Glossary
Keyphrase Extraction
Datasets
Fundamentals
Models
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Techniques
Last updated on February 20, 202410 min read

Keyphrase Extraction

Keyphrase extraction is crucial for analyzing customer reviews, understanding sentiments, and spotting emerging trends. It's difficult, demanding attention to linguistic details, context, and document structure. Yet, it plays a vital role in information retrieval and NLP.

Keyphrase extraction in natural language processing (NLP) involves pulling out the most important phrases from a document and capturing their essence. This technique facilitates document similarity checks and enhances search efficiency. Matching user queries with extracted phrases speeds up retrieval, especially in large databases.

In business, keyphrase extraction is crucial for analyzing customer reviews, understanding sentiments, and spotting emerging trends. It's difficult, demanding attention to linguistic details, context, and document structure. Yet, it plays a vital role in information retrieval and NLP.

Understanding Keyphrase Extraction

The keyphrase extraction process is divided into two stages: 

  • Extraction of Candidate Phrases: It involves studying how words are used and the way documents are written, looking for possible phrases based on specific rules, like how often they appear or how important they are, and then using either manual or automated methods, such as regular expressions, to pull out the important phrases. These hard-coded extracted phrases are termed candidate phrases.

  • Ranking of Candidate Phrases: Once candidate phrases have been identified,  the ranking is determined based on their relevance to a text document of interest. The candidate phrases that rank the highest are keyphrases for that document. This ranking process is typically accomplished using specialized algorithms like….. 

First, the candidate phrases are converted into numerical vectors in a process called word embedding. After that, we take the entire document, let's say an article on artificial intelligence, and represent it as another vector. Finally, we compare the vectors of the candidate phrases with the vector of the entire document. This comparison helps us understand how closely these phrases relate to the content of the article, allowing us to assess their similarity and rank their importance within the context of the document.

Keyphrase Extraction compared to other NLP Techniques

Keyphrase extraction shares similarities with other NLP techniques. In this section, we will break down the similarities and differences.

  • Keyword Extraction: Keyword and keyphrase extraction are often confused but have distinct goals. Keyword extraction aims to extract important words from a document. In contrast, keyphrase extraction targets grouped words that form phrases. Think of keyword extraction as a part of keyphrase extraction—they use similar techniques.

  • Text Summarization: Text summarization is an NLP technique where a lengthy document is condensed while keeping its meaning intact. Keyphrase extraction helps in summarization by ensuring essential phrases are included, irrespective of the document's size

Both techniques differ in their usage and implementation. Summaries are often generated by extracting key sentences from a document. This technique, known as extractive text summarization, is similar to those used for extracting phrases. However, more advanced NLP techniques are now used to generate summaries in practice.

  • Information Extraction: Information extraction involves retrieving organized information like dates, location or any relevant information in a text document. In contrast, keyphrase extraction is used to identify key terms or phrases that represent the themes of a document. Since text is unstructured, we use information extraction to pull out useful details in a structured way. Techniques like Named Entity Recognition (NER) are valuable for information extraction tasks.

Techniques for Keyphrase Extraction

Keyphrase Extraction Techniques can be categorized into two groups:

  • Supervised techniques.

  • Unsupervised techniques.

Supervised Keyphrase Extraction Techniques

In this method, you train a model with a dataset of labeled keyphrases for a particular domain. For instance, we can have a dataset of keyphrases for a specific domain, like business. When extracting keyphrases from a new document, the model decides whether each candidate phrase is a keyphrase.

While supervised techniques excel in their domain, creating the training dataset is time-consuming. They may perform poorly in different subjects due to specific training data characteristics.

In supervised keyphrase extraction, tasks can be either classification (deciding if a phrase is a keyphrase) or ranking (assigning ranks to phrases). The Ranking SVM, using a support vector machine, is an example of a model for ranking tasks.

Unsupervised Keyphrase Extraction Techniques

Unsupervised keyphrase extraction techniques do not rely on a pre-existing dataset to train a model for extraction. Instead, they use methods ranging from analyzing a text's linguistic properties to utilizing language models for extraction.

Frequency-based method: TF-IDF

This simple and effective technique extracts phrases by focusing on their frequency in a document. It identifies commonly occurring word groups, assuming that important phrases will be repeated several times in the document.

Term Frequency (TF) is one such approach that is popularly used, especially in keyword extraction. It involves extracting the most frequently occurring word in the document. However, it can also be employed in keyphrase extraction by considering n-grams greater than 1.

Inverse Document Frequency (IDF) complements TF by assessing term rarity across documents. It can also analyze multiple paragraphs in one document. The phrases with the highest IDF in the document are considered keyphrases.

While frequency-based approaches work in some cases, they have limitations. They might catch frequently occurring word sequences, but not all repetitions form meaningful phrases. 

Frequency-based approaches can be effective in some contexts, but they have limitations that make them less reliable in certain situations. For example, while these approaches may identify frequently occurring word sequences, not all repetitions are meaningful phrases.

Linguistics method: Part-of-speech (POS) tagging

This technique involves breaking down the text into words and labeling each word with its part of speech (POS). Then, using predefined rules and patterns (regular expression), phrases are extracted based on linguistic properties like nouns, verbs, adjectives, etc., using regular expressions. 

For instance, we can use a pattern to detect phrases without a subject and predicate. Regular expressions are also handy for spotting nouns, adjectives, and verb phrases.

POS tagging is often the first step to finding potential phrases in a document. These phrases then go through a more advanced process to identify the actual keyphrases.

