acl acl2013 acl2013-306 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Tiziano Flati ; Roberto Navigli
Abstract: We present SPred, a novel method for the creation of large repositories of semantic predicates. We start from existing collocations to form lexical predicates (e.g., break ∗) and learn the semantic classes that best f∗it) tahned ∗ argument. Taon idco this, we extract failtl thhee ∗ occurrences ion Wikipedia ewxthraiccht match the predicate and abstract its arguments to general semantic classes (e.g., break BODY PART, break AGREEMENT, etc.). Our experiments show that we are able to create a large collection of semantic predicates from the Oxford Advanced Learner’s Dictionary with high precision and recall, and perform well against the most similar approach.
Reference: text
sentIndex sentText sentNum sentScore
1 We start from existing collocations to form lexical predicates (e. [sent-4, score-0.32]
2 , break ∗) and learn the semantic classes that best f∗it) tahned ∗ argument. [sent-6, score-0.489]
3 Taon idco this, we extract failtl thhee ∗ occurrences ion Wikipedia ewxthraiccht match the predicate and abstract its arguments to general semantic classes (e. [sent-7, score-0.986]
4 Our experiments show that we are able to create a large collection of semantic predicates from the Oxford Advanced Learner’s Dictionary with high precision and recall, and perform well against the most similar approach. [sent-11, score-0.385]
5 For instance, which semantic classes are expected as a direct object of the verb break? [sent-34, score-0.366]
6 These approaches leverage lexico-syntactic patterns and input seeds to recursively learn the semantic classes of relation arguments. [sent-38, score-0.399]
7 However, they require the manual selection of one or more seeds for each pattern of interest, and this selection influences the amount and kind of semantic classes to be learned. [sent-39, score-0.366]
8 The goal of our research is to create a largescale repository of semantic predicates whose lexical arguments are replaced by their semantic classes. [sent-41, score-0.882]
9 Ac s2s0o1ci3a Atiosnso fcoirat Cio nm foprut Caotimonpaulta Lti nognuails Lti cnsg,u piasgteics 12 2–1232, ing semantic predicate break a BODY PART, where BODY PART is a class comprising several lexical realizations, such as leg, arm, foot, etc. [sent-44, score-0.841]
10 , n), c ∈ C is a semantic class selected from a fixed scet ∈ ∈C C Cof i scla ass seems, aanntdic ci c∈l {s0s, . [sent-87, score-0.308]
11 ntic predicate cup of BEVERAGE,1 where BEVERAGE is a semantic class representing beverages. [sent-95, score-0.881]
12 This predicate matches phrases like cup of coffee, cup of tea, etc. [sent-96, score-0.822]
13 Semantic predicates mix the lexical information of a given lexical predicate with the explicit semantic modeling of its argument. [sent-99, score-0.881]
14 Importantly, the same lexical predicate can have different classes as its argument, like cup of FOOD vs. [sent-100, score-0.874]
15 Note, however, that different classes might convey different semantics for the same lexical 1In what follows we denote the SEMANTIC CLASS in small capitals and the lexical predicate in italics. [sent-102, score-0.711]
16 predicate, such as cup of COUNTRY, referring to cup as a prize instead of cup as a container. [sent-103, score-0.747]
17 3 Large-Scale Harvesting of Semantic Predicates The goal of this paper is to provide a fully automatic approach for the creation of a large repository of semantic predicates in three phases. [sent-104, score-0.385]
18 We extract all its possible filling arguments from Wikipedia, e. [sent-108, score-0.406]
19 We disambiguate as many filling arguments as possible using Wikipedia, obtaining a set of corresponding Wikipedia pages, e. [sent-114, score-0.443]
20 We create the semantic predicates by generalizing the Wikipedia pages to their most suitable semantic classes, e. [sent-120, score-0.628]
21 We can then exploit the learned semantic predi- cates to assign the most suitable semantic class to new filling arguments for the given lexical predicate (Section 3. [sent-125, score-1.326]
