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<Paper uid="P06-1107">
  <Title>using selectional preferences</Title>
  <Section position="4" start_page="849" end_page="850" type="metho">
    <SectionTitle>
2 Selectional Preferences and Verb
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="849" end_page="850" type="sub_section">
      <SectionTitle>
Entailment
</SectionTitle>
      <Paragraph position="0"> Selectional restrictions are strictly related to entailment. When a verb or a noun expects a modifier having a predefined property it means that the truth value of the related sentences strongly depends on the satisfiability of these expectations.</Paragraph>
      <Paragraph position="1"> For example, &amp;quot;X is blue&amp;quot; implies the expectation that X has a colour. This expectation may be seen as a sort of entailment between &amp;quot;being a modifier of that verb or noun&amp;quot; and &amp;quot;having a property&amp;quot;. If the sentence is &amp;quot;The number three is blue&amp;quot;, then the sentence is false as the underlying entailment blue(x) - has colour(x) does not hold (cf.</Paragraph>
      <Paragraph position="2"> (Resnik, 1993)). In particular, this rule applies to verb logical subjects: if a verb v has a selectional restriction requiring its logical subjects to satisfy a property c, it follows that the implication:</Paragraph>
      <Paragraph position="4"> should be verified for each logical subject x of the verb v. The implication can also be read as: if x has the property of doing the action v this implies that x has the property c. For example, if the verb is to eat, the selectional restrictions of to eat would imply that its subjects have the property of being animate.</Paragraph>
      <Paragraph position="5"> Resnik (1993) introduced a smoothed version of selectional restrictions called selectional preferences. These preferences describe the desired properties a modifier should have. The claim is that if a selectional preference holds, it is more probable that x has the property c given that it modifies v rather than x has this property in the general case, i.e.:</Paragraph>
      <Paragraph position="7"> The probabilistic setting of selectional preferences also suggests an entailment: the implication v(x) - c(x) holds with a given degree of certainty. This definition is strictly related to the probabilistic textual entailment setting in (Glickman et al., 2005).</Paragraph>
      <Paragraph position="8"> We can use selectional preferences, intended as probabilistic entailment rules, to induce entailment relations among verbs. In our case, if a verb vt expects that the subject &amp;quot;has the property of doing an action vh&amp;quot;, this may be used to induce that the verb vt probably entails the verb vh, i.e.:</Paragraph>
      <Paragraph position="10"> As for class-based selectional preference acquisition, corpora can be used to estimate these particular kinds of preferences. For example, the sentence &amp;quot;John McEnroe won the match...&amp;quot; contributes to probability estimation of the class-based selectional preference win(x) human(x) (since John McEnroe is a human). In particular contexts, it contributes also to the induction of the entailment relation between win and play, as John McEnroe has the property of playing. However, as the example shows, classes relevant for acquiring selectional preferences (such as human) are explicit, as they do not depend from the context. On the contrary, properties such as &amp;quot;having the property of doing an action&amp;quot; are less explicit, as they depend more strongly on the context of sentences. Thus, properties useful to derive entailment relations among verbs are more difficult to find. For example, it is easier to derive that John McEnroe is a human (as it is a stable property) than that he has the property of playing. Indeed, this latter property may be relevant only in the context of the previous sentence.</Paragraph>
      <Paragraph position="11"> However, there is a way to overcome this limitation: agentive nouns such as runner make explicit this kind of property and often play subject roles in sentences. Agentive nouns usually denote the &amp;quot;doer&amp;quot; or &amp;quot;performer&amp;quot; of some action. This is exactly what is needed to make clearer the relevant property vh(x) of the noun playing the logical sub-ject role. The action vh will be the one entailed by the verb vt heading the sentence. As an example in the sentence &amp;quot;the player wins&amp;quot;, the action play  evocated by the agentive noun player is entailed by win.</Paragraph>
