I'm new to AWS CloudSearch and have set up my first domain. It only has one basic text index field.
I've tried a number of simple searches and – more often than not – I get different relevance scores across documents where it seems they should be the same. Even searching for one simple word, which matches exactly once in a number of documents, often produces different scores.
Is this supposed to happen? If so, why?
This is normal. Document length is one factor that will affect this. Think about it: finding your query in a 5 word document indicates a better match than finding your query in a 1000 word document.
The current version of CloudSearch uses Solr/Lucene, an Apache project, so you can dig into the internals to your heart's content if you'd like to learn more. Here is the Similarity which discusses the underlying scoring algorithm in Lucene.
As your app matures, you may want to look into custom ranking of your results. CloudSearch provides this capability as well as a tool for comparing the results according to different rankers. You aren't able to customize the base document relevance score but you can boost it according to different fields, etc.
Related
Our clients query on our Azure Search index, mostly for people's names. We are using the Lucene analyzer for all of our fields. We build the query string by making the client's input name into a phrase, and adding proximity rate of 3. Because we search using a phrase, we can not use the Fuzzy Search capability of the Lucene analyzer, as it only works on single words.
We were therefore in search of a solution for being able to bring back results with names that weren't spelled exactly as the client input them. We came across the phonetic analyzer, and have just implemented the Metaphone algorithm into our index. We've run some tests and while it gets us closer to what we need, we still see some issues:
The analyzer's scope is so wide, that it's bringing back a lot of false positives. For example, when searching on Kenneth Gooden, it brings back Kenneth Cotton. That's just a little too far to be considered phonetically similar, in our opinion. Can the sensitivity be tweaked in any way, or, can something be done to boost some other parameter to remedy this?
When doing a search on Barry Soper, the first and highest-scored result that comes back is "Barry Spear." The second result, scored lower, is "Soper, Barry Russell." To a certain extent, I can maybe see why it's scored that way (b/c of the 2nd one being last name first) but then... not really. The 2nd result contains both exact terms within the required proximity. Maybe Azure Search gives priority to the order of words in the phrase before applying the analyzer? Still doesn't make sense to me. (Side note - this query also brings back "Barh Super" - see issue #1 above)
I would like to know if someone could offer suggestions to tweak Azure Search's behavior to work more along the lines of what we need, OR, perhaps suggest an alternative to the phonetic analyzer. We haven't tried any of the the other available phonetic algorithms either yet, only b/c it seems Metaphone is the best and most commonly-used. But we're open to suggestions regarding the other algorithms as well.
Thanks.
You are correct that the fuzzy operator only works on single terms. In this case, you can use a custom analyzer (phonetic tokenfilter) or Synonyms feature (in preview). I am not sure what you meant by "we have just implemented the Metaphone algorithm into our index" but there are several phonetic tokenfilters you can choose from in Azure Search custom analysis stack. Synonyms is a newer feature only available in preview, you can take a look here. For synonyms, you will need to define synonyms rules, say 'Nate, Nathan, Nathaniel' for example, and at query time, searching for one automatically includes the results for the others.
Okay, then how should I use these building blocks in a way to control relevance for my search? One way to model is to use separate field for each expansion strategy. For example, instead of a single field for the name, you can have three fields, say 'name', 'name_synonym', and 'name_phonetic'. The first field 'name' is for exact matches, 'name_synonym' field has synonyms enabled and the third uses a phonetic analyzer and broadens the search the most. You can then use the scoring profile to boost scores from matches in each field. You can give the boost value of 10 for exact matches, 5 for synonyms and 1 for phonetic expansions, for example. Your search will be issued against these three internal fields.
Regarding your question as to why 'Soper, Barry Russell' is ranked lower than 'Barry Spear'. After the phonetic analysis. the words 'soper' and 'spear' reduce to the same form both at indexing and query time and treated as if they were identical terms. In computing the score and ranking, the search engine uses analyzed form of the terms and phonetic similarity makes no influence to the score. That’s why, secondary factors, like field length, will play a more significant role influencing the relevance score.
Hope this helps. I provided one example to model this but you could also take a look at term boosting in the full lucene query syntax.
Let me know if you have any additional questions.
Nate
I am working with large number of documents and have a set of predefined categories/tags(could be phrases) that would be present in the text of the documents either in the exact or inexact form.
I want to assign each document to exactly one tag among the tags that is closest to its text.
Please give me some directions as to what should I do to address this problem.
You can look at the lucene search engine that tags the documents while indexing. Northernlight search engine used to do a similar task mentioned by you in their searching methodology. You can have a look at its implementation in order to get an idea.
Association mining seems to give good results for retrieving related terms in text corpora. There are several works on this topic including well-known LSA method. The most straightforward way to mine associations is to build co-occurrence matrix of docs X terms and find terms that occur in the same documents most often. In my previous projects I implemented it directly in Lucene by iteration over TermDocs (I got it by calling IndexReader.termDocs(Term)). But I can't see anything similar in Solr.