22 1 Extraction of Filling Arguments Let π be an input lexical predicate (e. [sent-128, score-0.41]
23 s Weaerc shhionwg Wikipedia for the arguments of the lexical predicate a * of milk in Table 1. [sent-135, score-0.771]
24 The output of this first step is a set Lπ of triples (a, s, l) of filling arguments a matching the lexical predicate π in a sentence s of the Wikipedia corpus, with a potentially linked to a page l (e. [sent-137, score-1.061]
25 2 Disambiguation of Filling Arguments The objective of the second step is to disambiguate as many arguments in Lπ as possible for the lex2We will also refer to l as the sense of a in sentence s. [sent-143, score-0.367]
26 1223 Table1:Aa nevfcixuorcelydnbh[rosp[atibg nto[uldbaetcoslnutf]pletho f tom mki lek nsequ cs which match the lexical predicate a * of milk in Wikipedia (filling argument shown in the second column; following the Wikipedia convention we provide links in double square brackets). [sent-144, score-0.617]
27 }lin ⊆ked L to the corresponding Wikipedia page = (like the top three linked arguments in Table 1). [sent-148, score-0.457]
28 )eur ∈ist Uics: • One sense per page: if another occurrence oOfn ae sine tshee psaerme p a Wgeik:i ipfed aniao page c(cinudrerpenencedent of the lexical predicate) is linked to a page l, then remove (a, s, ? [sent-160, score-0.471]
29 For instance, cup of coffee appears in the Wikipedia page Energy drink in the sentence “[. [sent-163, score-0.495]
30 ] energy drinks contain more caffeine than a strong cup of coffee”, but this occurrence of coffee is not linked. [sent-166, score-0.414]
31 ] combined with a cup of coffee and a half-boiled egg” is not linked, but we have collected many other occurrences, all linked to the Coffee page, so this link gets propagated to our ambiguous item as well. [sent-175, score-0.531]
32 Consider the instance “At that point, Smith threw down a cup of Gatorade” in page Jimmy Clausen; there is only one sense for Gatorade in Wikipedia, so we link the unannotated occurrence to it. [sent-180, score-0.478]
33 As a result, the initial set of disambiguated arguments in Dπ is augmented with all those triples for which any of the above three heuristics apply. [sent-181, score-0.346]
34 3 Generalization to Semantic Classes Our final objective is to generalize the annotated arguments to semantic classes picked out from a fixed set C of classes. [sent-194, score-0.626]
35 We perform this in two substeps: we first link all our disambiguated arguments to WordNet (Section 3. [sent-196, score-0.339]
36 1) and then leverage the WordNet taxonomy to populate the semantic classes in C (Section 3. [sent-198, score-0.452]
37 While it is true that attached to each Wikipedia page there are one or more categories, these categories just provide shallow information about the class the page 1224 belongs to. [sent-205, score-0.431]
38 Indeed, categories are not ideal for representing the semantic classes of a Wikipedia page for at least three reasons: i) many categories do not express taxonomic information (e. [sent-206, score-0.56]
39 , 2009; Erk and McCarthy, 2009; Huang and Riloff, 2010), we pick out our semantic classes C from WordNet and leverage its manually-curated taxonomy to associate our arguments with the most suitable class. [sent-211, score-0.763]
40 This way we avoid building a new taxonomy and shift the problem to that of projecting the Wikipedia pages associated with annotated filling arguments to – – µ synsets in WordNet. [sent-212, score-0.573]
41 For instance, the mapping provided by BabelNet does not provide any link for the page Peter Spence; thanks to WCL, though, we are able to set the page Journalist as its hypernym, and link it to the WordNet synset journalistn1. [sent-229, score-0.37]
42 2 Populating the Semantic Classes We now proceed to populating the semantic classes in C with the annotated arguments obtained for the lexical predicate π. [sent-235, score-1.036]
43 The semantic class for a WordNet synset S is the class c among those in C which is the most specific hypernym of S according to the WordNet taxonomy. [sent-237, score-0.597]
