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Implementing Conceptual Search in Solr

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4K views|8 years ago
💫 Short Summary

The video discusses the development and implementation of conceptual search techniques, specifically focusing on Word2Vec by Google for automatic word inference. It explores the use of machine learning models, clustering algorithms, and keyword extraction methods to enhance search algorithms and user experience. The speaker emphasizes the importance of mapping terms into concepts for better accuracy, relevancy, and recall in search results. Additionally, the video addresses the utilization of synonym maps, payloads, and clustering for improving search functionality and handling large document volumes efficiently. The overall goal is to provide users with more relevant and precise search results across various domains, including job searches and document processing.

✨ Highlights
📊 Transcript
Introduction to open-source tools and plugins available on GitHub.
Custom MLT hammer and spellcheck plugin mentioned for better accuracy in spellcheck customization.
Work on improving relevancy algorithms for job searches discussed.
Skills page feature on website allows users to explore technology skills like Apache Solr and Elasticsearch.
Ongoing efforts to enhance search algorithms and user experience emphasized.
Conceptual search
Conceptual search, also known as semantic search, learns important concepts in a domain instead of just matching keywords.
The goal is to improve recall by matching documents based on concepts rather than exact query terms or synonyms.
This approach provides more accurate and relevant search results, even if documents don't contain specific query terms.
Automated mechanisms like conceptual search help search engines enhance recall without sacrificing precision.
Conceptual search addresses challenges of traditional keyword matching by mapping terms into concepts.
It helps with ambiguous terms, synonyms, typos, spelling errors, and idioms.
Concepts represent high-level ideas in domains like Java technologies or healthcare support positions.
Machine learning techniques can automatically learn concepts from documents.
Google's Word2Vec algorithm efficiently maps documents to low-dimensional vectors to represent concepts.
Development of Word2Vec by Google in 2013.
Word2Vec trains a machine learning model to predict words before and after a word in a document within a fixed window.
Faster and more scalable than traditional techniques, allowing for mining of synonyms and related terms within a document set.
Training the model on a large dataset like Wikipedia enables Word2Vec to learn concepts automatically, such as gender and capital cities.
Considered state-of-the-art for automatic word inference.
Using Soglin for conceptual search by representing documents as vectors or concepts, learning related words, boosting recall, and automatically learning related terms.
Elastic search and Solar are mentioned as examples of related terms that tend to occur together.
Demonstrating a search for data scientists, programming languages, and job titles to show the effectiveness of the search method in retrieving relevant results.
The search is not limited to tech jobs, as shown by searching for project manager positions.
Methods for document processing within Solar without using a machine learning model.
Python scripts are provided for processing documents, extracting keywords, and training a word-back model.
Different approaches for scaling the process are explored, such as using synonym functionality, embedding raw vectors, and augmenting query expansion terms.
Using a machine learning clustering algorithm to compute clusters for words is discussed.
Having a set of important keywords for domain analysis is emphasized, along with query log mining and analyzing commonly occurring phrases to determine important terms.
Overview of Solar Plugins and Payloads in Solar.
All code is available on GitHub and documentation will be added in the coming weeks.
Solar plugins for term extraction using synonym filters or solar text tagger are included in the repository.
Payloads in solar can tag terms with values for scoring functionality and applications like recommender engines or machine learning algorithms.
The speaker emphasizes the versatility and potential applications of payloads in solar.
Highlights of Using Synonym Maps in Apache Lucene
Synonym maps in Apache Lucene involve associating each term with a payload to improve search results.
The payload is converted into a term boost at query time to enhance search relevance.
Terms can be filtered based on job titles, payloads can be extracted using custom token filters, and payload usage can be configured at query time.
The ability to experiment with different approaches at query time without reindexing is important for sites with large document volumes.
Clustering can be used as an alternative to top-end term mapping for similarity analysis.
Algorithm clusters terms based on weights assigned to different fields.
Clusters related terms like natural language, programming languages, and search engine technologies.
Identifies terms used to describe ideal job candidates, such as 'self-motivated' and 'detail-oriented'.
Conceptual search improves recall by matching documents on related terms and concepts.
Word2Vec algorithm is scalable, efficient, and developed by Google.
Methods for making solar conceptual search without complicated machine learning models.
Clustering was used as a starting point for solar conceptual search.
Keyword extraction approaches were tested, with the second approach of keyword clusters being effective.
Caution is needed when presenting documents to users based on search terms to avoid complaints.
Suggestions for configuring search functionality in production were provided, such as setting high values for phrase and exact matches, and utilizing ranking functions for better matching results.
Evaluation of using DevOps developers and alternative types of developers for tech jobs in high demand.
Method involving vector matching and synonym files to find good matches, similar to LSA inside solar.
Approach has potential but may not scale well and is complex.
Trade-off between accuracy and performance in using certain methods for matching terms.
Job and resume data distinctions and the efficiency of model training in Python.
The importance of clustering and variability in cluster sizes for better results in machine learning.
Utilizing co-occurrence models and document analysis to train the system effectively.
Not all clusters are perfect, but they can still be valuable for refining search results.
Separating training on different document sections and utilizing various fields and weights for improved accuracy.
Mapping multiple words to different clusters to disambiguate problems and enhance context understanding.
The speaker discusses the importance of terms and clusters for network analysis.
Emphasizes the use of commonly occurring terms in documents for efficient data mining.
Mentions the effectiveness of using keywords and closely related terms for analysis.
Highlights the benefits of automated learning and acquiring new skills in sectors like technology.
Stresses the importance of adapting to rapidly changing keywords in certain industries for effective analysis.