Understanding Semantic Analysis Using Python - NLP Towards AI

Bos [31] indicates machine learning, knowledge resources, and scaling inference as topics that can have a big impact on computational semantics in the future. Wimalasuriya and Dou [17] present a detailed literature review of ontology-based information extraction. Bharathi and Venkatesan [18] present a brief description of several studies that use external knowledge sources as background knowledge for document clustering.

Where there would be originally r number of u vectors; 5 singular values and n number of 𝑣-transpose vectors. This technique is used separately or can be used along with one of the above methods to gain more valuable insights. To learn more and launch your own customer self-service project, get in touch with our experts today. As such, Cdiscount was able to implement actions aiming to reinforce the conditions around product returns and deliveries (two criteria mentioned often in customer feedback). Since then, the company enjoys more satisfied customers and less frustration. For example, the top 5 most useful feature selected by Chi-square test are “not”, “disappointed”, “very disappointed”, “not buy” and “worst”.

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. IBM’s Watson provides a conversation service that uses semantic analysis (natural language understanding) and deep learning to derive meaning from unstructured data.

Topic Modeling

It involves words, sub-words, affixes (sub-units), compound words, and phrases also. This article is part of an ongoing blog series on Natural Language Processing (NLP). I hope after reading that article you can understand the power of NLP in Artificial Intelligence. So, in this part of this series, we will start our discussion on Semantic analysis, which is a level of the NLP tasks, and see all the important terminologies or concepts in this analysis.

Likewise, the word ‘rock’ may mean ‘a stone‘ or ‘a genre of music‘ – hence, the accurate meaning of the word is highly dependent upon its context and usage in the text. Hence, under Compositional Semantics Analysis, we try to understand how combinations of individual words form the meaning of the text. This is often accomplished by locating and extracting the key ideas and connections found in the text utilizing algorithms and AI approaches.

LSA for Exploratory Data Analysis (EDA)

Schiessl and Bräscher [20] and Cimiano et al. [21] review the automatic construction of ontologies. Schiessl and Bräscher [20], the only identified review written in Portuguese, formally define the term ontology and discuss the automatic building of ontologies from texts. The authors state that automatic ontology building from texts is the way to the timely production of ontologies for current applications and that many questions are still open in this field.

Natural Language Processing or NLP is a branch of computer science that deals with analyzing spoken and written language. Advances in NLP have led to breakthrough innovations such as chatbots, automated content creators, summarizers, and sentiment analyzers. The field’s ultimate goal is to ensure that computers understand and process language as well as humans. 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. The semantic analysis creates a representation of the meaning of a sentence.

Using our latent components in our modelling task

Now just to be clear, determining the right amount of components will require tuning, so I didn’t leave the argument set to 20, but changed it to 100. You might think that’s still a large number of dimensions, but our original was 220 (and that was with constraints on our minimum document frequency!), so we’ve reduced a sizeable chunk of the data. I’ll explore in another post how to choose the optimal number of singular values. 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.

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. 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. The accuracy of the summary depends on a machine’s ability to understand language data. While, as humans, it is pretty simple for us to understand the meaning of textual information, it is not so in the case of machines.

The idea of entity extraction is to identify named entities in text, such as names of people, companies, places, etc. In Sentiment analysis, our aim is to detect the emotions as positive, negative, or neutral in a text to denote urgency. 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. That is why the task to get the proper meaning of the sentence is important.

The approach helps deliver optimized and suitable content to the users, thereby boosting traffic and improving result relevance. You can foun additiona information about ai customer service and artificial intelligence and NLP. Text mining initiatives can get some advantage by using external sources of knowledge. Thesauruses, taxonomies, ontologies, and semantic networks are knowledge sources that are commonly used by the text mining community.

The authors compare 12 semantic tagging tools and present some characteristics that should be considered when choosing such type of tools. Stavrianou et al. [15] also present the relation between ontologies and text mining. 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. Powerful semantic-enhanced machine learning tools will deliver valuable insights that drive better decision-making and improve customer experience. Automated semantic analysis works with the help of machine learning algorithms.

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.

However, there is a lack of secondary studies that consolidate these researches. This paper reported a systematic mapping study conducted to overview semantics-concerned text mining literature. The scope of this mapping is wide (3984 papers matched the search expression).

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. A systematic review is performed in order to answer a research question and must follow a defined protocol. 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.

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. As text semantics has an important role in text meaning, the term semantics has been seen in a vast sort of text mining studies.

By sticking to just three topics we’ve been denying ourselves the chance to get a more detailed and precise look at our data. This article assumes some understanding of basic NLP preprocessing and of word vectorisation (specifically tf-idf vectorisation). With the help of semantic analysis, machine learning tools can recognize a ticket either as a “Payment issue” or a“Shipping problem”. It is the first part of semantic analysis, in which we study the meaning of individual words.

The activities performed in the pre-processing step are crucial for the success of the whole text mining process. The data representation must preserve the patterns hidden in the documents in a way that they can be discovered in the next step. In the pattern extraction step, the analyst applies a suitable algorithm to extract the hidden patterns. The algorithm is chosen based on the data available and the type of pattern that is expected.