44 For instance, given the synset tap watern1, its semantic class is watern1 (while the other more general subsumers in C are not considered, e. [sent-238, score-0.43]
45 For each argument a ∈ A for which a Wikipedia-to-WordNet maa ∈ppi Ang µ(sense(a)) could be established as a result of the linking procedure described above, we associate a with the semantic class of µ(sense(a)). [sent-241, score-0.414]
46 For example, consider the case in which a is equal to tap water and sense(a) is equal to the Wikipedia page Tap water, in turn mapped to tap watern1 via µ; we thus associate tap water with its semantic class watern1. [sent-242, score-0.706]
47 7 Ultimately, for each class c ∈ C, we obtain a sUetlt smuaptpeolyr,t(c fo) m eaadche up osf c a l∈l t Che, arguments a ∈ A associated with c. [sent-244, score-0.417]
48 1th),a ntkhes support of a class can also contain arguments not covered in WordNet (e. [sent-259, score-0.417]
49 Since not all classes are equally relevant to the lexical predicate π, we estimate the conditional probability of each class c ∈ C given π on the pbarosibsa obifl tithye nofum eabcehr o clfa assesnt cen ∈ces C Cwh giivchen nco πnt oanin t hane argument in that class. [sent-267, score-0.922]
50 -p Arosba abnil eitxy- classesP for theP Plexical predicate cup of ∗. [sent-269, score-0.573]
51 ciation of each semantic class c with a target lexical predicate w1 w2 . [sent-271, score-0.718]
52 4 Classification of new arguments Once the semantic predicates for the input lexical predicate π have been learned, we can classify a new filling argument a of π. [sent-285, score-1.307]
53 Next, we create a distributional vector for each class c ∈ C as follows: c = PS∈desc(c) S~, where desc(c) iPs the set of all synsets which are descendants of the semantic class c in WordNet. [sent-290, score-0.575]
54 As a result we obtain a predicate-independent distributional description for each semantic class in C. [sent-291, score-0.345]
55 Now, given an argument a of a lexical predicate π, we create a distributional vector by summing the noun occurrences of all the sentences s such that (a, s, l) ∈ Lπ (cf. [sent-293, score-0.589]
56 Let Ca be the set of candidate semantic classes for argument a, i. [sent-297, score-0.472]
57 , Ca contains the semantic classes for the WordNet synsets of a as well as the semantic classes associated with µ(p) for all Wikipedia pages p whose lemma is a. [sent-299, score-0.846]
58 Then, we determine the most suitable semantic class c ∈ Ca of argument a as the class twicith c ltahses h cig ∈heCs t distributional probability, estimated as: Pdistr(c|π,a) =Pc0∈sCimas(i c~m, a~() c~0, a~). [sent-302, score-0.659]
59 Given a textual expression such as virus replicate, we: (i) extract all the filling arguments of the lexical predicate * replicate; (ii) link and disambiguate the extracted filling arguments; (iii) query our system for the available virus semantic classes (i. [sent-305, score-1.602]
60 e,c {tovrirsu sfor 1226 the candidate semantic classes and the given input argument; (v) calculate the probability mixture. [sent-308, score-0.366]
61 For both evaluations, we use a lexical predicate dataset built from the Oxford Advanced Learner’s Dictionary (Crowther, 1998). [sent-314, score-0.41]
62 1 Set of Semantic Classes The selection of which semantic classes to include in the set C is of great importance. [sent-316, score-0.366]
63 In fact, having too many classes will end up in an overly finegrained inventory of meanings, whereas an excessively small number of classes will provide little discriminatory power. [sent-317, score-0.475]
64 As our set C of semantic classes we selected the standard set of 3,299 core nominal synsets available in WordNet. [sent-318, score-0.439]
65 2 Datasets The Oxford Advanced Learner’s Dictionary provides usage notes that contain typical predicates in various semantic domains in English, e. [sent-321, score-0.385]
66 The splitting procedure generated 6,220 instantiated lexical predicate items overall. [sent-332, score-0.476]
