Semantic Textual Similarity From Jaccard to OpenAI, implement the by Marie Stephen Leo

From optimizing data-driven strategies to refining automated processes, semantic analysis serves as the backbone, transforming how machines comprehend language and enhancing human-technology interactions. Words are treated as string sequences in these kinds of textual data representations. The main logic behind the algorithms in this category depends on a word/character sequence taken out from documents by ordinary string-matching method. N-gram based demonstration (Cavnar & Trenkle, 1994) and similar works in Ho and Funakoshi (1998), Ho and Nguyen (2000) and Fung (2003) are traditional examples of these types of systems. 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.

Its results were based on 1693 studies, selected among 3984 studies identified in five digital libraries. The produced mapping gives a general summary of the subject, points some areas that lacks the development of primary or secondary studies, and can be a guide for researchers working with semantics-concerned text mining. It demonstrates that, although several studies have been developed, the processing of semantic aspects in text mining remains an open research problem. Text semantics are frequently addressed in text mining studies, since it has an important influence in text meaning.

What is natural language processing?

In this subsection, we present a consolidation of our results and point some future trends of semantics-concerned text mining. When the field of interest is broad and the objective is to have an overview of what is being developed in the research field, it is recommended to apply a particular type of systematic review named systematic mapping study [3, 4]. Systematic mapping studies follow an well-defined protocol as in any systematic review.

The results of the systematic mapping study is presented in the following subsections. We start our report presenting, in the “Surveys” section, a discussion about the eighteen secondary studies (surveys and reviews) that were identified in the systematic mapping. In the “Systematic mapping summary and future trends” section, we present a consolidation of our results and point some gaps of both primary and secondary studies. In the literature, numerous studies leverage semantic knowledge to augment text mining tasks. For classification, graph-based semantic resources such as the WordNet ontology (Miller Reference Miller1995) have been widely used to enrich textual information.

APSIPA Transactions on Signal and Information Processing

In the pattern extraction step, user’s participation can be required when applying a semi-supervised approach. In the post-processing step, the user can evaluate the results according to the expected knowledge usage. Besides the vector space model, there are text representations based on networks (or graphs), which can make use of some text semantic features. Network-based representations, such as bipartite networks and co-occurrence networks, can represent relationships between terms or between documents, which is not possible through the vector space model [147, 156–158].

• Understanding Natural Language might seem a straightforward process to us as humans.
• Synset nodes are connected to neighbors through a variety of relations of lexical and semantic nature (e.g., is-a relations like hypernymy and hyponymy, part-of relations such as meronymy, and others).
• Ontologies can be used as background knowledge in a text mining process, and the text mining techniques can be used to generate and update ontologies.
• A variety of semantic selection and combination strategies are explored, along with a supervised feature selection phase that is based on the chi-squared statistic.

Table 13 presents the experimental results over the additional datasets for the two main baselines, our best-performing configuration as well as top performers from Table 12. The synsets extracted with this process are annotated with weights inversely proportional to the distance of the hypernymy level from the original synset. This weight decay is applied to diminish the contribution of general and/or abstract synsets, which are expected to be encountered frequently, thus saturating the final semantic vector. We extract information from WordNet via the natural language toolkit (NLTK) interface.Footnote e Its API supports the retrieval of a collection of synsets as possible semantic candidates for an input word. It also allows traversal of the WordNet graph via the synset relation links mentioned above.

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The analysis can segregate tickets based on their content, such as map data-related issues, and deliver them to the respective teams to handle. The platform allows Uber to semantic text analysis streamline and optimize the map data triggering the ticket. Relationship extraction is a procedure used to determine the semantic relationship between words in a text.

This field of research combines text analytics and Semantic Web technologies like RDF. The use of Wikipedia is followed by the use of the Chinese-English knowledge database HowNet [82]. Finding HowNet as one of the most used external knowledge source it is not surprising, since Chinese is one of the most cited languages in the studies selected in this mapping (see the “Languages” section). As well as WordNet, HowNet is usually used for feature expansion [83–85] and computing semantic similarity [86–88]. Schiessl and Bräscher [20] and Cimiano et al. [21] review the automatic construction of ontologies.

Chinese language is the second most cited language, and the HowNet, a Chinese-English knowledge database, is the third most applied external source in semantics-concerned text mining studies. Looking at the languages addressed in the studies, we found that there is a lack of studies specific to languages other than English or Chinese. We also found an expressive use of WordNet as an external knowledge source, followed by Wikipedia, HowNet, Web pages, SentiWordNet, and other knowledge sources related to Medicine. 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. Latent semantic analysis (sometimes latent semantic indexing), is a class of techniques where documents are represented as vectors in term space.

Moreover, context is equally important while processing the language, as it takes into account the environment of the sentence and then attributes the correct meaning to it. Semantic analysis helps in processing customer queries and understanding their meaning, thereby allowing an organization to understand the customer’s inclination. Moreover, analyzing customer reviews, feedback, or satisfaction surveys helps understand the overall customer experience by factoring in language tone, emotions, and even sentiments. When combined with machine learning, semantic analysis allows you to delve into your customer data by enabling machines to extract meaning from unstructured text at scale and in real time. Additionally, we utilize the Reuters-21578Footnote h dataset, which contains news articles that appeared on the Reuters financial newswire in 1987 and are commonly used for text classification evaluation. Using the traditional “ModApte” variant, the corpus comprises 9584 and 3744 training and test documents, respectively, with a labelset of 90 classes.

As previously stated, the objective of this systematic mapping is to provide a general overview of semantics-concerned text mining studies. The papers considered in this systematic mapping study, as well as the mapping results, are limited by the applied search expression and the research questions. Therefore, the reader can miss in this systematic mapping report some previously known studies. It is not our objective to present a detailed survey of every specific topic, method, or text mining task. 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.