Some extras about the term necessity work

There are several important things I didn’t get to talk about at cikm 2010.  The talk focused on technical understandings of the term necessity concept and its role in term weighting.  Below lists future work that the IR field can do, and also some related work I didn’t cover.

I’ll also tell a bit of background story/history of how we arrived at the term necessity work from the previous structured retrieval work we’ve done, (Michael Bendersky asked me this question when at cikm, so at least there is some external interest).

1. There is a lot more future work possible than any one group of people can finish in one project, so I’d like to see more people working on necessity prediction and applying necessity to retrieval in different ways.  After more than 30 years of not much progress, we understand now that term necessity/recall is an important part of term weighting, (the other part being idf).  Since necessity lies in the core of the retrieval model, many kinds of intervention in retrieval are possible.  As term weighting for original query terms, for expansion terms, and/or bigrams.  As a feature to guide expansion to those terms that tend to mismatch relevant documents, or in other totally new approaches.

The important sanity check is to make sure there are in fact lots of mismatch cases between query terms and relevant documents in your dataset.  Otherwise, mismatch prediction won’t help much.  High mismatch rate senarios include e.g. long queries, or short documents, or complex query terms such as phrases or field restrictions etc.

2. Besides the ones listed in my slides, Regression Rank (Matt Lease), Term importance (Bendersky), are a few must reads.  I’ll have a fairly complete review of related work in my thesis proposal this December, if you want a rough draft, email me, or look at the WikiQuery website for a list of queries I used in doing the survey, you can follow the links on the pages to find results from Google or Yahoo.

3. How did I end up in this work from structured retrieval?

A short version of the story:

Working with sentence retrieval (short documents) and trying to find a good form of structured query leads us to using the conjunctive normal form Boolean queries, which basically tries to improve term recall.  Teaching the IR class on probabilistic models lead us to revisit the binary independence model, which lead to the understanding that P(t|R) is term necessity (necessity of a term occurring in a document in order for the document to be relevant), also just term recall (the percentage of relevant documents matching the term).  Connecting them together, we get the experiments and the paper.

A longer version of the same story:

The initial influence came from the TREC Legal track.  Yangbo and I participated in the 2nd year of the Legal track, and for both the two years, to the disappointment of all participants, no keyword ranking approach could beat the lawyers’ (Boolean, roughly conjunctive normal form – CNF) structured queries.  We were simply frustrated, without much understanding of the reason behind.  I remember Doug Oard (one of the organizers of the track) even suggested to take the top keyword ranked documents that don’t match the Boolean filter to replace the bottom ranked documents that match the Boolean filter.  Whether the idea worked or not can be found in a later year’s TREC report from Doug’s group, but that’s not so important for the current story.  The important thing is that we were all frustrated, and didn’t realize that the lawyers are actually using queries more superior than all of our current retrieval models/techniques.

The next piece of work is the CIKM 2009 poster, most of the experiments for the poster happened much earlier in summer 2008.  We automatically created conjunctive normal form (CNF) Boolean filters from the questions, to filter out top false positive sentences for a question answering application.  For the question, When did Wilt Chamberlain score 100 points?  the final Boolean filter is: (Wilt OR Chamberlain) AND (100 OR points) AND #any:date.  For the TREC Legal track, we were not involved in creating the queries, the lawyers did for us.  But now that we looked at the actual rank list returned by the keyword query, we realized that this kind of Boolean queries are very effective at filtering out false positives caused by the keyword retrieval model biasing toward high-idf terms.  For example, #any:date will match any “date” named-entity tag, which has extremely low idf, so lots of the top sentences don’t even contain a date.  But a date field is necessary to answer the question.  The term “100” also tend to be de-emphasized by the retrieval model, while Wilt and Chamberlain and score would be preferred.  Because the newswire collection contain lots of sentences about Wilt scoring many points in one season or one year etc., these irrelevant sentences will be returned at top rank.  After this work, the CNF Boolean queries firmly held its position in my mind.

The next important link in the chain of reasoning actually came from teaching the probabilistic models in Jamie’s IR class in the Spring semester of 2009 (using mostly Jamie’s slides).  When trying to explain the Binary Independence model, the probability P(t|R) showed up.  Greiff’s exploratory data analysis showed that this probability is difficult to estimate, so it draw my attention, at the time, even though only subconsciously.  I named the probability term necessity, and when teaching, I tried to explain to the students, and some of them understood it and accepted the name.