67 For instance, the three items bacteria/microbes/viruses spread were generalized into the lexical predicate * spread. [sent-344, score-0.527]
68 The total number of different lexical predicates obtained was 1,446, totaling 1,429 distinct verbs (note that the dataset might contain the lexical predicate * spread as well as spread *). [sent-345, score-0.866]
69 For each lexical predicate we calculated the conditional probability of each semantic class using Formula 1, resulting in a ranking of semantic classes. [sent-347, score-0.903]
70 , locationn1 is a valid semantic class for travel to * while emotionn1 is not. [sent-350, score-0.308]
71 We note that high performance, attaining above 80%, can be achieved 10The low number of items per predicate is due to the original Oxford resource. [sent-358, score-0.39]
72 any semantic class 1227 by focusing up to the first 7 classes output by our system, with a 94% precision@ 1. [sent-360, score-0.523]
73 Starting from the lexical predicate items obtained as described in Section 4. [sent-363, score-0.476]
74 , virus in viruses spread) with the most suitable semantic class (e. [sent-367, score-0.435]
75 In this second evaluation we measure the accuracy of our method at assigning the most suitable semantic class to the argument of a lexical predicate item in our gold standard. [sent-373, score-0.909]
76 Precision is the number of items which are assigned the correct class (as evaluated by a human) over the number of items which are assigned a class by the system. [sent-377, score-0.446]
77 For tuning α we used a held-out set of 8 verbs, randomly sampled from the lexical predicates not used in the dataset. [sent-384, score-0.32]
78 We created a tuning set using the annotated arguments in Wikipedia for these verbs: we trained the model on 80% of the annotated lexical predicate arguments (i. [sent-385, score-0.93]
79 We also compared against a random baseline that randomly selects one out of all the candidate semantic classes for each item, achieving only moderate results. [sent-404, score-0.366]
80 Starting from the entire set of 1,446 lexical predicates from the Oxford dictionary (see Section 4. [sent-417, score-0.32]
81 We note that, while the amount of originally linked arguments is very low (about 2. [sent-425, score-0.339]
82 , 68 out of almost 74 million) remain unlinked, the ratio of distinct arguments which we linked to WordNet is considerably higher, calculated as 3,723,979 linked arguments over 12,43 1,564 distinct arguments, i. [sent-432, score-0.712]
83 The most similar approach is that of Kozareva and Hovy (2010, K&H;) who assign supertypes to the arguments of arbitrary relations, a task which resembles our semantic predicate ranking. [sent-436, score-0.819]
84 We calculated precision@k of the semantic classes obtained for each relation in the dataset of K&H. [sent-456, score-0.4]
85 Although we cannot report recall, we list the number of Wikipedia arguments and associated classes in Table 7, which provides an estimate of the extraction capability of SPred. [sent-465, score-0.513]
86 The large number of classes found for the arguments demonstrates the ability of our method to generalize to a variety of semantic classes. [sent-466, score-0.626]
87 PredicateNumber of argsNumber of classes classes for the 12 most frequent lexical predicates of Kozareva and Hovy (2010) extracted by SPred from Wikipedia. [sent-467, score-0.75]
88 However, these resources often operate purely at the lexical level, providing no information on the semantics of their arguments or relations. [sent-473, score-0.346]
89 Several studies have examined adding semantics through grouping relations into sets (Yates and Etzioni, 2009), ontologizing the arguments (Chklovski and Pantel, 2004), or ontologizing the relations themselves (Moro and Navigli, 2013). [sent-474, score-0.5]
90 Our method for identifying the different semantic classes ofpredicate arguments is closely related to the task of identifying selectional preferences. [sent-483, score-0.709]