      <Paragraph position="12"> 3 Verb entailment: a classification The focus of our study is on verb entailment. A brief review of the WordNet (Miller, 1995) verb hierarchy (one of the main existing resources on verb entailment relations) is useful to better explain the problem and to better understand the applicability of our hypothesis.</Paragraph>
      <Paragraph position="13"> In WordNet, verbs are organized in synonymy sets (synsets) and different kinds of semantic relations can hold between two verbs (i.e.</Paragraph>
      <Paragraph position="14"> two synsets): troponymy, causation, backwardpresupposition, and temporal inclusion. All these relations are intended as specific types of lexical entailment. According to the definition in (Miller, 1995) lexical entailment holds between two verbs vt and vh when the sentence Someone vt entails the sentence Someone vh (e.g. &amp;quot;Someone wins&amp;quot; entails &amp;quot;Someone plays&amp;quot;). Lexical entailment is then an asymmetric relation. The four types of WordNet lexical entailment can be classified looking at the temporal relation between the entailing verb vt and the entailed verb vh.</Paragraph>
      <Paragraph position="15"> Troponymy represents the hyponymy relation between verbs. It stands when vt and vh are temporally co-extensive, that is, when the actions described by vt and vh begin and end at the same times (e.g. limp-walk). The relation of temporal inclusion captures those entailment pairs in which the action of one verb is temporally included in the action of the other (e.g. snore-sleep). Backwardpresupposition stands when the entailed verb vh happens before the entailing verb vt and it is necessary for vt. For example, win entails play via backward-presupposition as it temporally follows and presupposes play. Finally, in causation the entailing verb vt necessarily causes vh. In this case, the temporal relation is thus inverted with respect to backward-presupposition, since vt precedes vh. In causation, vt is always a causative verb of change, while vh is a resultative stative verb (e.g. buy-own, and give-have).</Paragraph>
      <Paragraph position="16"> As a final note, it is interesting to notice that the Subject-Verb structure of vt is generally preserved in vh for all forms of lexical entailment. The two verbs have the same subject. The only exception is causation: in this case the subject of the entailed verb vh is usually the object of vt (e.g., X give Y - Y have). In most cases the subject of vt carries out an action that changes the state of the object of vt, that is then described by vh.</Paragraph>
      <Paragraph position="17"> The intuition described in Sec. 2 is then applicable only for some kinds of verb entailments. First, the causation relation can not be captured since the two verbs should have the same subject (cf.</Paragraph>
      <Paragraph position="18"> eq. (2)). Secondly, troponymy seems to be less interesting than the other relations, since our focus is more on a logic type of entailment (i.e., vt and vh express two different actions one depending from the other). We then focus our study and our experiments on backward-presupposition and temporal inclusion. These two relations are organized in WordNet in a single set (called ent) parted from troponymy and causation pairs.</Paragraph>
    </Section>
  </Section>
  <Section position="5" start_page="850" end_page="852" type="metho">
    <SectionTitle>
4 The method
</SectionTitle>
    <Paragraph position="0"> Our method needs two steps. Firstly (Sec. 4.1), we translate the verb selectional expectations in specific Subject-Verb lexico-syntactic patterns P(vt,vh). Secondly (Sec. 4.2), we define a statistical measure S(vt,vh) that captures the verb preferences. This measure describes how much the relations between target verbs (vt,vh) are stable and commonly agreed.</Paragraph>
    <Paragraph position="1"> Our method to detect verb entailment relations is based on the idea that some point-wise assertions carry relevant semantic information. This idea has been firstly used in (Robison, 1970) and it has been explored for extracting semantic relations between nouns in (Hearst, 1992), where lexico-syntactic patterns are induced by corpora.</Paragraph>
    <Paragraph position="2"> More recently this method has been applied for structuring terminology in isa hierarchies (Morin, 1999) and for learning question-answering patterns (Ravichandran and Hovy, 2002).</Paragraph>
    <Section position="1" start_page="850" end_page="851" type="sub_section">
      <SectionTitle>
4.1 Nominalized textual entailment
</SectionTitle>
