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<Paper uid="P04-1048">
  <Title>Inducing Frame Semantic Verb Classes from WordNet and LDOCE</Title>
  <Section position="4" start_page="0" end_page="0" type="metho">
    <SectionTitle>
3 Resources Used in SemFrame
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    <Paragraph position="0"> We adopt an approach that relies heavily on pre-existing lexical resources. Such resources have several advantages over corpus data in identifying semantic frames. First, both Third, lexical resources provide their data in a more systematic fashion than do corpora.</Paragraph>
    <Paragraph position="1"> Most centrally, the syntactic arguments of the verbs used in a definition often correspond to the semantic arguments of the verb being defined.</Paragraph>
    <Paragraph position="2"> For example, Table 1 gives the definitions of several verb senses in LDOCE that evoke the</Paragraph>
  </Section>
  <Section position="5" start_page="0" end_page="0" type="metho">
    <SectionTitle>
COMMERCIAL TRANSACTION frame, which
</SectionTitle>
    <Paragraph position="0"> includes as its semantic arguments a Buyer, a Seller, some Merchandise, and Money. Words corresponding to the Money (money, value), the Merchandise (property, goods), and the Buyer (buyer, buyers) are present in, and to some extent shared across, the definitions; however, no words corresponding to the Seller are present.</Paragraph>
  </Section>
  <Section position="6" start_page="0" end_page="0" type="metho">
    <SectionTitle>
Verb LDOCE Definition
</SectionTitle>
    <Paragraph position="0"> sense buy 1 to obtain (something) by giving money (or something else of value) buy 2 to obtain in exchange for something, often something of great value buy 3 to be exchangeable for purchase 1 to gain (something) at the cost of effort, suffering, or loss of something of value sell 1 to give up (property or goods) to another for money or other value sell 2 to offer (goods) for sale sell 3 to be bought; get a buyer or buyers; gain a sale Table 1. LDOCE Definitions for Verbs Evoking the COMMERCIAL TRANSACTION Frame Of available machine-readable dictionaries, LDOCE appears especially useful for this research. It uses a restricted vocabulary of about 2000 words in its definitions and example sentences, thus increasing the likelihood that words with closely related meanings will use Merge pairs, filtering out those not meeting threshold criteria  the same words in their definitions and support WordNet verb synsets and LDOCE verb senses the pattern of discovery envisioned. LDOCE's relies on finding matches between the data subject field codes also accomplish some of the available for the verb senses in each resource same type of grouping as semantic frames. (e.g., other words in the synset; words in WordNet is a machine-readable lexico- definitions and example sentences; words closely semantic database whose primary organizational related to these words; and stems of these words). structure is the synset--a set of synonymous word The similarity measure used is the average of the senses. A limited number of relationship types proportion of words on each side of the (e.g., antonymy, hyponymy, meronymy, comparison that are matched in the other. This troponymy, entailment) also relate synsets within mapping is used both to relate LDOCE verb senses, a part of speech. (Version 1.7.1 was used.) that map to the same WordNet synset (fig. 3f) and to Fellbaum (1998b) suggests that relationships translate previously paired WordNet verb synsets in WordNet &amp;quot;reflect some of the structure of into LDOCE verb sense pairs. frame semantics&amp;quot; (p. 5). Through the relational In the third stage, the resulting verb sense structure of WordNet, buy, purchase, sell, and pay pairs are merged into a single data set, retaining are related together: buy and purchase comprise one only those pairs whose cumulative support synset; they entail paying and are opposed to sell. exceeds thresholds for either the number of The relationship of buy, purchase, sell, and supporting data sources or strength of support, pay to other COMMERCIAL TRANSACTION thus achieving higher precision in the merged verbs--for example, cost, price, and the demand data set than in the input data sets. Then, the payment sense of charge--is not made explicit in graph formed by the verb sense pairs in the WordNet, however. Further, as Roger Chaffin merged data set is analyzed to find the fully has noted, the specialized vocabulary of, for connected components. example, tennis (e.g. racket, court, lob) is not co- Finally, these groups of verb senses become located, but is dispersed across different branches input to a clustering operation (Voorhees, 1986). of the noun network (Miller, 1998, p. 34). Those groups whose similarity (due to overlap in</Paragraph>
  </Section>
  <Section position="7" start_page="0" end_page="0" type="metho">
    <SectionTitle>
4 SemFrame Approach
</SectionTitle>
    <Paragraph position="0"> SemFrame gathers evidence about frame semantic relatedness between verb senses by analyzing LDOCE and WordNet data from a variety of perspectives. The overall approach used is shown in Figure 1. The first stage of processing extracts pairs of LDOCE and WordNet verb senses that potentially evoke the same frame. By exploiting many different clues to semantic relatedness, we overgenerate these pairs, favoring recall; subsequent stages improve the precision of the resulting data.</Paragraph>
    <Paragraph position="1"> Figures 2 and 3 give details of the algorithms for extracting verb pairs based on different types of evidence. These include: clustering LDOCE verb senses/WordNet synsets on the basis of words in their definitions and example sentences (fig. 2); relating LDOCE verb senses defined in terms of the same verb (fig. 3a); relating LDOCE verb senses that share a common stem (fig. 3b); extracting explicit sense-linking relationships in LDOCE (fig. 3c); relating verb senses that share general or specific subject field codes in LDOCE (fig. 3d); and extracting (direct or extended) semantic relationships in WordNet (fig. 3e).</Paragraph>
    <Paragraph position="2"> In the second stage, mapping between membership) exceed a threshold are merged together, thus reducing the number of verb sense groups. The verb senses within each resulting group are hypothesized to evoke the same  Input. SW, a set of stop words; M, a set of (word, stem) pairs; F, a set of (word, frequency) pairs; DE, a set of (verb_sense_id, def+ex) pairs, where def+ex = the set of words in thed definitions and example sentences of  verb_sense_idd Step 1. forall d DE, append to def+ex : d verb_sense_id and remove fromd def+ex any word w SWd Step 2. forall d DE forall m M if word exists in def+ex , m d substitute stem for wordm m Step 3. forall f F if frequency &gt; 1,f , else if frequency == 1,f Step 4. O Voorhees' average link clustering algorithm applied to DE, with initial weights forall t in def+ex set to wgtt Step 5. forall o O</Paragraph>
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