

Proposal for Senseval Scoring Scheme
I. Dan Melamed & Philip Resnik
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1.  A principled evaluation metric can be derived by assigning
    probabilities over sense tags output by WSD algorithms.
    Algorithms that output multiple tags but do not assign
    probabilities can be treated as assigning uniform probabilities
    over the tags that they output.  E.g. an algorithm that considers
    senses A and B as possible, but eliminates senses C, D and E for a
    word with 5 senses in the reference inventory is really saying:

    sense     prob.
    -----     -----
      A	       .5
      B	       .5
      C	       0
      E	       0
      D	       0

2.  Given a probability distribution over sense tags and a single
    known correct tag, the algorithm's score should be the probability
    that the algorithm assigns to the correct tag.  Note that the
    exact-match criterion falls out as a special case of this metric,
    where the algorithm selects exactly one sense, which is equivalent
    to assigning 100% of the probability mass to it.


3.  Given multiple possible correct tags for a given word token, the
    algorithm's score should be the sum of ALL probabilities that it
    assigns to ANY of the correct tags.  The premise here is that it
    is impossible to tell whether a multi-sense annotation was
    intended as disjunctive or conjunctive, so algorithms should be
    given the benefit of the doubt.  E.g. annotator tags a word with
    senses A and B:

    algorithm's output		score
    ------------------		-----
    A				1
    .5 A; .5 B			1
    .3 A; .7 C			.3
    .3 A; .4 B; .3 C		.7
    A, B, C			2/3


4.  The probabilistic treatment of sense tags can be extended to
    handle tree-structured tagsets, such as HECTOR, if the structure
    is interpreted as an IS-A hierarchy.  E.g., if sense 3.2 is a
    sub-sense of sense 3, then any word token of sense 3.2 *also* IS-A
    token of sense 3.  (Further extensions exist for more general
    DAGs, such as WordNet, but they don't concern us here.)

    The same scoring criterion can be used for structured tagsets as
    for unstructured ones: What's the probability that the algorithm
    assigns to any of the correct tags?  The complication for
    structured tagsets is that it is not obvious how to compare tags
    that are in a parent-child relationship.  This problem can be
    solved by defining two kinds of probability distributions:
    Pr(occurrence of parent sense | occurrence of child sense) and
    Pr(occurrence of child sense | occurrence of parent sense).  The
    first one is easy: In a tree-structured IS-A hierarchy,
    Pr(occurrence of parent node | occurrence of child node) = 1.  The
    second one is harder, unfortunately; in general, these
    ("downward") probabilities are unknown.  In the absence of prior
    knowledge about sense distributions over particular sense-tree
    branches, the maximum entropy principle dictates that we assign a
    uniform distribution over Pr(occurrence of child sense |
    occurrence of parent sense) for each sense.  Fortunately, this is
    not such a bad assumption.  It will be false in most individual
    cases, but if we evaluate WSD algorithms by averaging performance
    over many different word types, most of the biases should come out
    in the wash.

    Now, how do we use these conditional probabilities for scoring?
    Treat each non-leaf sense-tag as underspecified.  E.g. if sense 3
    has just the two subsenses 3.1 and 3.2, then tagging a word with
    sense 3 is equivalent to giving it a probability of one half of
    being sense 3.1 and one half of being sense 3.2, given our
    assumption of uniform downward probabilities.  This applies both
    to the tags in the output of WSD algorithms and to the manual
    (correct, reference) annotations.

    Example:

    Suppose our sense-tree for a given word has senses 1 and 2, which
    are not subdivided in any way.  It has sense 3 divided into 3.1
    and 3.2, as above.  In addition 3.1 is subdivided into 3.1a and
    3.1b.  There is also a sense 4, which is split into 4.1, 4.2 and
    4.3.

    Under our assumption of uniform downward probabilities, we start
    by deducing:

    Pr(3.1 | 3) = .5
    Pr(3.1a | 3.1) = .5
    (and so) Pr(3.1a | 3) = .25

    Pr(4.2 | 4) = 1/3, etc.

    If any of the conditionals above are reversed, then the
    probability is always 1.  E.g. Pr(3 | 3.1a) = 1.

    Next, we apply these probabilities to find Pr(any correct sense |
    algorithm's assigned senses) as in the following example
    cases:

    manual annotation	algorithm`s output	score
    -----------------	------------------	-----
    
	2			3		  0
	3			3		  1
	3			3.1		  1
	3			3.1b		  1
	3.1			3		  .5
	3.1 & 3.2		3		  .5 + .5* = 1
	3.1a			3		  .25
	3.1a & 4.2		4		  1/3 (= Pr(4.2 | 4))
	3.1a & 4.2		3.1		  .5
	3.1a & 4.2		3.1 & 4.2	  .5*.5 + .5*1 = .75
	3.1a & 4.2		3.1 & 4		  .5*.5 + .5*.333 = .41666


5.  This scoring scheme depends only on the tree structure of the
    hierarchy, and not on the types of nodes in it.  In particular,
    questions of whether part-of-speech and other syntactic
    distinctions should be part of the sense inventory are orthogonal
    to the issue addressed here.

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References: 

This proposal incorporates some ideas from section 3.1 of

Philip Resnik and David Yarowsky, "A perspective on word sense
    disambiguation methods and their evaluation", position paper
    presented at the ACL SIGLEX Workshop on Tagging Text with Lexical
    Semantics: Why, What, and How?, held April 4-5, 1997 in
    Washington, D.C., USA in conjunction with ANLP-97.

available from 
http://www.umiacs.umd.edu/~resnik/papers/siglex97_perspective.ps



