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Adding to the Final Lexicon

After deriving initial candidates, the greedy search begins. The heuristic used to evaluate candidates attempts to help assure that a small but covering lexicon is learned. The heuristic first looks at the weighted sum of two components, where p is the phrase and m its candidate meaning:
$P(m\mid p) \times P(p\mid m) \times P(m) = P(p) \times P(m\mid p)^2$
The generality of m
Then, ties in this value are broken by preferring less ambiguous (those with fewer current meanings) and shorter phrases. The first component is analogous the cluster evaluation heuristic used by COBWEB [Fisher1987], which measures the utility of clusters based on attribute-value pairs and categories, instead of meanings and phrases. The probabilities are estimated from the training data and then updated as learning progresses to account for phrases and meanings already covered. We will see how this updating works as we continue through our example of the algorithm. The goal of this part of the heuristic is to maximize the probability of predicting the correct meaning for a randomly sampled phrase. The equality holds by Bayes Theorem. Looking at the right side, $P(m\mid p)^2$ is the expected probability that meaning m is correctly guessed for a given phrase, p. This assumes a strategy of probability matching, in which a meaning m is chosen for p with probability $P(m\mid p)$ and correct with the same probability. The other term, P(p), biases the component by how common the phrase is. Interpreting the left side of the equation, the first term biases towards lexicons with low ambiguity, the second towards low synonymy, and the third towards frequent meanings. The second component of the heuristic, generality, is computed as the negation of the number of vertices in the meaning's tree structure, and helps prefer smaller, more general meanings. For example, in the candidate set above, if all else were equal, the generality portion of the heuristic would prefer state(_), with generality value -1, over largest(_,state(_)) and (state(S),loc(_,S)), each with generality value -2, as the meaning of ``estado''. Learning a meaning with fewer terms helps evenly distribute the vertices in a sentence's representation among the meanings of the phrases in that sentence, and thus leads to a lexicon that is more likely to be correct. To see this, we note that some pairs of words tend to frequently co-occur (``grande'' and ``estado'' in our example), and so their joint representation (meaning) is likely to be in the set of candidate meanings for both words. By preferring a more general meaning, we easily ignore these incorrect joint meanings. In this example and all experiments, we use a weight of 10 for the first component of the heuristic, and a weight of 1 for the second. The first component has smaller absolute values and is therefore given a higher weight. Modulo this consideration, results are not overly-sensitive to the weights and automatically setting them using cross-validation on the training set [Kohavi John1995] had little effect on overall performance. In Table 2 we illustrate the calculation of the heuristic measure for some of the above fourteen pairs, and its value for all. The calculation shows the sum of multiplying 10 by the first component of the heuristic and multiplying 1 by the second component. The first component is simplified as follows:

\begin{displaymath}P(p) \times P(m\mid p)^2=
\frac{\mid p\mid}{t} \times \frac{...
... p \mid ^2} \approx
\frac{\mid m \cap p \mid^2}{\mid p \mid},\end{displaymath}

where $\mid p\mid$ is the number of times phrase p appears in the corpus, t is the initial number of candidate phrases, and $\mid m \cap p \mid$ is the number of times that meaning m is paired with phrase p. We can ignore t since the number of phrases in the corpus is the same for each pair, and has no effect on the ranking.
Table 2: Heuristic Value of Sample Candidate Lexical Entries
nn \lq\lq capital'', {\tt population(S,\_),state(S)}): ...
..., loc(\_,S))}): \>
$10(1^2/1) + 1(-2) =8$\space %

The highest scoring pair is (``estado'', state(_)), so it is added to the lexicon. Next is the candidate generalization step (2.2), described algorithmically in Figure 8. One of the key
Figure 8: The Candidate Generalization Phase
\begin{figure}\hrulefill %
nn\=nn\=nn\=nn\=nn\=nn\=nn\= \kill
...> calculate their heuristic values.

ideas of the algorithm is that each phrase-meaning choice can constrain the candidate meanings of phrases yet to be learned. Given the assumption that each portion of the representation is due to at most one phrase in the sentence (exclusivity), once part of a representation is covered, no other phrase in the sentence can be paired with that meaning (at least for that sentence). Therefore, in step 2.2 the candidate meanings for words in the same sentences as the word just learned are generalized to exclude the representation just learned. We use an operation analogous to set difference when finding the remaining uncovered vertices of the representation when generalizing meanings to eliminate covered vertices from candidate pairs. For example, if the meaning largest(_,_) were learned for a phrase in sentence 2, the meaning left behind would be a forest consisting of the trees high_point(S,_) and (state(S), area(S,_)). Also, if the generalization results in an empty tree, new LICS are calculated. In our example, since state(_) is covered in sentences 1, 2, 3, 6, and 7, the candidates for several other words in those sentences are generalized. For example, the meaning (state(S), loc(_,S)) for ``encuentra'', is generalized to loc(_,_), with a new heuristic value of 10(12/1) + 1(-1)=9. Also, our single-use assumption allows us to remove all candidate pairs containing ``estado'' from the set of candidate meanings, since the learned pair covers all occurrences of ``estado'' in that set. Note that the pairwise matchings to generate candidate items, together with this updating of the candidate set, enable multiple meanings to be learned for ambiguous phrases, and makes the algorithm less sensitive to the initial rate of sampling for LICS. For example, note that ``capital'' is ambiguous in this data set, though its ambiguity is an artifact of the way that the query language was designed, and one does not ordinarily think of it as an ambiguous word. However, both meanings will be learned: The second pair added to the final lexicon is (``grande'', largest(_,_)), which causes a generalization to the empty meaning for the first candidate entry in Table 2, and since no new LICS from sentence 4 can be generated, its entire remaining meaning is added to the candidate meaning set for both ``capital'' and ``más.'' Subsequently, the greedy search continues until the resulting lexicon covers the training corpus, or until no candidate phrase meanings remain. In rare cases, learning errors occur that leave some portions of representations uncovered. In our example, the following lexicon is learned:
(``estado'', state(_)),

(``grande'', largest(_)),

(``area'', area(_)),

(``punta'', high_point(_,_)),

(``población'', population(_,_)),

(``capital'', capital(_,_)),

(``encuentra'', loc(_,_)),

(``alta'', loc(_,_)),

(``bordean'', next_to(_)),

(``capital'', capital(_)).
In the next section, we discuss the ability of WOLFIE to learn lexicons that are useful for parsers and parser acquisition.
next up previous
Next: Evaluation of WOLFIE Up: Our Solution: WOLFIE Previous: Candidate Generation Phase
Cindi Thompson