acl acl2013 acl2013-280 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Mehdi Manshadi ; Daniel Gildea ; James Allen
Abstract: Recent work on statistical quantifier scope disambiguation (QSD) has improved upon earlier work by scoping an arbitrary number and type of noun phrases. No corpusbased method, however, has yet addressed QSD when incorporating the implicit universal of plurals and/or operators such as negation. In this paper we report early, though promising, results for automatic QSD when handling both phenomena. We also present a general model for learning to build partial orders from a set of pairwise preferences. We give an n log n algorithm for finding a guaranteed approximation of the optimal solution, which works very well in practice. Finally, we significantly improve the performance of the pre- vious model using a rich set of automatically generated features.
Reference: text
sentIndex sentText sentNum sentScore
1 edu Abstract Recent work on statistical quantifier scope disambiguation (QSD) has improved upon earlier work by scoping an arbitrary number and type of noun phrases. [sent-3, score-0.739]
2 No corpusbased method, however, has yet addressed QSD when incorporating the implicit universal of plurals and/or operators such as negation. [sent-4, score-0.44]
3 We also present a general model for learning to build partial orders from a set of pairwise preferences. [sent-6, score-0.15]
4 1 Introduction The sentence there is one faculty member in every graduate committee is ambiguous with respect to quantifier scoping, since there are at least two possible readings: If one has wide scope, there is a unique faculty member on every committee. [sent-9, score-0.231]
5 Over the past decade there has been some work on statistical quantifier scope disambiguation (QSD) (Higgins and Sadock, 2003; Galen and MacCartney, 2004; Manshadi and Allen, 2011a). [sent-11, score-0.345]
6 Third, QSD has often been con- sidered only in the context of explicit quantification such as each and every versus some and a/an. [sent-16, score-0.147]
7 For example, Higgins and Sadock (2003) find fewer than 1000 sentences with two or more explicit quantifiers in the Wall Street journal section of Penn Treebank. [sent-18, score-0.145]
8 Furthermore, for more than 60% ofthose sentences, the order ofthe quantifiers does not matter, either as a result of the logical equivalence (as in two existentials), or because they do not have any scope interaction. [sent-19, score-0.293]
9 A deep understanding of this sentence, requires deciding whether each word in the set, referred to by Three words, starts with a potentially distinct capital letter (as in Apple, Orange, Banana) or there is a unique capital letter which each word starts with (as in Apple, Adam, Athens). [sent-28, score-0.393]
10 In general, every plural NP potentially introduces an implicit universal, ranging 1For example, Liang et al. [sent-30, score-0.339]
11 (2011) in their state-of-the-art statistical semantic parser within the domain of natural language queries to databases, explicitly devise quantifier scoping in the semantic model. [sent-31, score-0.515]
12 While explicit universals may not occur very often in natural language, the usage of plurals is very common. [sent-36, score-0.272]
13 , Morante and Blanco, 2012) has investigated automatically detecting the scope and focus of negation. [sent-42, score-0.198]
14 However, the scope of negation with respect to quantifiers is a different phenomenon. [sent-43, score-0.46]
15 Transforming this sentence into a meaning representation language, for almost any practical purposes, requires deciding whether the NP a capital letter lies in the scope of the negation or outside of it. [sent-47, score-0.509]
16 The former describes the preferred reading where The word starts with a lowercase letter as in apple, orange, banana, but the latter gives the unlikely reading, according to which there exists a particular capital letter, say A, that The word starts with, as in apple, Orange, Banana. [sent-48, score-0.218]
17 By not involving negation in quantifier scoping, a semantic parser may produce an unintended interpretation. [sent-49, score-0.314]
18 Their system disambiguates the scope of exactly two explicitly quantified NPs in a sentence, ignoring indefinite a/an, definites and bare NPs. [sent-52, score-0.238]
19 Manshadi and Allen (201 1a), hence MA11, go beyond those limitations and scope an arbitrary number of NPs in a sentence with no restriction on the type ofquantification. [sent-53, score-0.198]
