Contextual Synonym Embedding - Utilizes Embeddings

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This project focuses on enhancing keyword relevance within webpage content by identifying high-quality, context-aware synonyms. It leverages pre-trained language models to extract semantically aligned phrases from existing webpage blocks that closely match the meaning and intent of a given keyword. These synonyms are not generic alternatives but are grounded in the actual textual context of the page, ensuring natural integration and better content alignment.

The system supports multi-page and multi-keyword processing, automatically identifying and ranking potential synonym candidates across different content blocks. Each suggestion is scored based on its embedding similarity to the keyword, and accompanied by the source context to assist with transparent editorial decisions.

This approach provides a practical, scalable solution for improving search relevance and readability, especially in content-heavy SEO applications where keyword placement must remain both effective and natural.

Core Capabilities:

Understands the semantic meaning of keywords in context
Retrieves relevant phrases from content that function as natural synonyms
Handles multiple URLs and multiple keywords simultaneously
Ranks synonyms by contextual similarity to the keyword
Provides block-level source information to guide content edits

Project Purpose

This project is designed to address a common gap in SEO and content strategy workflows—identifying high-quality, context-appropriate synonyms for targeted keywords within specific webpage content. While traditional synonym discovery often depends on generic word lists or shallow lexical similarity, such approaches frequently miss the nuances of meaning that depend heavily on the surrounding context. As a result, suggested terms may sound unnatural, introduce ambiguity, or fail to contribute meaningfully to content optimization efforts.

The goal of this project is to produce synonym suggestions that not only resemble the keyword semantically but also fit naturally within the original document’s context. This is achieved by computing contextual embeddings for both keywords and potential candidate terms directly extracted from the actual content blocks of a webpage. Each candidate is evaluated not in isolation, but with respect to how well its usage aligns with the contextual meaning and tone of the keyword across the document.

This approach allows for more informed and precise synonym suggestions that can improve on-page variation, reduce keyword redundancy, and support more effective semantic SEO practices. In doing so, it strengthens a content strategist’s or SEO team’s ability to maintain quality while optimizing visibility.

Project’s Key Topics Explanation and Understanding

Understanding Contextual Synonym Embedding

Traditional synonym identification relies on static word lists or generic similarity models that treat all usage of a word as equal. But language doesn’t work that way. Words shift their meaning based on how and where they are used. This project introduces the idea of Contextual Synonym Embedding, which means identifying alternative words that not only share the same meaning, but also make sense in the specific context in which the original word appears.

For example, the word “optimize” in a technical SEO article may have valid synonyms like “refine” or “tune”, but those alternatives might not work in a general business context where words like “improve” or “streamline” are more natural. The goal is not just similarity, but contextual appropriateness.

Why Context Matters in Synonyms

Every piece of digital content lives within a certain environment — an article about marketing, a guide on SEO strategies, or a blog post on user engagement. Each environment influences the way words function. Suggesting a synonym without understanding this context may lead to awkward phrasing, diluted messaging, or even incorrect usage.

This project approaches synonym discovery through the lens of contextual usage, ensuring that any alternative word suggested is sensitive to the tone, purpose, and topic of the content where it’s meant to appear. This allows for smarter content refinement and helps maintain brand voice and clarity.

The Role of Language Embeddings

To make contextual synonym detection possible, the project uses language embeddings — a modern technique in natural language processing (NLP). In simple terms, embeddings are mathematical representations of words and phrases in a way that captures their meaning. More importantly, contextual embeddings generate these representations based on surrounding words, not just on the word itself.

This means that the same word will have different vector representations depending on the sentence or paragraph in which it is used. These vectors can be compared to identify synonyms that are not only close in meaning but aligned with the current usage scenario.

From Static Thesaurus to Context-Aware Intelligence

Think of traditional synonym lookup as a static dictionary, and contextual synonym embedding as a smart assistant that reads your content, understands its intent, and then recommends the best-fitting alternatives. This shift from rigid lists to context-aware intelligence makes the synonym suggestions far more relevant, valuable, and usable — especially in industries like SEO, marketing, or editorial where precision and tone really matter.

Why is contextual synonym discovery more valuable than traditional keyword matching or thesaurus-based methods?

Traditional methods for finding synonyms often rely on generic lists that ignore the actual usage context of the word. While they may produce similar terms, these suggestions are frequently inappropriate or awkward when used in real content. For example, a general synonym for “lead” could be “guide” or “direct,” but depending on the sentence, those substitutions might change the sentence’s tone or even meaning.

