later works modify the deep neural embedding training, with many of them investigating ways of introducing both distributional and relational information into word embeddings. Distributional information pertain text semantic analysis to statistics from the context of a word, while relational information utilizes semantic relationships such as synonymy and hypernymy. The resulting model trains faster and performs better than the bag-of-words baselines, but worse than the neural language model of Bengio et al. (Reference Bengio, Ducharme, Vincent and Jauvin2003). Semantic analysis stands as the cornerstone in navigating the complexities of unstructured data, revolutionizing how computer science approaches language comprehension.

A corpus-based semantic kernel for text classification by using meaning values of terms

Hotel Atlantis has thousands of reviews and 326 of them are included in the OpinRank Review Dataset. Elsewhere we showed how semantic search platforms, like Vectara Neural Search, allow organizations to leverage information stored as unstructured text — unlocking the value in these datasets on a large scale. An uncommon but equally important use case of text analysis and NLP can be for knowledge management and recall.

Semantic analysis is a crucial component of natural language processing (NLP) that concentrates on understanding the meaning, interpretation, and relationships between words, phrases, and sentences in a given context. It goes beyond merely analyzing a sentence’s syntax (structure and grammar) and delves into the intended meaning. In this model, each document is represented by a vector whose dimensions correspond to features found in the corpus. When features are single words, the text representation is called bag-of-words.

Moreover, they don’t just parse text; they extract valuable information, discerning opposite meanings and extracting relationships between words. Efficiently working behind the scenes, semantic analysis excels in understanding language and inferring intentions, emotions, and context. 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.