20 However, although their corpus annotates the scope of negations and the implicit universal of plurals, their QSD system does not handle those. [sent-54, score-0.596]
21 As a step towards comprehensive automatic QSD, in this paper we present our work on automatic scoping of the implicit universal of plurals and negations. [sent-55, score-0.805]
22 The new revision, called QuanText, carries a more detailed, fine-grained scope annotation (Manshadi et al. [sent-58, score-0.198]
23 The performance of 2Although plurals carry different types of quantification (Herbelot and Copestake, 2010), almost always there exists an implicit universal. [sent-60, score-0.342]
24 The importance of scoping this universal, however, may vary based on the type of quantification. [sent-61, score-0.368]
25 2 Task definition In QuanText, scope-bearing elements (or, as we call them, scopal terms) of each sentence have been identified using labeled chunks, as in (3). [sent-72, score-0.253]
26 Outscoping relations are used to specify the relative scope of scopal terms. [sent-76, score-0.39]
27 The relation i > j means that chunk ioutscopes (or has wide scope over) chunk j. [sent-77, score-0.51]
28 Equivalently, chunk j is said to have narrow scope with respect to i. [sent-78, score-0.354]
29 Each sentence is annotated with its most preferred scoping (according to the annotators’ judgement), represented as a partial order: 4. [sent-79, score-0.431]
30 This happens ifboth orders are equivalent (as in two existentials) or when the two chunks have no scope interaction. [sent-81, score-0.366]
31 For example, G1 in Figure 1 represents the scoping in (4). [sent-83, score-0.368]
32 In order to take the transitivity of outscoping relations into account, we use the transitive closure (TC) of DAGs. [sent-86, score-0.27]
33 A disadvantage of this metric is that it gives the same weight to outscoping and incomparability relations. [sent-91, score-0.251]
34 In practice, if two scopal terms with equivalent ordering (and hence, no outscoping relation) are incorrectly labeled with an outscoping, the logical form still remains valid. [sent-92, score-0.393]
35 But if an outscoping relation is mislabeled, it will change the interpretation ofthe sentence. [sent-93, score-0.201]
36 Therefore, in MA1 1, we suggest defining a precision/recall based on the number of outscoping relations recovered correctly: 4 P+=|E+p|E∩p+ E|g+|, R+=|Ep+|E∩g+ E|+g| 3 (u, v) (2) ∨ ∈ G+ ⇐⇒ ((u, v) ∈ G ∃∈w 1G . [sent-94, score-0.201]
37 1 Our framework Learning to do QSD Since we defined QSD as a partial ordering, automatic QSD would become the problem of learning to build partial orders. [sent-107, score-0.126]
38 Learning to build partial orders has not attracted much attention in the learning community, although as seen shortly, the techniques developed for ranking can be adopted for learning to build partial orders. [sent-114, score-0.171]
39 Although there could be many DAGs representing a partial order P, only one of those is a transitive DAG. [sent-116, score-0.132]
40 Every non-transitive DAG will be converted into its transitive counterpart by taking its transitive closure (as shown in Figure 1). [sent-118, score-0.138]
41 Therefore, we have reduced the problem of predicting a partial order to pairwise comparison, analogous to ranking by pairwise comparison or RPC (Hullermeier et al. [sent-136, score-0.147]
42 The difference though is that in RPC, the comparison is a (soft) binary classification, while for partial orders we have the case of incomparability (the label ? [sent-138, score-0.158]
43 T−h)e, proof −is) beyond the scope of this paper, but the idea is similar to that of Cohen et al. [sent-152, score-0.198]
44 2 Dealing with plurality and negation Consider the following sentence with the plural NP chunk the lines. [sent-202, score-0.5]
45 SI : (1c > 1d > 2; 1d > 3) 10 In QuanText, plural chunks are indexed with a number followed by the lowercase letter “p”. [sent-206, score-0.308]
46 As seen in (6), the scoping looks different from before in that the terms 1d and 1c are not the label of any chunk. [sent-207, score-0.368]
47 These two terms refer to the two quantified terms introduced by the plural chunk 1p: 1c (for collection) represents the set (or in better words collection) ofentities, defined by the plural, and 1d (for distribution) refers to the implicit universal, introduced by the plural. [sent-208, score-0.493]