Contextual synonym discovery solves this by using advanced language embeddings that evaluate a word within its specific surrounding text. This means synonym suggestions are semantically accurate and stylistically aligned with the content, resulting in more natural, coherent writing. It leads to better keyword diversification in SEO, improved readability, and enhanced user experience — all of which support stronger content performance.

How can this capability improve real-world SEO efforts?

In SEO, overusing exact-match keywords can lead to keyword stuffing, lower content quality, and even search penalties. However, simply replacing them with random synonyms may weaken the message or break the flow.

This system enables smart keyword diversification, where high-quality, relevant synonyms are recommended based on context. These can be used to naturally expand keyword coverage, target long-tail variations, and improve the content’s ability to rank across a wider range of search queries — without compromising clarity or tone.

Additionally, by embedding contextual understanding, the model respects the intent and structure of the page, ensuring that keyword changes enhance — not harm — content performance.

How is this approach useful beyond just SEO keyword work?

While designed with SEO in mind, contextual synonym embedding has broader applications in content strategy, writing assistance, and editorial optimization. For example:

Marketing teams can refine messaging by adapting word choices to better suit target audience tone and campaign goals.
Content writers can enhance fluency, avoid repetition, and tailor language for different platforms or regions.
Editorial teams can ensure brand tone is consistent while still keeping content varied and engaging.

The core advantage is context-aware rewriting, which is useful in any workflow that demands high-quality, tailored language use.

What makes this approach scalable and maintainable for ongoing content optimization?

Unlike manual synonym selection or rule-based systems, this approach is fully automated and model-driven, using pre-trained language models that generalize across domains without needing retraining or fine-tuning. This makes it practical for large-scale deployment, where hundreds of pages or thousands of keywords may need regular analysis.

Moreover, since the system works block-by-block on real content and supports multi-URL processing, it scales effortlessly to new inputs. This enables continuous content refinement, aligning with evolving SEO strategies and content goals without additional overhead.

Libraries Used

requests

· The requests library is a widely-used Python module for making HTTP requests in a simple and human-readable format. It supports GET and POST methods, custom headers, sessions, timeouts, and many other useful features for web communication.

· In this project, requests was used to fetch raw HTML content from different web pages. This served as the initial step in extracting relevant content blocks where contextual synonym embeddings could be applied. It allowed smooth handling of different URLs and supported batch operations for multiple pages.

BeautifulSoup (from bs4) and Comment

· BeautifulSoup is a powerful library for parsing HTML and XML documents. It provides tools for navigating and modifying parse trees, and can handle poorly formatted markup, making it highly suitable for web scraping tasks. The Comment class is used to detect and filter out HTML comments.

· Here, BeautifulSoup was used to clean and structure the raw HTML pages. It helped isolate meaningful content blocks (e.g., paragraphs, headings) and ignore non-visible or irrelevant elements like scripts, styles, and comments. This ensured that only high-quality, user-visible text was passed into the embedding model for analysis.

charset_normalizer.from_bytes

· charset_normalizer is a library used to detect and fix character encoding issues in text. It is particularly useful when reading data from web sources where encoding can vary or be inconsistent.

· This was used in the project to safely decode HTML content into readable Unicode text, avoiding issues with garbled characters or encoding errors during the content extraction stage.

re (Regular Expressions)

· Python’s built-in re module provides support for regular expressions, which are used to match patterns in text. It is often used for text preprocessing, such as filtering, tokenizing, and pattern replacement.

· In this project, re was employed for cleaning unwanted patterns in the text blocks. It helped remove excess whitespace, normalize line breaks, strip HTML remnants, and perform contextual phrase extractions with precise control.

logging

· The logging module is Python’s built-in library for tracking and recording program execution. It allows developers to log messages at different severity levels (DEBUG, INFO, WARNING, etc.).

· Logging was configured to suppress non-critical messages and provide warnings where necessary, especially during web scraping and content cleaning. This helped identify potential issues (e.g., malformed HTML, inaccessible URLs) without cluttering the output during batch runs.

html and unicodedata

· The html library provides utilities for handling HTML entities, such as converting & to &. The unicodedata module helps with normalizing Unicode characters, removing accents, and handling special symbols consistently.

· Both were used during the text normalization process. After extracting visible HTML text, these tools were applied to standardize characters and ensure that the text input to the embedding models was clean, simplified, and consistent across different webpages.

typing.List

· typing is a Python standard module that provides support for type hinting and annotations, which improve code readability and allow for better static type checking.

· The List type from typing was used in function definitions to clearly express the expected input and output data structures, ensuring robust and well-documented code across the content pipeline and synonym generation logic.

numpy

· numpy is a core scientific computing library in Python, providing high-performance support for arrays, matrices, and numerical operations.

