What is semantic analysis?
Semantic analysis is a sub-task of NLP. It uses machine learning and NLP to understand the real context of natural language. Search engines and chatbots use it to derive critical information from unstructured data, and also to identify emotion and sarcasm.
You can look at semantic analysis as the process of extracting meaning from text. It analyzes the grammatical structure of the text, looking for patterns and relationships between words in a specific text.
What are the elements of semantic analysis?
These are specific lexical items of generic lexical items. They illustrate the connection between a generic word and its occurrences. The generic lexical items are called hypernyms and their occurrences are known as hyponyms. As an example, ‘crow’ would be a hyponym of the hypernym ‘bird’.
These are words that are spelled identically but have different meanings. An example of homonyms would be ‘book’ (something that you read) and ‘book’ (the act of placing a reservation).
This refers to a situation where words are spelt identically but have different but related meanings. An example of polysemy could be ‘drink’. The mean could change depending on whether we are talking about a drink being made by a bartender or the actual act of drinking something.
Words that have the exact same or very similar meanings as each other. Examples could include ‘beautiful’ and ‘gorgeous’.
Words that have opposite meanings. Examples include ‘dull’ and ‘bright’.
An arrangement of words that suggests that something is a part of a whole. It is a logical arrangement of letters and words that indicate a component portion of or member of something. An example of meronomy could include ‘a slice of pizza.’
Semantic analysis also takes collocations (words that are habitually juxtaposed with each other) and semiotics (signs and symbols) into consideration while deriving meaning from text.
The system using semantic analysis identifies these relations and takes various symbols and punctuations into account to identify the context of sentences or paragraphs.
Where is semantic analysis used?
A subfield of natural language processing (NLP) and machine learning, semantic analysis aids in comprehending the context of any text and understanding the emotions that may be depicted in the sentence. It is useful for extracting vital information from the text to enable computers to achieve human-level accuracy in the analysis of text. Semantic analysis is very widely used in systems like chatbots, search engines, text analytics systems, and machine translation systems.
Why is meaning representation needed?
Here are a few reasons why meaning representation is necessary:
- Firstly, meaning representation allows us to link linguistic elements to non-linguistic elements.
- Meaning representation also allows us to represent unambiguous, canonical forms at their lexical level.
- It can even be used for reasoning and inferring knowledge from semantic representations.
How does semantic analysis represent meaning?
Here are the approaches that semantic analysis uses for the purpose of meaning representation:
- Semantic nets
- First order predicate logic (FOPL)
- Case grammar
- Conceptual dependency (CD)
- Conceptual graphs
- Rule-based architecture
What are the processes of semantic analysis?
Here are two of the processes of semantic analysis:
Word Sense disambiguation
This is an automatic process to identify the context in which any word is used in a sentence. In natural language, a single word could take on several meanings. For example, the word light could mean ‘not dark’ as well as ‘not heavy’. The process of word sense disambiguation enables the computer system to understand the entire sentence and select the meaning that fits the sentence in the best way.
There are entities in a sentence that happen to be co-related to each other. Relationship extraction is used to extract the semantic relationship between these entities.
What are the techniques used for semantic analysis?
There are two techniques for semantic analysis that you can use, depending on the kind of information you want to extract from the data being analyzed. These include text classification and text extraction.
Semantic text classification models
- Intent classification models classify text based on the kind of action that a customer would like to take next. They look at the customer’s intent. Having prior knowledge of whether customers are interested in something helps you in proactively reaching out to your customer base.
- Sentiment analysis involves identifying emotions in the text to suggest urgency. It is used to detect the hidden sentiment inside a text, whether it is positive, negative, or neutral. Sentiment analysis is widely used in social listening because customers tend to reveal their sentiment about the company on social media.
- Topic classification is all about looking at the content of the text and using that as the basis for classification into predefined categories. It involves processing text and sorting them into predefined categories on the basis of the content of the text.
Semantic text extraction models
- Entity extraction looks for entities in the text. Entities could include names of companies, products, places, people, etc. Sentences and phrases are made up of various entities like names of people, places, companies, positions, etc. Entity extraction is used to identify these entities and extract them. This method is rather useful for customer service teams because the system can automatically extract the names of their customers, their location, contact details, and other relevant information.
- Keyword extraction focuses on searching for relevant words and phrases. It is usually used along with a classification model to glean deeper insights from the text. Keyword extraction is used to analyze several keywords in a body of text, figure out which words are ‘negative’ and which ones are ‘positive’. Insights regarding the intent of the text can be derived from the topics or words mentioned the most in the text.