48 In other words, for a plural chunk ip, id represents the universally quantified entity over the collection ic. [sent-209, score-0.351]
49 The outscoping relation 1d > 2 in (6) states that every line in the collection, denoted by 1c, starts with its own punctuation character. [sent-210, score-0.28]
50 In QuanText, negation chunks are labeled with an uppercase “N” followed by a number. [sent-214, score-0.29]
51 txt”], which starts with [3/ a capital letter], but does [N1/ not] end with [4/ a capital letter]. [sent-218, score-0.193]
52 SI : (2 > 1> 3; 1> N1 > 4) As seen here, a negation simply introduces a chunk, which participates in outscoping relations like an NP chunk. [sent-220, score-0.368]
53 Figure 4(b) represents the scoping in (8) as a DAG. [sent-221, score-0.368]
54 From these examples, as long as we create two nodes in the DAG corresponding to each plural chunk, and one node corresponding to each negation, there is no need to modify the underlying model (defined in the previous section). [sent-222, score-0.233]
55 However, when u (or v) is a negation (Ni) or an implicit universal (id) node, the probabilities puλ,v (λ ∈ {+, ? [sent-223, score-0.471]
56 3 −, Feature selection Previous work has shown that the lexical item of quantifiers and syntactic clues (often extracted from phrase structure trees) are good at predicting quantifier scoping. [sent-229, score-0.242]
57 Another point to mention here is that the features that are predictive of the relative scope of quantifiers are not necessarily as helpful when determining the scope ofnegation and vice versa. [sent-235, score-0.491]
58 Therefore we do not use exactly the same set of features when 68 one of the scopal terms in the pair11 is a negation, although most of the features are quite similar. [sent-236, score-0.192]
59 1 NP chunks We first describe the set of features we have adopted when both scopal terms in a pair are NPchunks. [sent-239, score-0.315]
60 a Implicit universal of a plural Remember that every plural chunk i introduces two nodes in the DAG, ic and id. [sent-246, score-0.619]
61 Both nodes are introduced by the same chunk i, therefore they use the same set of features. [sent-247, score-0.213]
62 The only exception is a single additional binary feature for plural NP • chunks, which determines whether the given node refers to the implicit universal of the plural (i. [sent-248, score-0.599]
63 In the following, by chunk we mean the deepest phrase-level node in T dominating all the words in the chunk. [sent-259, score-0.213]
64 The choice of the word noun, however, may be sloppy, as the head of an NP chunk may not be a noun. [sent-269, score-0.156]
65 Therefore, for every pair of nodes with one negation, the other node must re- fer to an NP chunk. [sent-277, score-0.156]
66 • Lexical item of the determiner for the NP chunk •• LDoexeisc tahle i eNmP cohf tuhnek dceotnetrmainin ae cr ofonsr tthanet? [sent-279, score-0.186]
67 Of all the NP chunks, 320 (more than 18%) are plural, each introducing an implicit universal, that is, an additional node in the DAG. [sent-301, score-0.235]
68 Therefore, a sentence with n scopal elements creates C(n, 2) = n(n 1)/2 samples for classifcirceaatiteosn. [sent-303, score-0.253]
69 C W(nh,e2n) a =ll nth(en e −lem 1)e/n2ts s are tlaekse fno rin ctloa account,12 the total number of samples in the corpus will be: − 12Here by all elements we mean explicit chunks and the implicit universals. [sent-304, score-0.412]
70 In particular, some nouns introduce two entities: a type and a 69 XC(ni,2) ≈ 4500 (9) Xi Where ni is the number of scopal terms introduced by sentence i. [sent-306, score-0.192]
71 Out of the 4500 samples, around 1800 involve at least one implicit universal (i. [sent-307, score-0.304]
72 We evaluate the performance ofthe system for implicit universals and negation both separately and in the context of full scope disambiguation. [sent-310, score-0.656]
73 14 In order to run the baseline system on implicit universals, we take the feature vector of a plural NP and add a feature to indicate that this feature vector represents the implicit universal of the corresponding chunk. [sent-317, score-0.601]
74 Similarly, for negation we add a feature to show that the chunk represents a negation. [sent-318, score-0.323]
75 13Since the percentage of sentences with negation is small, we made sure that those sentences are distributed uniformly between three sets. [sent-328, score-0.167]
76 However, similar to plurals, NP conjunctions (disjunctions) introduce an extra scopal element: a universal (existential). [sent-333, score-0.318]
