insight - Machine Learning Keyphrase Extraction - # Supervised Keyphrase Extraction

An Efficient Supervised Approach for Keyphrase Extraction without Reliance on External Knowledge

Q: How can the proposed approach be extended to handle absent keyphrases (keyphrases not present in the document text)?

The proposed approach for keyphrase extraction primarily focuses on extracting keyphrases that are present in the document text. To extend this approach to handle absent keyphrases, which are keyphrases that do not directly appear in the document but represent important concepts or topics discussed, several modifications can be made. One way is to incorporate topic modeling techniques such as Latent Dirichlet Allocation (LDA) or Non-Negative Matrix Factorization (NMF) to identify latent topics in the document and then generate keyphrases based on these topics. By analyzing the context and semantic relationships within the document, the model can predict keyphrases that are relevant but not explicitly mentioned. Another approach is to leverage external knowledge bases or ontologies to enrich the keyphrase extraction process. By integrating domain-specific knowledge graphs or semantic networks, the model can infer keyphrases that are related to the document content but may not be explicitly stated. This external knowledge can provide a broader context for keyphrase extraction and enhance the model's ability to identify relevant keyphrases, both present and absent in the document text.

Q: What are the potential limitations of the supervised approach in terms of generalization to new domains or document types beyond academic papers?

While the supervised approach presented in the context demonstrates high accuracy in keyphrase extraction, there are potential limitations when it comes to generalizing to new domains or document types beyond academic papers. Some of the limitations include: Domain-specific features: The model relies on features that are predominantly based on statistical and positional properties of candidate phrases within the training data. These features may not capture the nuances or specific characteristics of different domains, making it challenging to generalize to diverse document types. Training data bias: The model is trained on specific datasets, such as academic papers, which may not represent the full spectrum of document types and topics. This bias in the training data can limit the model's ability to adapt to new domains with different writing styles, vocabulary, or content structures. Limited external knowledge: The approach does not leverage external knowledge bases or pre-trained language models, which could provide valuable information for keyphrase extraction in various domains. Without access to domain-specific knowledge, the model may struggle to generalize effectively across different document types. Subjectivity in keyphrase selection: The choice of keyphrases in the training data, which is manually annotated, may introduce subjectivity and inconsistency in the selection process. This can impact the model's generalization to new domains where keyphrase selection criteria may vary.

Q: How could the model's performance be further improved by incorporating additional features or leveraging external knowledge in a lightweight manner?

To enhance the model's performance in keyphrase extraction, several strategies can be employed by incorporating additional features or leveraging external knowledge in a lightweight manner: Semantic features: Integrate semantic features such as word embeddings or contextual embeddings to capture the semantic relationships between words and phrases in the document. This can help the model understand the context and meaning of keyphrases more effectively. Graph-based features: Incorporate lightweight graph-based features to represent the relationships between words or phrases in the document. Graph algorithms like TextRank can be used to identify key phrases based on their centrality and connectivity within the document. Domain-specific dictionaries: Utilize domain-specific dictionaries or ontologies to enrich the keyphrase extraction process. By incorporating specialized terminology or domain knowledge, the model can better identify keyphrases that are relevant to specific domains. Transfer learning: Explore transfer learning techniques to fine-tune the model on new domains or document types. By leveraging pre-trained models or knowledge from related tasks, the model can adapt more easily to different domains without requiring extensive retraining. Ensemble models: Combine the strengths of multiple models or approaches, such as supervised and unsupervised methods, to create an ensemble model that leverages the benefits of each approach. This can improve the robustness and performance of the keyphrase extraction system.

Core Concepts

A lightweight supervised machine learning approach for automatic keyphrase extraction that uses simple statistical and positional features, without relying on any external knowledge base or pre-trained language models.

Abstract

The author presents a novel supervised learning approach for automatic extraction of keyphrases from single documents. The proposed solution uses simple statistical and positional features of candidate phrases and does not rely on any external knowledge base or pre-trained language models.

The key highlights of the approach are:

The author frames keyphrase selection as a partial ranking problem, where the goal is to find a scoring function that assigns higher scores to true keyphrases compared to non-keyphrases. Two variants are explored - a direct ranking model using XGBoost Ranker, and a classification model using XGBoost Classifier.
The statistical features used include phrase count, document frequency (max-scaled), suffix phrase frequency, suffix phrase document frequency, suffix phrase average per-doc frequency, and word combination likelihood. Positional features include first occurrence index and n-gram size.
The author evaluates the model on two benchmark datasets - SemEval2010 and Krapivin. The results show that the proposed approach outperforms several state-of-the-art unsupervised and supervised baselines, including deep learning-based models, in terms of F1-score. On the SemEval2010 dataset, the model's performance is competitive with some supervised deep learning-based models.
A key advantage of the proposed approach is that it does not rely on any external knowledge base or pre-trained language models, making it more domain-agnostic and able to generalize better compared to prior non-deep learning supervised solutions.