      <Paragraph position="0"> lexico-syntactic patterns The idea described in Sec. 2 can be applied to generate Subject-Verb textual entailment lexico-syntactic patterns. It often happens that verbs can undergo an agentive nominalization, e.g., play vs. player. The overall procedure to verify if an entailment between two verbs (vt,vh) holds in a point-wise assertion is: whenever it is possible to apply the agentive nominalization to the hypothesis vh, scan the corpus to detect those expressions in which the agentified hypothesis verb is the subject of a clause governed by the text verb vt.</Paragraph>
      <Paragraph position="1"> Given a verb pair (vt,vh) the assertion is for-</Paragraph>
      <Paragraph position="3"/>
      <Paragraph position="5"> malized in a set of textual entailment lexico-syntactic patterns, that we call nominalized patterns Pnom(vt,vh). This set is described in Tab. 1.</Paragraph>
      <Paragraph position="6"> agent(v) is the noun deriving from the agentification of the verb v. Elements such as l|f1,...,fN are the tokens generated from lemmas l by applying constraints expressed via the feature-value pairs f1,...,fN. For example, in the case of the verbs play and win, the related set of textual entailment expressions derived from the patterns are</Paragraph>
      <Paragraph position="8"> win&amp;quot;, &amp;quot;player won&amp;quot;, &amp;quot;players won&amp;quot;}. In the experiments hereafter described, the required verbal forms have been obtained using the publicly available morphological tools described in (Minnen et al., 2001). Simple heuristics have been used to produce the agentive nominalizations of verbs1.</Paragraph>
      <Paragraph position="9"> Two more sets of expressions, Fagent(v) and F(v) representing the single events in the pair, are needed for the second step (Sec. 4.2).</Paragraph>
      <Paragraph position="10"> This two additional sets are described in Tab. 1. In the example, the derived expressions</Paragraph>
      <Paragraph position="12"/>
    </Section>
    <Section position="2" start_page="851" end_page="852" type="sub_section">
      <SectionTitle>
4.2 Measures to estimate the entailment
</SectionTitle>
      <Paragraph position="0"> strength The above textual entailment patterns define point-wise entailment assertions. If pattern instances are found in texts, the related verb-subject pairs suggest but not confirm a verb selectional preference. 1Agentive nominalization has been obtained adding &amp;quot;-er&amp;quot; to the verb root taking into account possible special cases such as verbs ending in &amp;quot;-y&amp;quot;. A form is retained as a correct nominalization if it is in WordNet.</Paragraph>
      <Paragraph position="1"> The related entailment can not be considered commonly agreed. For example, the sentence &amp;quot;Like a writer composes a story, an artist must tell a good story through their work.&amp;quot; suggests that compose entails write. However, it may happen that these correctly detected entailments are accidental, that is, the detected relation is only valid for the given text. For example, if the text fragment &amp;quot;The writers take a simple idea and apply it to this task&amp;quot; is taken in isolation, it suggests that take entails write, but this could be questionable.</Paragraph>
      <Paragraph position="2"> In order to get rid of these wrong verb pairs, we perform a statistical analysis of the verb selectional preferences over a corpus. This assessment will validate point-wise entailment assertions.</Paragraph>
      <Paragraph position="3"> Before introducing the statistical entailment indicator, we provide some definitions. Given a corpus C containing samples, we will refer to the absolute frequency of a textual expression t in the corpus C with fC(t). The definition can be easily extended to a set of expressions T.</Paragraph>
      <Paragraph position="4"> Given a pair vt and vh we define the following entailment strength indicator S(vt,vh).</Paragraph>
      <Paragraph position="5"> Specifically, the measure Snom(vt,vh) is derived from point-wise mutual information (Church and Hanks, 1989):</Paragraph>
      <Paragraph position="7"> where nom is the event of having a nominalized textual entailment pattern and pers is the event of having an agentive nominalization of verbs. Probabilities are estimated using maximum-likelihood:</Paragraph>
      <Paragraph position="9"> p(vt) [?] fC(F(vt))/fC(uniontextF(v)), and p(vh|pers) [?] fC(Fagent(vh))/fC(uniontextFagent(v)). Counts are considered useful when they are greater or equal to 3.</Paragraph>
