Automatic synonym implementation helps to catch all the errors people make, such as misspelling words or describing things in a specific manner. A detection synonym system is the ability of a computer to understand and analyze human language and different word meanings.
Synonym detection can bring your business to a new level as it helps to better understand the customer and make your search results specific and related to the customer’s intent. It can also make your business more competitive. Continue reading to learn more about how automatic synonym implementation with AI works.
What are the Synonyms in NLT?
Let’s start with a definition from the NLTK dictionary. The WordNet is a part of Python's Natural Language Toolkit - a large word synonyms database of English Nouns, Adjectives, Adverbs, and Verbs. The words in this database are grouped into sets of cognitive synonyms, which are called synsets. There are also some groups of words with the same meaning.
WordNet is often combined with NLTK for different purposes. First of all, it links together related words. Note that it doesn’t simply link the words based on form or letters but unites the words in the same semantic network with no ambiguity. In general, it marks and identifies the semantic relationships between the words and learned behavior synonym.
As you can see in the example above, such entries as Word of God, Word, Scripture, Holy Writ, Holy Scripture, Good Book, Christian Bible, and Bible create the synset related to the Bible concept, and each of these forms is a lexical variant.
WordNet groups words based on their meanings based on two factors:
- Interlinking not just word forms but specific meanings of words
- Labeling the semantic relations among the words and find alternative words
Businesses can use automatic synonym detection for better searches and user experience
“The system is agnostic as far as grammar goes. It doesn’t actually care what the words mean. What it cares about is the intent of the users.”
— Carlos Valcarcel, a senior solutions architect at Lucidworks
According to a study published by the Baymard Institute, 70% of search engines fail their synonym searches, demanding users to search for the precise product name. You need to analyze your clients’ search terms before starting to use the modeling synonym detection feature. In such a way, you’ll be able to come with the most specific search results possible. Let’s see how to do it with artificial intelligence.
Artificial intelligence synonym detection: Step-by-step process
Here is how automatic synonym implementation with AI works in wordnet synonyms.
Find similar search queries
First, the algorithm studies the customer behavior and uses it as a pillar for building the list of synonyms. Such an approach is better than starting with predetermined synonyms or related words. Why? It studies what people type in the search box and what links they click. For example, when the same queries lead to the same search results, they are considered similar queries.
Before creating a final synonym list, it’s crucial to perform several steps. The first one is stemming, which means reducing different forms of the word to the common one. For example, stemming connection, connective, connected, and connecting to connect. After that, it’s required to remove stop words, e.g., taking out words like the, is, at, which, and on will speed up search performance. Also, the algorithms change multi-word phrases into a single word, usually by underscoring it.
After the user queries are cleaned up, it’s time to pull a set of synonyms. The algorithm scans for words and phrases that appear before or after the same word to define whether they are synonyms. For example, laptop charger and laptop power are considered synonyms because they both follow the word “laptop.”
Using graph model to define relationships between the group of potential synonyms
During this stage, similar terms are grouped based on how likely they are related to each other. If this relationship is high enough, they appear close to each other on the graph.
Determine synonyms and just context match
During the last stage, the synonyms are divided from the phrases that match up in the context. For example, the words “game” and “play station” are not synonyms. However, they relate to the same concept, when the words “earbud” and “earphone” are synonyms as they appear before and after the brand name “Bose.”
Synonym Detection Benefits
What are the benefits of automated synonym detection?
“Machine learning does all the heavy lifting, allowing business users to “sit back, get to watch and celebrate.”
— Noah Locke, manager of web technology at UW Health
First of all, computational linguistics and neural language processing provides overly simplistic solutions to really complex problems and frees your team of dull, repetitive tasks. Here’s what can bring automated synonym detection to a higher level. Constant analysis of search queries to understand where and how to apply ML to take site search to the next level
Machine learning needs to analyze the queries and make decisions at the time of the query. It’s important to listen and learn from the customer and syndicate these insights to the relevant applications.
For example, Algolia detects user intent to come up with better search results. It has created an AI studio for making recommendations based on how real users use synonyms. It proves that machine learning helps to remove ambiguity from a lot of polysemous words and makes synonym detection more efficient. Another example of a smart search for e-commerce is Klevu AI, an eCommerce search solution that delivers search results based on user intention and behavior in real-time.
“Machine learning should study the current location, previous searches, and past product views of the customer, as well as clicks and implicit preferences to come up with the best search results.”
— George Serebrennikov, C0O at Proxet, a company providing software development services
Synonym generation with AI and NLTK WordNet in Python
So here’s how you can find Synonyms from NLTK WordNet in Python NLTK. There are 155287 words and 117659 synonym sets included with English WordNet, and different methods available with WordNet can be found by typing dir(guru).
Here’s an example of writing a program using Python to find synonyms and antonyms of the word “active” using Wordnet.
- Wordnet is a corpus, so it is imported from the ntlk.corpus
- List of both synonym and antonym is taken as empty, which will be used for appending
- Synonyms of the word active are searched in the module synsets and are appended in the list of synonyms
- Output is printed
Also, Python allows stemming and lemmatization which is widely used for text preprocessing. Here’s how it works:
- Importing a stem module in the nltk dictionary. If you import the complete module, the program becomes heavy as it contains thousands of lines of codes.
- Preparing a dummy list of variation data of the same word.
- Creating an object which belongs to class nltk.stem.porter.PorterStemmer.
- Passing it to PorterStemmer one by one using the “for” loop and proceeding to the output root word of each word mentioned in the list.
- Removing redundancy in the data and variations in the same word; filtering data will help in better machine training.
Also, there is a Python library - a part of Python’s Natural Language Toolkit used for automatic text analysis and artificial intelligence applications in modeling synonym.
Also, vocabulary and phrase matching can be done using the Python SpaCy. It’s used for synonym modeling Python with the Matcher tool to specify custom rules for phrase matching. First, you need to define patterns to match. Then, you have to add these patterns to the Matcher tool to get synonyms from NLTK WordNet and apply this tool to the document that you want to match your rules with.
To conclude, there are the following NLP trends that build up the futures of NLT:
- Transfer learning is where a model is trained for one task and repurposed for a second task related to the main task.
- Low code tools allow non-technical users to perform NLP tasks that were once only accessible to data scientists and developers.
- Pre-trained multilingual models may perform even better than monolingual models.
- Automated customer service to help companies eliminate tedious tasks such as sorting customer support tickets and handling customer queries.
Synonym detection helps to obtain accurate searches, even at times when such technologies as voice search on smart speakers, car dashboards, or mobile digital assistants become more popular.
Sometimes, business users and digital players don’t trust machine learning, that’s why combining it with human touch can make it more appealing. AI algorithms can help better understand and improve customer understanding and focus more on business results.
If you want to enhance the opportunities of your business with the help of automatic synonym implementation, talk with us at Proxet. Sixty percent of our projects have AI/ML components, including everything from chatbot implementation to image recognition and sentiment analysis. We’ll guide you from the concept to its implementation and help you bring your business to a new level.
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