Vector Embeddings: Word2Vec, Doc2Vec and Glove

Embedding is another technique employed for keyphrase extraction. This process involves converting potential keyphrases into vector representations and then comparing them with the vector representation of the document. Using vector embedding for NLP was initially introduced with the Word2Vec algorithm, which proposed that words with similar meanings have similar vector representations. 

Later developments, such as Doc2Vec,  extended this concept to entire documents. GloVe (Glove Vectors), on the other hand, applies the same principles as Word2Vec but with a simple difference. GloVe uses global co-occurrence statistics across the entire corpus, while Word2Vec focuses on local context.

Evaluation Metrics for Keyphrase Extraction

When evaluating keyphrase extraction algorithms, one common approach is treating the task as a binary classification problem, where the algorithm predicts whether a candidate phrase is a keyphrase. But not all keyphrases are equally relevant to the text document. Some keyphrases may have a stronger relationship with the document content than others. Therefore, it's essential to use evaluation metrics that consider the ranking of keyphrases.

This leads us to our two categories of metrics for Keyphrase Extraction: 

  • Traditional Metrics 

  • Rank-based Metrics.

Traditional Metrics

These metrics are commonly employed in classification tasks. They include precision, recall, and the F1 score. Here's how you use them for keyphrase extraction:

  • Precision: Precision for keyphrase extraction is the proportion of correctly identified keyphrases extracted by the algorithm. In other words, it measures the accuracy of the extracted keyphrases.

  • Recall: Recall is the proportion of correctly identified keyphrases among all the relevant keyphrases in the document or corpus. It measures the completeness of the keyphrase extraction process.

  • F1 Score: The F1 score combines precision and recall into one metric, using the harmonic mean. In keyphrase extraction, we're not evaluating the model across all possible categories but focusing on its accuracy in extracting a set number of keyphrases from a document.

These evaluation metrics are similar to top-k classification tasks, where the model is assessed on predicting the top-k classes with the highest confidence scores. For keyphrase extraction, metrics like precision@k, recall@k, and F1 score@k specifically evaluate the model's effectiveness in identifying the most relevant keyphrases within a given limit, resembling top-k classification scenarios.

Rank-Based Metric

These metrics treat the keyphrase extraction task as a ranking problem, evaluating each extracted keyphrase based on its relevance to the document content.

  • Mean Reciprocal Rank (MRR): Mean Reciprocal Rank (MRR) is used to evaluate the effectiveness of ranking the extracted keyphrases. It measures the average quality of the ranking by assessing the reciprocal of the rank of the first correctly extracted keyphrase in the list of candidate keyphrases. In other words, MRR quantifies how quickly the algorithm can identify relevant keyphrases, with a higher MRR value indicating better performance. 

  • Mean Average Precision (MAP): Mean Average Precision (MAP) is used to evaluate the overall ranking quality produced by the extraction algorithm across multiple documents. It calculates the average precision for each document and then computes the mean of these average precisions.

  • Normalized Discounted Cumulative Gain (nDCG): Normalized Discounted Cumulative Gain (nDCG) assesses how well the extracted keyphrases are ranked, considering their relevance and position in the list. It calculates the total gain by adding the relevance scores of keyphrases, and adjusting for their position. The ideal score then normalizes the gain to get the nDCG.

Challenges in Keyphrase Extraction

Keyphrase extraction encounters several challenges that can impede the performance of even state-of-the-art algorithms. Here are a few of these challenges:

  • Loss of Context: Context is often lost once a keyphrase is extracted from a document. It becomes challenging to discern the relevance of a phrase when it is isolated from other words that provide additional context. As a result, the extracted phrase may be erroneously ranked lower than its actual relevance.

  • Ambiguity due to Polysemy: Polysemy is when a phrase has multiple meanings. Ambiguity arises due to this. A keyphrase algorithm must navigate through all of this in order to correctly identify and extract the most contextually relevant keyphrase. 

  • Adaptation to Different Languages: A challenge in keyphrase extraction is smoothly transferring learned phrases between languages. Since each language has its own rules, techniques like POS tagging don't work well across languages. Even language embedding models need retraining. This requires specific methods for each language and constant reassessment to ensure effectiveness across different languages.

  • Adaptation to a new domain: This challenge is common in supervised keyphrase extraction models. They find it hard to apply what they learn from one knowledge domain to another because each domain has unique keyphrases. While unsupervised models may help with domain-specific issues, past research shows that supervised models often perform better than unsupervised ones.

Real-world Applications

Keyphrase extraction is applied to various industries. The following are a couple of applications:

  • Search Engine Optimization (SEO) for Digital Content: Keyphrases are essential for enhancing by improving how content ranks on search engines. A keyphrase extraction algorithm helps find relevant keyphrases, which can be added to metadata, used as alternative text for images, and inform ad creation on platforms like Google Ads. Utilizing keyphrases improves the SEO performance of content.

  • Business Intelligence through Customer Feedback Analysis: Keyphrase extraction helps businesses understand customer feedback from various sources like social media, surveys, and reviews. By analyzing these keyphrases, businesses can learn about customer sentiments and preferences. This helps them identify trends and patterns in feedback, revealing what aspects of their products or services are most important to customers.

Conclusion

In summary, keyphrase extraction is an important part of understanding written content. It's versatile, helping in summarizing, trend analysis, and decoding customer feedback, among other use cases. Despite challenges like language nuances and changing content, refining extraction techniques and exploring new metrics are essential. 

With its power to uncover meaningful insights and improve information retrieval, keyphrase extraction sits at the intersection of language understanding and computational efficiency, revealing the essence of text with precision and clarity.

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