· It played a foundational role in this project by handling content and tone embeddings as numerical vectors. Numpy arrays were used for storing, manipulating, and computing similarity between the keyword embeddings and candidate phrases.

sentence_transformers

· sentence_transformers is a Python library that wraps and extends pretrained transformer models to support efficient sentence-level embeddings. It simplifies the process of converting text into high-dimensional vectors using models like BERT.

· This was central to the project. It was used to generate contextual embeddings for both keywords and candidate phrases extracted from web content. These embeddings formed the basis for calculating semantic similarity and selecting the most context-relevant synonym suggestions.

transformers.utils

· The transformers library by HuggingFace is a highly popular framework for using pretrained language models. The utils submodule allows configuration of logging, verbosity, and other global behaviors.

· In this project, it was used to suppress progress bars and reduce verbose output from the transformers backend during embedding computations. This helped maintain a clean and focused interface, especially during large-scale or batch execution.

spacy

· spacy is an industrial-strength NLP library used for fast and accurate linguistic analysis, including tokenization, part-of-speech tagging, entity recognition, and phrase extraction.

· It was used here specifically for candidate phrase extraction — helping to extract meaningful noun phrases or keyword-like structures from web content blocks. These phrases were later scored and compared with the target keyword to find suitable synonym replacements in context.

Function: extract_page_blocks()

Overview

· This function is responsible for extracting high-quality, visible, and meaningful text content from a webpage given its URL. It follows a modular approach:

Fetch HTML content using a robust, encoding-safe method.
Clean the HTML by removing noise (e.g., scripts, forms, hidden tags).
Extract content blocks (e.g., paragraphs, headings, bullet points) that are readable, ASCII-heavy, deduplicated, and long enough to be meaningful.

· This function plays a critical role in the contextual synonym embedding pipeline by providing clean and structured input text blocks from live webpages. These blocks are later used for embedding, scoring, and selecting synonyms in context.

Key Code Explanations

Robust HTML Fetching with Encoding Handling

The content fetch logic ensures two things: valid HTTP response and correct character decoding.
If the encoding is not declared by the server, it automatically falls back to charset_normalizer to detect and decode the page accurately.
This prevents common scraping issues like broken characters or garbled Unicode.

HTML Cleanup and Noise Removal

· This cleaning step strips out all non-visible or decorative content, including:

Scripts, styles, navigation bars, footers, form elements
Comment tags and even hidden elements (display:none, hidden attributes)

· The result is a lean HTML structure focused solely on readable content meant for human visitors.

Clean Text Block Extraction

Only specific tags that typically contain meaningful text are selected (like paragraphs and headings).
Very short text segments (less than a word count threshold) are skipped, ensuring quality over quantity.

ASCII Ratio Filtering

This filter removes non-English or noisy blocks by measuring the proportion of ASCII characters.
A low ASCII ratio often indicates foreign scripts, heavy symbols, or malformed content not useful for English-language analysis.

Deduplication via Hashing

Duplicate or near-duplicate text blocks are avoided using hash-based fingerprinting.
This prevents repeated content from skewing the embedding-based scoring or generating redundant synonym suggestions.

Function: preprocess_blocks()

Overview

This function takes raw content blocks extracted from a webpage and prepares them for downstream processing such as embedding, scoring, and synonym extraction.
It removes unwanted boilerplate phrases, URL fragments, formatting clutter, and overly short texts — all of which are common in crawled web data but undesirable for contextual NLP tasks.
The result is a structured list of cleaned block dictionaries ({“text”: …, “url”: …}), each representing a useful unit of content.

This preprocessing step ensures higher quality embeddings by filtering noise and standardizing textual content.

Key Code Explanations

Regex for Common Web Clutter

· These regex patterns match common website boilerplate content:

Legal disclaimers, call-to-action phrases, cookie banners, etc.
URLs embedded inside text (which offer little semantic value)
Numbered or bulleted list formats
Roman numerals or step-wise prefixes (e.g., “Step 1:”, “IV)”)

Substitution Rules for Standardization

This dictionary defines normalization for special Unicode characters, smart quotes, and invisible characters (non-breaking spaces, zero-width spaces) often found in scraped content.
It helps improve tokenization and downstream model consistency.

Central Cleaning Logic

· Applies all the cleaning steps in order:

Decodes HTML entities (e.g.,  , &)
Normalizes Unicode representations
Removes URLs and boilerplate phrases
Replaces unwanted characters
Strips extra whitespaces

This ensures clean, plain, and standardized content.

Minimum Length Filter and Final Output

Any block with fewer than min_word_count words is discarded as likely uninformative.
The rest are packaged into structured dictionaries and added to the result.