After summer 2008, I became more and more obsessed with the CNF Boolean queries.  I discovered Marti Hearst (1996)’s and Mandar Mitra et al (1998)’s  interesting work in manually formulating CNF queries (Mandar even kindly gave me the Boolean filters from his PhD thesis which was about 10 years ago!), I discovered that librarians also use such kind of queries.  And at the beginning of summer 2009, without the TA burden, I set out to automatically build CNF Boolean structured queries for ad hoc retrieval.  The initial approach was just naively grouping expansion terms into the original query concept (concept groups are from Mandar’s Boolean disjunctions), it was not successful (not better than the baseline Boolean filtered keyword retrieval).  Halfway into the summer, we decided to make it smarter by introducing term necessity predictions, so that the algorithm can know which terms to expand.  That attempt was also not very successful, and the reason is because most of the expansion terms are not related to the query concept at all.  The poor term similarity measure leads me to finally use local SVD to calculate term similarity.  Luckily adaptation of the SVD code in Lemur did not take much time.  But still, time flew by quickly, and per term expansion seemed far away, so I asked Jamie to allow me one more week, just trying to weight the original query terms using the predicted necessity values.  I still clearly remember on the way to the LTI picnic at the end of the summer, I said to Ni Lao (my officemate and good friend) that I need to show some progress within this week, or end the current attempt.

That simple experiment did show a lot of promise, and showed some real (i.e. significant) performance gain over the baseline language model on TREC description (long) queries.

Sometimes, when I look back, I think even if it were not successful, I’ll still ask for more time to analyze the errors made by the model, and try more things to make it work.  I’m quite convinced myself that such CNF queries and term necessity will work, given that the true necessity weights can give 30-80% gain in MAP.  One thing is quite certain though, I was too ambitious at that time.  Now, I’m proposing with the term weighting results, and planning to work on automatic CNF queries as thesis work.

After some good results, the rest is straightforward, though not always easy.  We spent the whole September to December of 2009 writing the work up.  Making it clear the relationship between necessity and term weighting was easy, but telling a convincing story is not.  Justifying the features was a bit tricky but worthwhile.  Including more experiments on more datasets was quite easy.

Another significant event during the experimentation and writing period was a short chat with Yiming Yang briefly at an LTI event.  I explained to her the CNF queries and Yiming immediately said oh, you are improving recall.  This turned up in the SIGIR submission, and more significantly, it made it easy to make the connection between necessity and mismatch.

Term mismatch was in the title of a proposal Jamie and I wrote.

Following that, SIGIR submission and rejection, the story is convincing, but one reviewer picked on a relatively low baseline run.  Ok, fixed.  And following that, CIKM submission and acceptance.  One reviewer from CIKM was extremely thorough, 8 main points and 16 small points about the work.  I took one week of vacation from my summer internship to do more experiments to address the feedback from all the reviewers, which finally made the paper a full 10 page long.  Sometimes I wonder how come such a simple idea would need 10 pages to explain..

After that, the feedback from the proposal also came back.  On average, the proposal reviews are much more serious (helpful) than the conference paper reviewers.  It took me over a week to carefully read through the reviews, addressing each point in my thesis proposal.  One reviewer emphasized the RIA workshop.  I re-examined the report from the workshop, and also dug out an old 2007 email from Ni Lao, summarizing a discussion between us that happened then.  The email clearly summarized two main causes of failure for search engines 1) the emphasis problem, and 2) the mismatch problem.  This became the nice looking pie chart in my cikm talk.  Ni is just amazing!  Unfortunately, this pie chart and the RIA workshop connection is not in the cikm paper, it did not arrive in time for the camera ready deadline.

With the understanding of term necessity/recall/mismatch, everything else just fits in surprisingly nicely.  From CNF Boolean queries to the mismatch rate, to necessity, to the emphasis problem.  Back in 2007, it was unclear how the emphasis problem is caused, we briefly guessed that it’s related to term weighting, but not sure what the optimal weights should be, until revisiting the BIM when teaching the IR class.  And even when creating the pie chart for the cikm 2010 talk, how term necessity connects the emphasis problem and the mismatch problem struck me.

In summary, I am usually surprisingly slow at understanding and generalization, of empirical results and theoretical concepts.  As laid out above, every transition took me months.  But I guess that’s true for most human, that’s why research progresses slowly.  Months of experimentation, writing, teaching and discussion only a couple of really important serendipity moments, and usually you don’t realize the importance of those moments until much later.  The only way out is to keep doing everything (experiments, reading) and keep thinking (writing, talking) at the same time.

I am also not very good at lateral thinking, making connections with existing concepts and papers.  This is relatively easy to solve, usually crystallize the idea, and look at the idea from various different angles will reflect related concepts and approaches.  And talking to lots of people about the idea, plus searching for related work on the Web, will complement the limited knowledge of a single person.

Finally, something practically useful: you have to try by yourself to see how Conjunctive Normal Form queries both increases recall and top precision for your searches.  And here is a useful website for you to easily create such Boolean Conjunctive Normal Form queries, store the queries, search for results, and even share the queries publicly so that other people may benefit from the queries you created.  I’ve used this WikiQuery website for the literature review of my thesis proposal, and find myself constantly coming back to the site to look at existing queries (refinding relevant documents, or finding new documents), and also create new queries as our understanding of the topic increases.


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