77 457 (a) Scoping explicit NP chunks BnO aevusgreamrtlainoldne (salMyn(sARdt1eiPm C)p-S(liVcnMtcl-u1nd3ivn)egrsal)0σ. [sent-341, score-0.173]
78 A457 (b) Scoping all elements (including id and Ni) Figure 5: Performance on QuanText data tem on the task that it was actually defined to perform (that is scoping only explicit NP chunks). [sent-347, score-0.515]
79 A sentence counts as correct iff every pair of scopal elements has been labeled correctly. [sent-350, score-0.332]
80 In computing AR, a sentence counts as correct iff all the outscoping relations have been recovered correctly in other words, iff R = 100%, regardless – of the value of P. [sent-354, score-0.275]
81 AR may be more practically meaningful, because if in the correct scoping of the sentence there is no outscoping between two elements, inserting one does not affect the interpretation ofthe sentence. [sent-355, score-0.569]
82 Figure 5(b) gives the performance of the overall model when all the elements including the implicit universals and the negations are taken into account. [sent-357, score-0.446]
83 042 higher than F-score for the first indicates that scoping implicit universals and/or negations must be easier than scoping explicit NP chunks. [sent-359, score-1.171]
84 In order to find how much one or both ofthe two elements contribute to this gain, we have run two more experiments, scoping only the pairs with at least one implicit universal and pairs with one negation, respectively. [sent-360, score-0.733]
85 As seen, the contribution in boosting the overall performance comes from the implicit universals while negations, in fact, lower the performance. [sent-362, score-0.291]
86 The performance for pairs with implicit universal is higher because universals, in general, 70 wIBOmiuatshrpealmitcnoieldt e(ualMsn(AtiRov1Pen1Cer)-sSiaVdlM)s-o1n3l)y(pairs0P. [sent-363, score-0.304]
87 F65+2394 (a) Pairs with at least one implicit universal (b) Pairs with at least one negation Figure 6: Implicit universals and negations are easier to scope, even for the human annotators. [sent-369, score-0.678]
88 Scoping a negation relative to an NP chunk, with which it has a long distance dependency, often depends on the scope of the elements in between. [sent-373, score-0.426]
89 Third, scoping negation usually requires a deep semantic analysis. [sent-374, score-0.566]
90 In order to see how well our approximation algorithm is working, similar to the approach of Chambers and Jurafsky (2008), we tried an ILP solver18 for DAGs with at most 8 nodes to find the optimum solution, but we found the difference insignificant. [sent-375, score-0.137]
91 19 5 Related work Since automatic QSD is in general challenging, traditionally quantifier scoping is left underspecified in deep linguistic processing systems (Alshawi and Crouch, 1992; Bos, 1996; Copestake et al. [sent-377, score-0.546]
92 In or17Trivially, we have taken the relation outscoping ic > id for granted and not counted it towards higher performance. [sent-382, score-0.237]
93 der to evade scope disambiguation, yet be able to perform entailment, Koller and Thater (2010) propose an algorithm to calculate the weakest readings20 from a scope-underspecified representation. [sent-389, score-0.224]
94 The first three only scope two scopal terms in a sentence, where the scopal term is an NP with an explicit quantification. [sent-392, score-0.632]
95 MA1 1 is the first to scope any number of NPs in a sentence with no restric- tion on the type of quantification. [sent-393, score-0.198]
96 Besides ignoring negation and implicit universals, their system has some other limitations too. [sent-394, score-0.345]
97 6 Summary and future work We develop the first statistical QSD model addressing the interaction of quantifiers with negation and the implicit universal of plurals, defining a baseline for this task on QuanText data (Manshadi et al. [sent-399, score-0.566]
98 This work can be improved in many directions, among which are scoping more elements such as other scopal operators and implicit entities, deploying more complex learning models, and de- veloping models which require less supervision. [sent-402, score-0.826]
99 Regular tree grammars as a formalism for scope underspecification. [sent-490, score-0.198]
100 Quantifier scope disambiguation using extracted pragmatic knowledge: preliminary results. [sent-523, score-0.198]
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