Function: load_embedding_model()

Overview

This function loads a pre-trained sentence embedding model using the SentenceTransformer class from the sentence-transformers library. The model converts text blocks into dense vector representations, which are critical for capturing semantic similarity in downstream tasks like contextual synonym extraction, clustering, or scoring.

The default model used—”sentence-transformers/paraphrase-mpnet-base-v2″—is a lightweight yet high-performing transformer model optimized for semantic textual similarity (STS), making it a practical choice for production-level SEO applications.

Key Code Explanation

Load the Model

embedding_model = SentenceTransformer(model_name)

This line initializes a transformer-based sentence embedding model.
By default, it loads “paraphrase-mpnet-base-v2”, a widely-used model trained to embed similar phrases closely in vector space.

Embedding Model Used

To power contextual understanding in this project, used the paraphrase-mpnet-base-v2 model from the sentence-transformers library. This model plays a central role in converting both content and candidate phrases into high-quality semantic embeddings. The following sections explain different aspects of the model and why it’s a suitable choice for our use case.

What is the paraphrase-mpnet-base-v2 Model?

This model is a pre-trained transformer-based sentence embedding model developed using Microsoft’s MPNet architecture. It has been fine-tuned specifically for semantic textual similarity tasks—meaning it is highly capable of understanding when two different phrases convey similar meanings.

Rather than focusing on token-level details like traditional word embeddings (e.g., Word2Vec or GloVe), this model encodes entire sentences or blocks of text into dense, fixed-size vectors. These vectors can be used to compare meaning, relevance, or contextual alignment between any two pieces of text.

Why This Model is Used

This model was chosen for three key reasons:

· Performance: It consistently ranks among the top models for semantic similarity tasks on benchmarks such as STS and SentEval. It offers excellent balance between accuracy and computational efficiency.

· Contextual Quality: It produces embeddings that are highly contextual—meaning it understands the differences in meaning of similar words used in different settings. For example, “link building strategy” in an SEO context is different from “building a chain link fence,” and this model can differentiate that.

· Zero Configuration: As a fully pre-trained and publicly available model, it works directly out of the box with no fine-tuning required. This makes it ideal for production workflows that need immediate deployment without custom training pipelines.

How the Model Generates Embeddings

When a sentence or paragraph is passed into the model, it performs several steps internally:

Tokenization: Breaks the input into word pieces compatible with the MPNet encoder.
Transformer Encoding: Passes tokens through multiple layers of attention and feed-forward networks to build context-aware representations.
Pooling: The model pools the token-level embeddings into a single sentence-level vector using mean pooling (or similar strategies).
Vector Output: The result is a 768-dimensional vector that captures the semantic properties of the input text.

This vector can then be directly compared to another vector using cosine similarity to determine how closely related the meanings are.

Applications Within This Project

In this project, the model serves three specific roles:

Embedding Cleaned Content Blocks: After preprocessing, all textual blocks from the crawled pages are converted to embeddings.
Embedding Candidate Phrases: Synonym candidates are also embedded to measure their semantic closeness to original keywords or blocks.
Computing Relevance Scores: The cosine similarity between vectors helps rank which phrases are most appropriate in context—thereby identifying true contextual synonyms.

Function: embed_blocks

Overview

This function takes a list of cleaned content blocks (each represented as a dictionary containing at least a ‘text’ field) and adds a contextual embedding vector to each block using the paraphrase-mpnet-base-v2 model.

The output is a list of dictionaries, where each dictionary contains the original block fields plus a new ‘content_vector’ key, which holds the semantic embedding (as a NumPy array). This vector representation enables further semantic operations such as similarity comparison, clustering, and contextual synonym matching.

Key Code Explanations

Text Extraction for Batch Encoding

texts = [block[“text”] for block in cleaned_blocks]

This line extracts only the raw text values from all input blocks. These will be fed into the embedding model in a batch for efficient processing.

Embedding Generation

embeddings = embedding_model.encode(texts, convert_to_numpy=True, normalize_embeddings=True)

This is the core embedding step:

convert_to_numpy=True ensures the output is a NumPy array.
normalize_embeddings=True ensures all output vectors are L2-normalized, which is especially useful when computing cosine similarity later (as it makes cosine similarity equivalent to dot product).

Merging Embeddings with Original Blocks

Each block is copied and augmented with the corresponding content_vector. This step keeps the original block metadata (like text and url) intact while adding the vector. The result is a ready-to-use list of fully embedded blocks.

This function plays a foundational role in the pipeline, as it prepares content blocks for semantic scoring, clustering, and retrieval—all of which rely on accurate and normalized vector representations.