Customize Summary

Rewrite with AI

Generate Citations

Translate Source

To Another Language

Generate MindMap

from source content

Visit Source

arxiv.org

Stats

The average number of words per document in the Krapivin dataset is 8040, with an average of 6.34 keyphrases per document, of which 15.3% are absent keyphrases.
The average number of words per document in the SemEval2010 dataset is 8332, with an average of 16.47 keyphrases per document, of which 11.3% are absent keyphrases.

Quotes

"Our solution uses simple to compute statistical and positional features of candidate phrases and does not rely on any external knowledge base or on pre-trained language models or word embeddings."
"Evaluation on benchmark datasets shows that our approach achieves significantly higher accuracy than several state-of-the-art baseline models, including all deep learning-based unsupervised models compared with, and is competitive with some supervised deep learning-based models too."

Key Insights Distilled From

An efficient domain-independent approach for supervised keyphrase extraction and ranking

by Sriraghavend... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07954.pdf

An efficient domain-independent approach for supervised keyphrase extraction and ranking

Deeper Inquiries

How can the proposed approach be extended to handle absent keyphrases (keyphrases not present in the document text)?

The proposed approach for keyphrase extraction primarily focuses on extracting keyphrases that are present in the document text. To extend this approach to handle absent keyphrases, which are keyphrases that do not directly appear in the document but represent important concepts or topics discussed, several modifications can be made. One way is to incorporate topic modeling techniques such as Latent Dirichlet Allocation (LDA) or Non-Negative Matrix Factorization (NMF) to identify latent topics in the document and then generate keyphrases based on these topics. By analyzing the context and semantic relationships within the document, the model can predict keyphrases that are relevant but not explicitly mentioned.
Another approach is to leverage external knowledge bases or ontologies to enrich the keyphrase extraction process. By integrating domain-specific knowledge graphs or semantic networks, the model can infer keyphrases that are related to the document content but may not be explicitly stated. This external knowledge can provide a broader context for keyphrase extraction and enhance the model's ability to identify relevant keyphrases, both present and absent in the document text.

What are the potential limitations of the supervised approach in terms of generalization to new domains or document types beyond academic papers?

While the supervised approach presented in the context demonstrates high accuracy in keyphrase extraction, there are potential limitations when it comes to generalizing to new domains or document types beyond academic papers. Some of the limitations include:

Domain-specific features: The model relies on features that are predominantly based on statistical and positional properties of candidate phrases within the training data. These features may not capture the nuances or specific characteristics of different domains, making it challenging to generalize to diverse document types.

Training data bias: The model is trained on specific datasets, such as academic papers, which may not represent the full spectrum of document types and topics. This bias in the training data can limit the model's ability to adapt to new domains with different writing styles, vocabulary, or content structures.

Limited external knowledge: The approach does not leverage external knowledge bases or pre-trained language models, which could provide valuable information for keyphrase extraction in various domains. Without access to domain-specific knowledge, the model may struggle to generalize effectively across different document types.

Subjectivity in keyphrase selection: The choice of keyphrases in the training data, which is manually annotated, may introduce subjectivity and inconsistency in the selection process. This can impact the model's generalization to new domains where keyphrase selection criteria may vary.

How could the model's performance be further improved by incorporating additional features or leveraging external knowledge in a lightweight manner?

To enhance the model's performance in keyphrase extraction, several strategies can be employed by incorporating additional features or leveraging external knowledge in a lightweight manner:

Semantic features: Integrate semantic features such as word embeddings or contextual embeddings to capture the semantic relationships between words and phrases in the document. This can help the model understand the context and meaning of keyphrases more effectively.

Graph-based features: Incorporate lightweight graph-based features to represent the relationships between words or phrases in the document. Graph algorithms like TextRank can be used to identify key phrases based on their centrality and connectivity within the document.

Domain-specific dictionaries: Utilize domain-specific dictionaries or ontologies to enrich the keyphrase extraction process. By incorporating specialized terminology or domain knowledge, the model can better identify keyphrases that are relevant to specific domains.

Transfer learning: Explore transfer learning techniques to fine-tune the model on new domains or document types. By leveraging pre-trained models or knowledge from related tasks, the model can adapt more easily to different domains without requiring extensive retraining.

Ensemble models: Combine the strengths of multiple models or approaches, such as supervised and unsupervised methods, to create an ensemble model that leverages the benefits of each approach. This can improve the robustness and performance of the keyphrase extraction system.