Yes, the keyphrase filtering model could be significantly enhanced by integrating additional features that capture semantic and contextual information beyond the basic keyphrase-document pairs. One approach would be to utilize embeddings from pre-trained language models, such as BERT or Sentence-BERT, to represent both the keyphrases and the documents in a high-dimensional semantic space. This would allow the model to assess the relevance of keyphrases based on their semantic similarity to the document content, rather than relying solely on surface-level matches.
Incorporating contextual information, such as the document's topic, genre, or intended audience, could also improve the filtering process. For instance, using metadata associated with the documents (e.g., publication date, author information, or source) could help the model better understand the context in which the keyphrases are being evaluated. This contextual awareness could lead to more accurate filtering decisions, as the model would be able to discern which keyphrases are more likely to be relevant based on the specific characteristics of the document.
Additionally, employing techniques such as attention mechanisms could allow the model to focus on specific parts of the document that are most relevant to the keyphrases being evaluated. This would enhance the model's ability to filter out irrelevant keyphrases that may not align with the core content of the document.
Overall, by incorporating these advanced features, the keyphrase filtering model could achieve higher accuracy in identifying relevant keyphrases, thereby improving the overall quality of the keyphrase generation process.