Function: generate_contextual_embeddings

Overview

This function generates context-aware embedding vectors for a given list of keywords by wrapping them in multiple natural language prompt templates. These prompts simulate real SEO-style usage of the keywords to extract richer semantic meaning.

Instead of using isolated keyword embeddings (which lack surrounding context), this approach simulates how a keyword would typically appear in meaningful content. The function averages the embeddings from multiple prompt variations to produce a robust, contextually grounded representation for each keyword.

The result is a dictionary where each keyword is mapped to its corresponding semantic vector (NumPy array), ready to be compared with document or block embeddings for tasks like contextual matching or ranking.

Key Code Explanations

Default Prompt Templates

If no custom prompts are provided, the function uses three standard SEO-oriented sentences to wrap each keyword. These templates simulate how keywords appear in real-world articles, blog intros, or educational content — helping the model generate meaningful contextual vectors.

Prompted Sentence Generation

contextual_sentences = [template.format(keyword=keyword) for template in prompt_templates]

This line generates three distinct full sentences for each keyword by filling in the {keyword} placeholder in each template. This enriches the embedding with multiple contexts instead of a single usage.

Embedding + Averaging

The model encodes each sentence separately.
The function then averages the resulting vectors across all prompts for the same keyword to create a single, smoothed representation of the keyword in context.
Normalization is applied if normalize=True, ensuring all output embeddings lie on the same vector scale (L2 unit sphere), which is critical for cosine-based comparisons.

Function: get_top_blocks_for_keyword

Overview

This function identifies the top-K content blocks that are most contextually relevant to a given keyword. It compares the keyword’s contextual embedding to the content embeddings of all blocks using cosine similarity, and ranks the blocks by how semantically close they are to the keyword’s meaning.

This is a core part of the synonym matching and content recommendation process — enabling SEO strategies that rely on deep understanding of how well a webpage’s content addresses a particular term or concept in context.

Key Code Explanations

Extract Block Embedding Matrix

block_vectors = np.array([block[“content_vector”] for block in embedded_blocks])

This step gathers all content_vectors from the pre-embedded blocks into a single NumPy array, forming a dense matrix where each row corresponds to a block’s embedding.

This structure allows efficient batch computation of similarity scores between the keyword embedding and all block embeddings.

Cosine Similarity Scoring

scores = util.cos_sim(keyword_embedding, block_vectors)[0].cpu().numpy()

Using sentence_transformers.util.cos_sim, the function calculates cosine similarity between the keyword embedding and every block embedding. This gives a score between -1 and 1 for each block, where higher values mean stronger semantic alignment.

The result is converted to a NumPy array for further ranking.
.cpu() ensures compatibility when the model runs on GPU environments.

Selecting Top-K Relevant Blocks

top_indices = np.argsort(-scores)[:top_k]

This line performs descending sort on the similarity scores (via -scores) and selects the top K indices. These indices correspond to the most contextually similar content blocks for the given keyword.

Function: extract_candidate_phrases_from_blocks

Overview

This function automatically extracts candidate synonym phrases from the content blocks of a webpage, excluding any that directly match the original input keywords. It focuses on noun phrases, adjectives, and verbs — the parts of language most likely to yield meaningful alternatives or variations.

The result is a list of contextual phrases, each tagged with the source text and URL, making them ready for scoring and use in content optimization workflows like synonym recommendation or gap analysis.

Key Code Explanations

Setup and Keyword Normalization

seen_terms ensures we don’t include duplicates.
normalize_candidate_phrase() is used to preprocess keywords and candidate phrases for consistent comparison (e.g., lowercasing, whitespace trimming).
This also prevents extracted terms from duplicating the original SEO keyword list.

Processing Each Block with spaCy

doc = nlp(block[“text”])

For each content block, the text is passed through the spaCy NLP pipeline, which parses syntactic structure, POS tags, lemmatization, and chunking — enabling precise phrase-level extraction.

Noun Phrase Extraction

This section:

Extracts noun chunks — phrases like “search algorithm”, “content strategy”, etc.
Filters based on text length to avoid overly short (less meaningful) or long (unwieldy) phrases.
Each candidate is normalized and checked against the input keyword list and the seen_terms set to avoid redundancy.

If the candidate passes all conditions, it’s added to the candidates list, along with:

The original block text (source_text)
The associated page (url)

Adjective and Verb Extraction

This captures adjectives and verbs that aren’t stopwords — useful for generating more action-oriented or descriptive alternatives.

Examples:

From “optimize content”, it might extract “optimize”.
From “effective strategy”, it might pick “effective”.