91 The most similar approaches to it are taxonomybased ones, which leverage the semantic types of the relations arguments (Resnik, 1996; Li and Abe, 1998; Clark and Weir, 2002; Pennacchiotti and Pantel, 2006). [sent-484, score-0.488]
92 As a result, alternative approaches have been proposed that eschew taxonomies in favor of rating the quality of potential relation arguments (Erk, 2007; Chambers and Jurafsky, 2010) or generating probability distributions over the arguments (Rooth et al. [sent-486, score-0.52]
93 Another closely related work is that of Hanks (2013) concerning the Theory of Norms and Exploitations, where norms (exploitations) represent expected (unexpected) classes for a given lexical predicate. [sent-497, score-0.34]
94 The closest technical approach to ours is that of Kozareva and Hovy (2010), who use recursive patterns to induce semantic classes for the arguments of relational patterns. [sent-499, score-0.626]
95 Whereas their approach requires both a relation pattern and one or more seeds, which bias the types of semantic classes that are learned, our proposed method requires only the pattern itself, and as a result is capable of learning an unbounded number of different semantic classes. [sent-500, score-0.517]
96 In order to semantify lexical predicates we exploit the wide coverage of Wikipedia to extract and disambiguate lexical predicate occurrences, and leverage WordNet to populate the semantic classes with suitable predicate arguments. [sent-502, score-1.579]
97 As a result, we are able to ontologize lexical predicate instances like those available in existing dictionaries (e. [sent-503, score-0.41]
98 , break a toe) into semantic predicates (such as break a BODY PART). [sent-505, score-0.631]
99 , Wikipedias in other languages), provided lexical predicates can be extracted with associated semantic classes. [sent-514, score-0.471]
100 In order to support future efforts we are releasing our semantic predicates as a freely available – resource. [sent-515, score-0.385]
wordName wordTfidf (topN-words)
[('predicate', 0.324), ('arguments', 0.26), ('cup', 0.249), ('predicates', 0.234), ('classes', 0.215), ('wikipedia', 0.196), ('class', 0.157), ('semantic', 0.151), ('filling', 0.146), ('navigli', 0.143), ('coffee', 0.128), ('break', 0.123), ('wordnet', 0.122), ('page', 0.118), ('spred', 0.118), ('kozareva', 0.116), ('babelnet', 0.111), ('oxford', 0.108), ('argument', 0.106), ('milk', 0.101), ('moro', 0.095), ('lexical', 0.086), ('supertypes', 0.084), ('selectional', 0.083), ('hypernym', 0.08), ('roberto', 0.08), ('linked', 0.079), ('ontologizing', 0.076), ('virus', 0.076), ('synsets', 0.073), ('faralli', 0.073), ('sense', 0.07), ('formula', 0.07), ('tap', 0.07), ('spend', 0.067), ('items', 0.066), ('yates', 0.06), ('hovy', 0.059), ('ontologized', 0.057), ('stefano', 0.056), ('velardi', 0.056), ('ponzetto', 0.054), ('etzioni', 0.053), ('taxonomy', 0.053), ('synset', 0.052), ('spread', 0.051), ('suitable', 0.051), ('exploitations', 0.051), ('wi', 0.049), ('triples', 0.048), ('stroudsburg', 0.047), ('uppsala', 0.047), ('disambiguation', 0.047), ('inventory', 0.045), ('textual', 0.044), ('relations', 0.044), ('chklovski', 0.044), ('pa', 0.042), ('link', 0.041), ('pages', 0.041), ('roma', 0.04), ('oren', 0.039), ('norms', 0.039), ('riloff', 0.039), ('stern', 0.039), ('categories', 0.038), ('crowther', 0.038), ('gatorade', 0.038), ('igo', 0.038), ('kcorrecttotal', 0.038), ('kprec', 0.038), ('menta', 0.038), ('pclass', 0.038), ('pdistr', 0.038), ('semantify', 0.038), ('thelen', 0.038), ('wisenet', 0.038), ('disambiguated', 0.038), ('extraction', 0.038), ('disambiguate', 0.037), ('energy', 0.037), ('distributional', 0.037), ('fader', 0.036), ('occurrences', 0.036), ('water', 0.035), ('wn', 0.035), ('calculated', 0.034), ('carlson', 0.034), ('weld', 0.034), ('item', 0.034), ('tithye', 0.034), ('supertype', 0.034), ('totaling', 0.034), ('izquierdo', 0.034), ('bottle', 0.034), ('cocoa', 0.034), ('desc', 0.034), ('yakushiji', 0.034), ('leverage', 0.033), ('harvesting', 0.033)]
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