      <Paragraph position="10"> The measure Snom(vt,vh) indicates the relatedness between two elements composing a pair, in line with (Chklovski and Pantel, 2004; Glickman et al., 2005) (see Sec. 5). Moreover, if Snom(vt,vh) &gt; 0 the verb selectional preference property described in eq. (1) is satisfied.</Paragraph>
    </Section>
  </Section>
  <Section position="6" start_page="852" end_page="852" type="metho">
    <SectionTitle>
5 Related &amp;quot;non-distributional&amp;quot; methods
</SectionTitle>
    <Paragraph position="0"> and integrated approaches Our method is a &amp;quot;non-distributional&amp;quot; approach for detecting semantic relations between verbs. We are interested in comparing and integrating our method with similar approaches. We focus on two methods proposed in (Chklovski and Pantel, 2004) and (Glickman et al., 2005). We will shortly review these approaches in light of what introduced in the previous sections. We also present a simple way to combine these different approaches.</Paragraph>
    <Paragraph position="1"> The lexico-syntactic patterns introduced in (Chklovski and Pantel, 2004) have been developed to detect six kinds of verb relations: similarity, strength, antonymy, enablement, and happensbefore. Even if, as discussed in (Chklovski and Pantel, 2004), these patterns are not specifically defined as entailment detectors, they can be useful for this purpose. In particular, some of these patterns can be used to investigate the backward-presupposition entailment. Verb pairs related by backward-presupposition are not completely temporally included one in the other (cf. Sec. 3): the entailed verb vh precedes the entailing verb vt. One set of lexical patterns in (Chklovski and Pantel, 2004) seems to capture the same idea: the happens-before (hb) patterns. These patterns are used to detect not temporally overlapping verbs, whose relation is semantically very similar to entailment. As we will see in the experimental section (Sec. 6), these patterns show a positive relation with the entailment relation. Tab. 1 reports the happens-before lexico-syntactic patterns (Phb) as proposed in (Chklovski and Pantel, 2004).</Paragraph>
    <Paragraph position="2"> In contrast to what is done in (Chklovski and Pantel, 2004) we decided to directly count patterns derived from different verbal forms and not to use an estimation factor. As in our work, also in (Chklovski and Pantel, 2004), a mutualinformation-related measure is used as statistical indicator. The two methods are then fairly in line.</Paragraph>
    <Paragraph position="3"> The other approach we experiment is the &amp;quot;quasi-pattern&amp;quot; used in (Glickman et al., 2005) to capture lexical entailment between two sentences.</Paragraph>
    <Paragraph position="4"> The pattern has to be discussed in the more general setting of the probabilistic entailment between texts: the text T and the hypothesis H. The idea is that the implication T - H holds (with a degree of truth) if the probability that H holds knowing that T holds is higher that the probability that H holds alone, i.e.:</Paragraph>
    <Paragraph position="6"> This equation is similar to equation (1) in Sec. 2.</Paragraph>
    <Paragraph position="7"> In (Glickman et al., 2005), words in H and T are supposed to be mutually independent. The previous relation between H and T probabilities then holds also for word pairs. A special case can be applied to verb pairs:</Paragraph>
    <Paragraph position="9"> Equation (5) can be interpreted as the result of the following &amp;quot;quasi-pattern&amp;quot;: the verbs vh and vt should co-occur in the same document. It is possible to formalize this idea in the probabilistic entailment &amp;quot;quasi-patterns&amp;quot; reported in Tab. 1 as Ppe, where verb form variability is taken into consideration. In (Glickman et al., 2005) point-wise mutual information is also a relevant statistical indicator for entailment, as it is strictly related to eq. (5).</Paragraph>
    <Paragraph position="10"> For both approaches, the strength indicator Shb(vt,vh) and Spe(vt,vh) are computed as follows: null</Paragraph>
    <Paragraph position="12"> pe for the probabilistic entailment patterns. Probabilities are estimated as in the previous section.</Paragraph>
    <Paragraph position="13"> Considering independent the probability spaces where the three patterns lay (i.e., the space of subject-verb pairs for nom, the space of coordinated sentences for hb, and the space of documents for pe), the combined approaches are obtained summing up Snom, Shb, and Spe. We will then experiment with these combined approaches: nom+pe, nom+hb, nom+hb+pe, and hb+pe.</Paragraph>
  </Section>
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