Function: score_candidates_against_embedding

Overview

This function evaluates the semantic closeness of each candidate phrase to a given keyword by computing cosine similarity between their embeddings. It selects only the top candidates that are both contextually relevant and semantically rich, based on a minimum score threshold and top-K cutoff.

The function is a critical ranking step that filters out weak or unrelated terms, ensuring that only high-quality, meaningfully related phrases are recommended.

Key Code Explanations

Validation & Candidate Preparation

Before doing any processing, the function checks if there are any candidate phrases at all. If not, it returns an empty list immediately, avoiding unnecessary model inference.

This line extracts just the raw phrase texts (without their metadata) for embedding.

Embedding Candidates and Scoring

The SentenceTransformer model (usually MPNet) is used to convert all terms into dense semantic vectors. Normalization ensures that cosine similarity behaves properly.

Here, a cosine similarity score is computed between the keyword’s embedding and each candidate phrase. The result is a 1D array of similarity values (typically between 0 and 1), where higher is better.

Filtering by Score Threshold

For each candidate, its similarity score is compared to the min_score (default: 0.7). Only those that are above this threshold are considered strong enough matches to be kept.

Function: suggest_synonyms_for_keywords

Overview

This function is the core pipeline that brings together all previous components to suggest high-quality contextual synonyms for a list of SEO keywords. It identifies the best-matching content blocks, extracts candidate phrases, scores them for contextual relevance, and returns top-ranked alternatives with traceable origin.

It’s designed for real-world SEO content enhancement, giving clients actionable recommendations rooted in their existing site language.

Key Code Explanations

Generate Contextual Keyword Embeddings

keyword_embeddings = generate_contextual_embeddings(keywords, model)

Each input keyword is transformed into a context-aware embedding vector using sentence prompts. These embeddings capture not just the literal term but also its surrounding usage and tone within SEO contexts.

Identify Top-Matching Content Blocks

top_blocks = get_top_blocks_for_keyword(keyword_embedding, embedded_blocks, top_k=top_k_blocks)

Using the keyword’s embedding, the system searches across all embedded content blocks to find the top K blocks that are most semantically aligned.

Extract Candidate Phrases

candidates = extract_candidate_phrases_from_blocks(top_blocks, keywords)

From the matched blocks, potential synonym phrases are extracted using syntactic heuristics (e.g., noun chunks) while ensuring duplicates of the keyword itself are not considered.

Score and Rank Candidates

All candidate phrases are embedded and scored based on their cosine similarity to the keyword embedding. Only high-confidence matches (above min_score) are retained, and the top K ranked alternatives are returned.

Function: display_synonym_suggestions

This function serves as a clean and practical output interface for presenting the synonym suggestions generated by the pipeline. It provides a simple, readable printout grouped by each keyword, allowing users (or clients) to quickly understand the results without navigating structured data or files.

For each keyword, the function prints:

The keyword itself
A ranked list of synonym terms
Each term’s similarity score
A truncated source text snippet for context
The originating URL

Because it’s compact and clear, this display function is particularly useful in review sessions, notebooks, or client reports where transparency and traceability matter. It supports efficient result validation without overwhelming technical detail.

Result Analysis and Explanation

This section interprets the synonym suggestions derived from the content of the following page: https://thatware.co/advanced-seo-services/

The purpose of this analysis is to help understand how effectively the page captures the semantic intent of the provided keywords. It also identifies relevant alternative phrases that can be used to strengthen keyword targeting, diversify content, and enhance semantic richness across the page.

Keyword: search engine optimization

The page reflects a mature and high-quality treatment of this concept through alternative expressions such as:

advanced seo strategies
strategically implemented seo techniques
proactive seo practices

All of these scored between 0.71 and 0.74, indicating that the content uses well-aligned and purpose-driven variations of the keyword. These expressions not only maintain the core semantic meaning but also elevate the language style, making the content more authoritative and appealing to both search engines and advanced readers.

Keyword: technical seo

This keyword shows excellent contextual alignment within the page. Notable variations include:

technical seo analysis
seo
advanced seo

The top phrase scored well above 0.84, showing near-perfect contextual relevance. This indicates that the page offers strong coverage of technical topics like performance audits, site structure, and crawlability. The broader terms that follow suggest comprehensive content that blends technical depth with high-level strategy.

Keyword: on-page seo

Here, the synonym matches are particularly strong and clearly defined:

a well-implemented on-page seo strategy
advanced seo
seo
a robust seo strategy

The highest scoring phrase reached a value of 0.905, indicating extremely precise alignment. The combination of exact and semantically extended variants demonstrates that the page effectively communicates detailed, actionable insights around on-page SEO principles — including optimization structure, content tuning, and relevance signals.

