2402 01495 A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation

Instead, this study focuses on a specific machine learning task, namely text classification, exploring the effect of semantic augmentation on deep neural models to the classification performance. Our worked is focused on the feature level, applying semantic enrichment on the input space of the classification process. We separate the embedding generation from the semantic enrichment phase, as in Faruqui et al. (Reference Faruqui, Dodge, Jauhar, Dyer, Hovy and Smith2015), where the semantic augmentation can be applied as a post-processing step. In fact, we model the semantic content as a separate representation of the input data that can be combined with a variety of embeddings, features, and classifiers. We also expand our investigation to additional semantic extraction and disambiguation approaches, by considering the effect of the n-th degree hypernymy relations and of several context semantic embedding methods.

This mapping shows that there is a lack of studies considering languages other than English or Chinese. The low number of studies considering other languages suggests that there is a need for construction or expansion of language-specific resources (as discussed in “External knowledge sources” section). These resources can be used for enrichment of texts and for the development of language specific methods, based on natural language processing. The second most frequent identified application domain is the mining of web texts, comprising web pages, blogs, reviews, web forums, social medias, and email filtering [41–46].

A tutorial survey of architectures, algorithms, and applications for deep learning

If we have only two variables to start with then the feature space (the data that we’re looking at) can be plotted anywhere in this space that is described by these two basis vectors. Now moving to the right in our diagram, the matrix M is applied to this vector space and this transforms it into the new, transformed space in our top right corner. In the diagram below the geometric effect of M would be referred to as “shearing” the vector space; the two vectors 𝝈1 and 𝝈2 are actually our singular values plotted in this space. The extra dimension that wasn’t available to us in our original matrix, the r dimension, is the amount of latent concepts. Generally we’re trying to represent our matrix as other matrices that have one of their axes being this set of components. You will also note that, based on dimensions, the multiplication of the 3 matrices (when V is transposed) will lead us back to the shape of our original matrix, the r dimension effectively disappearing.

Adding text analysis and NLP to your technology stack can be an incredible boost to the business intelligence and data analysis work done for your business. Whether it’s through APIs or a user-friendly software, it’s possible to build out applications of this across many different industries including research, healthcare, filmmaking, retail and SAAS to name a few. If you work with industry-specific vocabulary or are looking to identify uncommon terms or topics, it’s easy to set up our system to do that for you. Simply create custom categories and populate them with the terms you would be looking to identify, and our system will do the rest across all your media files.

Free Text Analysis Tools Value Proposition

This integration could enhance the analysis by leveraging more advanced semantic processing capabilities from external tools. 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. QuestionPro often includes text analytics features that perform sentiment analysis on open-ended survey responses. While not a full-fledged semantic analysis tool, it can help understand the general sentiment (positive, negative, neutral) expressed within the text. Moreover, while these are just a few areas where the analysis finds significant applications.

Relationship extraction is a procedure used to determine the semantic relationship between words in a text. In semantic analysis, relationships include various entities, such as an individual’s name, place, company, designation, etc. Moreover, semantic categories such as, ‘is the chairman of,’ ‘main branch located a’’, ‘stays at,’ and others connect the above entities. Apart from these vital elements, the semantic analysis also uses semiotics and collocations to understand and interpret language. Semiotics refers to what the word means and also the meaning it evokes or communicates. For example, ‘tea’ refers to a hot beverage, while it also evokes refreshment, alertness, and many other associations.

Higher-order naive Bayes: A novel non-IID approach to text classification

An experimental evaluation over the BBC, 20-Newsgroups, and Ohsumed datasets shows that their approach introduces significant benefits in terms of F1-score, consistently improving the lexical embedding baseline on randomly initialized vectors. This is attributed to the short document sizes and the lack of word ambiguity in the examined datasets. Experiments over a US immigration dataset show that this approach outperforms supervised latent dirichlet allocation (LDA) (Mcauliffe and Blei Reference Mcauliffe and Blei2008) on document classification.

This approach avoids the common problem of extreme feature sparsity and mitigates the curse of dimensionality that usually plagues shallow representations. Stavrianou et al. [15] present a survey of semantic issues of text mining, which are originated from natural language particularities. This is a good survey focused on a linguistic point of view, rather than focusing only on statistics.

Semantic analysis and self-service work hand in hand to empower users

As an example, explicit semantic analysis [129] rely on Wikipedia to represent the documents by a concept vector. In a similar way, Spanakis et al. [125] improved hierarchical clustering quality by using a text representation based on concepts and other Wikipedia features, such as links and categories. Whether using machine learning or statistical techniques, the text mining approaches are usually language independent. However, specially in the natural text semantic analysis language processing field, annotated corpora is often required to train models in order to resolve a certain task for each specific language (semantic role labeling problem is an example). Besides, linguistic resources as semantic networks or lexical databases, which are language-specific, can be used to enrich textual data. Thus, the low number of annotated data or linguistic resources can be a bottleneck when working with another language.

The application of semantic analysis methods generally streamlines organizational processes of any knowledge management system. Academic libraries often use a domain-specific application to create a more efficient organizational system. By classifying scientific publications using semantics and Wikipedia, researchers are helping people find resources faster. Search engines like Semantic Scholar provide organized access to millions of articles.

It is extensively applied in medicine, as part of the evidence-based medicine [5]. This type of literature review is not as disseminated in the computer science field as it is in the medicine and health care fields1, although computer science researches can also take advantage of this type of review. We can find important reports on the use of systematic reviews specially in the software engineering community [3, 4, 6, 7]. Other sparse initiatives can also be found in other computer science areas, as cloud-based environments [8], image pattern recognition [9], biometric authentication [10], recommender systems [11], and opinion mining [12]. In this step, raw text is transformed into some data representation format that can be used as input for the knowledge extraction algorithms.

• With line-by-line sentiment analysis as well, it’s possible to code every project accordingly and end up with a final result that is easy to extract insights from.
• WordNet consists of a graph, where each node is a set of word senses (called synonymous sets or synsets) representing the same approximate meaning, with each sense also conveying part-of-speech (POS) information.
• This section presents a summary comparison with respect to a number of key criteria.
• The multi-context cluster-based approach underperforms all other configurations.

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A number of patterns and explanations in these errors are identified by a manual analysis of the results, hereby outlined by selected examples. For each instance we illustrate the true label, the wrong prediction made by our system, and indicative segments found in the instance text. We can arrive at the same understanding of PCA if we imagine that our matrix M can be broken down into a weighted sum of separable matrices, as shown below. Semantic analysis, on the other hand, is crucial to achieving a high level of accuracy when analyzing text. 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. It represents the relationship between a generic term and instances of that generic term.

All factors considered, Uber uses semantic analysis to analyze and address customer support tickets submitted by riders on the Uber platform. 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 streamline and optimize the map data triggering the ticket. Moreover, granular insights derived from the text allow teams to identify the areas with loopholes and work on their improvement on priority.