ML & Data Science

As the field of ML continues to evolve, it’s anticipated that machine learning tools and its integration with semantic analysis will yield even more refined and accurate insights into human language. Thanks to machine learning and natural language processing (NLP), semantic analysis includes the work of reading and sorting relevant interpretations. Artificial intelligence contributes to providing better solutions to customers when they contact customer service. These proposed solutions are more precise and help to accelerate resolution times.

The selection and the information extraction phases were performed with support of the Start tool [13]. While semantic analysis is more modern and sophisticated, it is also expensive to implement. A strong grasp of semantic analysis helps firms improve their communication with customers without needing to talk much. You see, the word on its own matters less, and the words surrounding it matter more for the interpretation.

• Its results were based on 1693 studies, selected among 3984 studies identified in five digital libraries.
• SentiWordNet, a lexical resource for sentiment analysis and opinion mining, is already among the most used external knowledge sources.
• Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive
  positive feedback from the reviewers.
• 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.

A semantic analysis algorithm needs to be trained with a larger corpus of data to perform better. That leads us to the need for something better and more sophisticated, i.e., Semantic Analysis. TruncatedSVD will return it to as a numpy array of shape (num_documents, num_components), so we’ll turn it into a Pandas dataframe for ease of manipulation. Note that LSA is an unsupervised learning technique — there is no ground truth. In the dataset we’ll use later we know there are 20 news categories and we can perform classification on them, but that’s only for illustrative purposes.

A Survey of Semantic Analysis Approaches

Health care and life sciences is the domain that stands out when talking about text semantics in text mining applications. This fact is not unexpected, since life sciences have a long time concern about standardization of vocabularies and taxonomies. Among the most common problems treated through the use of text mining in the health care and life science is the information retrieval from publications of the field.

Methods that deal with latent semantics are reviewed in the study of Daud et al. [16]. The authors present a chronological analysis from 1999 to 2009 of directed probabilistic topic models, such as probabilistic latent semantic analysis, latent Dirichlet allocation, and their extensions. In this step, raw text is transformed into some data representation format that can be used as input for the knowledge extraction algorithms.

Therefore, it is not a proper representation for all possible text mining applications. The distribution of text mining tasks identified in this literature mapping is presented in Fig. Classification corresponds to the task of finding a model from examples with known classes (labeled instances) in order to predict the classes of new examples. On the other hand, clustering is the task of grouping examples (whose classes are unknown) based on their similarities. Classification was identified in 27.4% and clustering in 17.0% of the studies.

The Latent Semantic Index low-dimensional space is also called semantic space. 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].

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]. Specifically for the task of irony detection, Wallace [23] presents both philosophical formalisms and machine learning approaches. The author argues that a model of the speaker is necessary to improve current machine learning methods and enable their application in a general problem, independently of domain. He discusses the gaps of current methods and proposes a pragmatic context model for irony detection.

Suppose that we have some table of data, in this case text data, where each row is one document, and each column represents a term (which can be a word or a group of words, like “baker’s dozen” or “Downing Street”). This is the standard way to represent text data (in a document-term matrix, as shown in Figure 2). The numbers in the table reflect how important that word is in the document. If the number is zero then that word simply doesn’t appear in that document. With the help of meaning representation, we can represent unambiguously, canonical forms at the lexical level.

It’ll often be the case that we’ll use LSA on unstructured, unlabelled data. Latent Semantic Analysis (LSA) is a popular, dimensionality-reduction techniques that follows the same method as Singular Value Decomposition. LSA ultimately reformulates text data in terms of r latent (i.e. hidden) features, where r is less than m, the number of terms in the data. I’ll explain the conceptual and mathematical intuition and run a basic implementation in Scikit-Learn using the 20 newsgroups dataset.

It allows computers to understand and interpret sentences, paragraphs, or whole documents, by analyzing their grammatical structure, and identifying relationships between individual words in a particular context. Conversational chatbots have come a long way from rule-based text semantic analysis systems to intelligent agents that can engage users in almost human-like conversations. The application of semantic analysis in chatbots allows them to understand the intent and context behind user queries, ensuring more accurate and relevant responses.

• As examples of semantics-related subjects, we can mention representation of meaning, semantic parsing and interpretation, word sense disambiguation, and coreference resolution.
• It then identifies the textual elements and assigns them to their logical and grammatical roles.
• Accuracy has dropped greatly for both, but notice how small the gap between the models is!
• We will calculate the Chi square scores for all the features and visualize the top 20, here terms or words or N-grams are features, and positive and negative are two classes.
• I’ll explain the conceptual and mathematical intuition and run a basic implementation in Scikit-Learn using the 20 newsgroups dataset.
• 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.

Companies, organizations, and researchers are aware of this fact, so they are increasingly interested in using this information in their favor. Some competitive advantages that business can gain from the analysis of social media texts are presented in [47–49]. The authors developed case studies demonstrating how text mining can be applied in social media intelligence.