Keyword: off-page seo

This keyword shows limited presence, with only one moderately relevant match:

seo

The relatively lower score of just above 0.70 suggests that off-page elements are not discussed in a dedicated or structured manner. This reveals a clear opportunity for enhancement. Integrating content that addresses backlinks, authority building, or social proof would help round out the page’s overall SEO scope.

Keyword: link building

The content demonstrates good depth for this topic through the following expressions:

advanced link-building strategies
traditional link-building strategies
resource page link building

These phrases scored between 0.74 and 0.82, reflecting strong topical relevance. The variety of strategies mentioned indicates that the page discusses link building not just as a concept but as an applied discipline, which is valuable for attracting authoritative backlinks and improving page trustworthiness.

Score Threshold Explanation

The semantic match scores used in this analysis reflect how closely each suggested phrase aligns with the intended keyword in context. Here’s how to interpret the ranges:

Scores above 0.85 represent excellent matches, indicating phrases that are semantically rich and nearly identical in intent. These are the best candidates for direct use in the content.
Scores between 0.75 and 0.85 are considered strong matches. They maintain close contextual relevance and are reliable for use in headings, anchor texts, or supporting copy.
Scores between 0.70 and 0.75 are moderately aligned. These may capture partial intent and can be used when expanding or diversifying keyword coverage.
Scores below 0.70 are weakly relevant and typically not considered strong enough for substitution or integration.

These ranges help prioritize which suggestions to adopt or focus on when refining the page content.

Result Analysis and Explanation

This section provides a broad analysis of how effectively different web pages incorporate semantically aligned phrases for important SEO keywords. The model-generated suggestions reveal how each page conveys the intended meanings of these keywords through diverse language expressions. By doing so, the analysis highlights strengths, gaps, and actionable opportunities for improving keyword diversity and search relevance across multiple website assets.

Insights from Synonym Coverage Across Pages

Across the analyzed pages, synonym suggestions varied in quality and quantity. Certain keywords like “SEO tools” and “on-page SEO” yielded a strong number of high-scoring phrase variants on most pages. These alternatives, which included expressions like advanced SEO techniques, any effective SEO strategy, and SEO professionals, indicate that these pages address the concepts in a way that search engines can semantically associate with the target keyword. This contributes positively to keyword richness and ranking performance.

In contrast, for more niche or technical terms like “HTTP headers” or “keyword analysis”, fewer or no high-quality synonym matches were found on several pages. This gap suggests that these topics may not be covered explicitly or deeply enough, potentially missing out on valuable semantic signals that contribute to topical authority.

Patterns in Keyword-Specific Performance

Some keywords consistently surfaced relevant variants across multiple pages. For example, content related to site audits and on-page SEO frequently aligned with expressions involving customized audits or SEO strategy framing. However, the quality of these suggestions often varied depending on how directly the page content tied into the keyword’s intent.

Keywords that are broader in scope or have multiple interpretations (such as redirect or SEO tools) showed strong synonym coverage only when the content included implementation details or tool discussions. This reinforces the importance of context depth for stronger synonym alignment.

Understanding Synonym Suggestion Volume and Distribution

From the visual summary of the analysis, several insights emerge:

· Total Synonym Suggestions per Page: Some pages are semantically dense for multiple keywords, indicating robust keyword integration. Others had lower coverage, pointing to content that may be too narrowly focused or missing essential topics.

· Number of High-Scoring Synonyms per Keyword: Keywords like SEO tools and on-page SEO consistently yielded more high-scoring matches across pages. This suggests these concepts are well embedded and can be easily expanded upon for improved keyword targeting.

· Distribution of Synonym Scores: Score dispersion varied by keyword. Broader topics had wider score distributions, while well-focused topics had tightly clustered high scores, indicating clearer content intent.

· Average Score per Keyword across Pages: This gives a useful comparative signal—keywords with higher average synonym scores tend to be more consistently addressed across pages. Those with low average scores may require focused content updates.

· Top Scoring Synonyms: Reviewing the strongest phrase matches shows which expressions are already contributing high semantic value. These can be reinforced through headings, anchor texts, or semantic HTML to further strengthen topical authority.

Score Threshold Explanation

To help interpret the strength of synonym matches, scores are binned into four practical categories:

· Above 0.85: These represent high-confidence synonyms. They capture the same meaning as the target keyword in near-exact contextual usage. Such phrases are excellent for directly boosting keyword coverage without redundancy.