So, my needs are:
To retrieve the most associated terms within particular field.
To retrieve the term, that is closest to the specified one within particular field.
I will rate answers in the following way:
Ideally I would like to find Solr's component that directly covers specified needs, that is, something to get associated terms directly.
If this is not possible, I'm seeking for the way to get co-occurrence matrix information for specified field.
If this is not an option too, I would like to know the most straightforward way to 1) get all terms and 2) get ids (numbers) of documents these terms occur in.
You can export a Lucene (or Solr) index to Mahout, and then use Latent Dirichlet Allocation. If LDA is not close enough to LSA for your needs, you can just take the correlation matrix from Mahout, and then use Mahout to take the singular value decomposition.
I don't know of any LSA components for Solr.
Since there are still no answers to my questions, I have to write my own thoughts and accept it. Nevertheless, if someone propose better solution, I'll happily accept it instead of mine.
I'll go with co-occurrence matrix, since it is the most principal part of association mining. In general, Solr provides all needed functions for building this matrix in some way, though they are not as efficient as direct access with Lucene. To construct matrix we need:
All terms or at least the most frequent ones, because rare terms won't affect result of association mining by their nature.
Documents where these terms occur, again, at least top documents.
Both these tasks may be easily done with standard Solr components.
To retrieve terms TermsComponent or faceted search may be used. We can get only top terms (by default) or all terms (by setting max number of terms to take, see documentation of particular feature for details).
Getting documents with the term in question is simply search for this term. The weak point here is that we need 1 request per term, and there may be thousands of terms. Another weak point is that neither simple, nor faceted search do not provide information about the count of occurrences of the current term in found document.
Having this, it is easy to build co-occurrence matrix. To mine association it is possible to use other software like Weka or write own implementation of, say, Apriori algorithm.
You can get the count of occurrences of the current term in found document in the following query:
http://ip:port/solr/someinstance/select?defType=func&fl=termfreq(field,xxx),*&fq={!frange l=1}termfreq(field,xxx)&indent=on&q=termfreq(field,xxx)&sort=termfreq(field,xxx) desc&wt=json
For a toy project, I want to implement an automated question answering system with Lucene and I'm trying to figure out a reasonable way to implement it. The basic operation is as follows:
1) The user will enter a question.
2) The system will identify the keywords in the question.
3) The keywords will be searched in a large knowledgebase and matching sentences will be shown as answers.
My knowledgebase (i.e., corpus) is not structured. It is just a large, continuous text (say, a user manual without any chapters). I mean that the only structure is that sentences and paragraphs are identified.
I plan to treat each sentence or paragraph as a separate document. To present the answer in a context, I may consider keeping one sentence/paragraph before/after the indexed one as payload. I would like to know if that makes sense. Also, I'm wondering if there are other tried and well-known approaches for that kind of systems. As an example, another approach that comes to mind is to index large chunks of the corpus as documents with the token positions, then process the vicinity of found keywords to construct my answers.
I would appreciate direct recommendations based on experience or intuition, but also tutorials or introductory materials to question-answering systems with Lucene in mind.
Thanks.
It's not an unreasonable approach to take.
One enhancement you might consider is incorporating learning feedback, so that you can continually improve the scoring of content vs search terms. To do this you would ask users to rate the answers that come back ('helpful vs unhelpful'), that way you can start to rank documents against keywords based on the historical data. You could classify potential documents as helpful/unhelpful for given keywords by using a simple Bayesian classifier.
Indexing each sentence as a document will give you some problems. You've pointed out one: you would need to store the surrounding texts a payloads. That means you'll need to store each sentence three times (before, during and after), and you'll have to manually get into the payload.
If you want to go the route of each sentence being a document, I would recommend coming up with an ID for each sentence and storing that as a separate field. Then you can display [ID-1, ID, ID+1] in each result.
The bigger question though is: how should you break up the text into documents? Identifying semantically related areas seems difficult, so doing it by sentence/paragraph might be the only way to go. A better way would be if you could find which text is the header of a section, and then put everything in that section as a document.
You might also want to use the index (if your corpus has one). The terms there could be boosted, as they are presumably more important.
Instead of luncene which does text indexing, search and retrieval, I think using something like Apache Mahout would help with this. Mahout considers text as knowledge and doing that makes the answering the question better than just text matching. Mahout is a machine learning and data mining f/w which fits this domain better. Just a very high level thought.
--Sai
Is there a way to itterate through all of the terms held against a particular document in a Lucene.NET index?
Basically I want to be able to retrieve a Document from the Index based on it's ID and then find the frequency with which each Term is used in that Document. Does anyone know a way to do this?
I can find the number of Documents that match a particular Term but not the Terms contained within a particular Document.
Many thanks,
Tim
In Lucene Java, at least, one of the options when indexing a document is storing the term frequency vector. The term frequency vector is simply a list of all the terms in a given field of a document, and how often each of those terms was used. Getting the term frequency vector at runtime involves calling a method in the IndexReader with the Lucene ID of the document in question.