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<Paper uid="W00-1102">
  <Title>Exploiting Lexical Expansions and Boolean Compositions for Web Querying</Title>
  <Section position="5" start_page="18" end_page="19" type="evalu">
    <SectionTitle>
4 Results and discussion
</SectionTitle>
    <Paragraph position="0"> During the assessing process, some requested URLs were not retrieved. We have a total of 546 URLs and 516 retrieved web documents, meaning that about 6% of URLs were not  methods and URLs retrieved during the assessing process.</Paragraph>
    <Paragraph position="1"> Table 2 shows the assessments on the KAS search method, which we consider the baseline of the experiment, being search by keywords a standard search method on the Web.</Paragraph>
    <Paragraph position="2"> Results are presented for three partitions of the question set. QS1 is the subset of questions whose number of morphological derivations and synonyms is higher than three; QS2 is the subset whose number of lexical expansions is equal to two or three; QS3 is the subset whose number of lexical expansions is lower than two. The table reports the average values of f. (i.e. document order not considered) and f/ (i.e. order considered) with respect to each partition. The obtained values, f 0.23 and f/ 0.25, indicate that, on average, about 2 web documents have an answer in context assessment and 7 web documents have noanswer no context assessment out of 10 documents returned by this method.</Paragraph>
    <Paragraph position="3">  relevance values f. (without position) and f/ (with position) of retrieved web documents returned by KAS method.</Paragraph>
    <Paragraph position="4"> Table 3 reports the relevance values for the documents retrieved respectively by KIS and KCS. For KIS we have a growth of the 19% and 13% compared with the KAS method. For KCS the average growth is 33 % and 22% compared with KAS. On QS2 there is a remarkable improvement in the KCS performances compared with KAS (+59% and +77%). In this case the average value off+ is greater than f. meaning that KCS recovers good web documents in a better position than KAS. On QS3 there is also a good performance of both KIS and KCS compared with K.AS (+18% and +17% for KIS, +23% and +17% for KCS).</Paragraph>
    <Paragraph position="5"> On the contrary, on the subset QS1 both KIS and KCS performances are comparable to KAS.</Paragraph>
    <Paragraph position="6">  relevance with respect to K/kS.</Paragraph>
    <Paragraph position="7"> From the data presented here it does not emerge a clear correlation between the performance of a search method and the number of lexical expansions. It can be noted that both KIS and KCS perform quite well, compared with KAS, on the set of questions having no expansions. This can be explained because KIS and KCS create queries less restrictive than KAS and are able to recover the same documents of KAS as well as other documents that can be meaningful. In case lexical expansions are present, the best performance compared with KAS is carried out by KCS method on question 1 (Figure 6), which have a total of four derivations and four synonyms. In this case K.AS recovered two documents and KCS more than ten documents, improving also the answer in context assessments thanks to both the morphological derivation &amp;quot;invenzione&amp;quot; (&amp;quot;invention&amp;quot;) and the synonym &amp;quot;lampadina elettrica&amp;quot; (&amp;quot;electric lamp&amp;quot;).</Paragraph>
    <Paragraph position="8">  It is not clear if synonyms affect search performance more than morphological derivation or vice versa. It seems that synonyms and morphological derivations are significant expansions in the same way. If we consider the set of the questions characterised by an improvement in the KCS and KIS performance compared with K.AS performance, then there are four questions having the number of synonyms greater than the number of morphological derivations, three questions having the number of synonyms lower than the number of morphological derivations and three questions having the number of synonyms equal to the number of morphological derivations (zero included).</Paragraph>
    <Paragraph position="9"> If we consider the set of questions having the number of synonyms higher than the number of morphological derivations, then there are four cases out of eight where KIS and KCS enhance the performance of KAS. If instead we consider the set of questions having the number of synonyms lower than the number of morphological derivations there are three cases out of six where KIS and KCS enhance the performance of KAS.</Paragraph>
    <Paragraph position="10"> Finally, Table 4 synthetically shows how KIS and KCS perform with respect to document &amp;quot;context retrieval&amp;quot;, that is the degree of contextual information provided by the document with respect to the question, no matter if the answer to the question was present or not in the document itself. To focus on context we set the tuning functions tx(x) and ~(x) to tx(O )=0, or(l)= 1, tx(2)=O, ot(3)=1/3 and ~(x)=O. The reason for considering a context retrieval score is that, in case the answer is not present, context increases the probability that other relevant documents can be found following hypertextual links, possibly including the correct answer to the question.</Paragraph>
    <Paragraph position="11"> Results obtained with KIS and KCS confirm that they provide a significant increase (from 31% to 41%) of context retrieval score.</Paragraph>
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