· Between 0.75 and 0.85: These are reliable and relevant matches, semantically close to the original keyword but potentially more specific or stylistically different. They are safe to incorporate into the page and especially useful in subheadings and callouts.

· Between 0.70 and 0.75: These are moderately aligned phrases. They share topical relevance but may require slight contextual adjustment. They are still valuable for introducing variation and semantic breadth.

· Below 0.70: Phrases in this range typically lack clear alignment and should be used with caution. They might capture only a partial aspect of the keyword or diverge from the intended meaning.

Understanding and applying these thresholds allows content creators to choose the most contextually valuable synonyms and identify where additional optimization efforts are necessary.

Q&A Section: Understanding the Results and Client Actions

Why are some keywords showing no relevant synonym suggestions?

Keywords with no relevant suggestions (e.g., http headers or keyword analysis on certain pages) likely lack contextual coverage within the content. This doesn’t necessarily mean the keyword is completely absent—it may be mentioned in passing but not discussed with enough depth or in a way that aligns semantically with its core meaning. This is a signal that the page could benefit from content expansion or clarification to better address that topic.

Review the content on pages with missing suggestions. Add targeted sections or expand discussions to clearly explain these concepts in context, using natural language and related terms.

What does a high synonym score tell us about content quality?

A high synonym score (typically above 0.80) means the phrase strongly aligns with the target keyword’s meaning in context. It reflects well-optimized content that communicates the keyword’s intent without repeating the keyword excessively.

Identify and reuse high-scoring phrases in prominent locations such as headings, meta descriptions, and internal link anchors. These help reinforce SEO signals while maintaining content diversity.

Some keywords appear across multiple pages—how can we tell which page is stronger for a keyword?

Pages with more high-scoring synonym matches for a given keyword indicate deeper or more comprehensive treatment of that topic. These pages are more likely to be perceived as relevant by search engines.

Use the average score and number of high-scoring synonyms per keyword to prioritize which page should be your primary target for that keyword. Consider consolidating or differentiating content across pages to reduce overlap.

How should we use the insights from synonym score distributions?

Score distributions (e.g., via boxplots) show how varied or consistent your content is in expressing the target concept. A narrow range of high scores implies focused, well-targeted content. A wider spread may suggest inconsistency or mixed messaging.

For keywords with wide score variation, standardize messaging by editing or removing weakly aligned phrases. For those with tight high scores, reinforce those strong sections with additional internal links or schema markup.

What’s the practical benefit of using alternative phrases instead of just repeating keywords?

Using semantically rich alternatives improves your page’s ability to rank for long-tail and conceptually similar queries. It also enhances user experience by avoiding keyword stuffing and promoting natural, readable content.

Incorporate high-scoring synonym phrases into your content plan. Use them in variations across sections, while ensuring the meaning stays on-topic and aligned with user intent.

Are there specific pages that require immediate content updates based on this analysis?

Yes. Pages with either very few or no relevant synonym matches for multiple keywords are under-optimized. These are missed opportunities to rank for valuable terms.

Flag pages with low total synonym suggestions or low average scores per keyword. Begin with foundational updates—adding contextual explanations, examples, or sections directly addressing those missing or weak keywords.

Can we use this analysis to guide our internal linking strategy?

Absolutely. High-scoring synonyms are ideal anchor text candidates for internal links. They are contextually relevant, keyword-aligned, and help diversify your link signals without redundancy.

Create internal links using these alternative phrases to connect related pages. This not only improves site structure but also strengthens topical relevance across your domain.

Final Thoughts

This contextual synonym embedding analysis offers a deep, actionable understanding of how well your content aligns with important SEO keywords—not just through exact matches but through semantically meaningful expressions. The model identifies where your content already performs well, highlighting strong synonym usage, and where it falls short, signaling opportunities for targeted improvement.

Pages that generate multiple high-scoring synonyms demonstrate strong contextual relevance and should be prioritized for content reinforcement, structured markup, and internal linking. On the other hand, keywords with weak or missing synonym coverage suggest content gaps that can be strategically addressed to improve visibility and ranking potential.

Overall, this analysis not only highlights opportunities for improving individual content pieces but also lays the groundwork for a more cohesive and intent-aligned content architecture across your site.

Tuhin Banik

Thatware | Founder & CEO

Tuhin is recognized across the globe for his vision to revolutionize digital transformation industry with the help of cutting-edge technology. He won bronze for India at the Stevie Awards USA as well as winning the India Business Awards, India Technology Award, Top 100 influential tech leaders from Analytics Insights, Clutch Global Front runner in digital marketing, founder of the fastest growing company in Asia by The CEO Magazine and is a TEDx speaker and BrightonSEO speaker.