emnlp emnlp2013 emnlp2013-68 knowledge-graph by maker-knowledge-mining
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Author: Filipe Mesquita ; Jordan Schmidek ; Denilson Barbosa
Abstract: A large number of Open Relation Extraction approaches have been proposed recently, covering a wide range of NLP machinery, from “shallow” (e.g., part-of-speech tagging) to “deep” (e.g., semantic role labeling–SRL). A natural question then is what is the tradeoff between NLP depth (and associated computational cost) versus effectiveness. This paper presents a fair and objective experimental comparison of 8 state-of-the-art approaches over 5 different datasets, and sheds some light on the issue. The paper also describes a novel method, EXEMPLAR, which adapts ideas from SRL to less costly NLP machinery, resulting in substantial gains both in efficiency and effectiveness, over binary and n-ary relation extraction tasks.
Reference: text
sentIndex sentText sentNum sentScore
1 The paper also describes a novel method, EXEMPLAR, which adapts ideas from SRL to less costly NLP machinery, resulting in substantial gains both in efficiency and effectiveness, over binary and n-ary relation extraction tasks. [sent-9, score-0.258]
2 1 Introduction Open Relation Extraction (ORE) (Banko and Et- zioni, 2008) has become prevalent over traditional relation extraction methods, especially on the Web, because of the intrinsic difficulty in training individual extractors for every single relation. [sent-10, score-0.183]
3 Broadly speaking, existing ORE approaches can be grouped according to the level of sophistication of the NLP techniques they rely upon: (1) shallow parsing, (2) dependency parsing and (3) semantic role labelling (SRL). [sent-11, score-0.235]
4 , 2012), identify relations by matching patterns over such tags. [sent-14, score-0.169]
5 Dependency parsing gives unambiguous relations among 447 each word in the sentence, and the ORE approaches in this category such as PATTY (Nakashole et al. [sent-15, score-0.169]
6 , 2013) identify whole subtrees connecting the relation predicate and its arguments. [sent-18, score-0.235]
7 , 2003), add roles to each node in a parse tree, en- abling ORE approaches that identify the precise connection between each argument and the predicate in a relation, independently. [sent-20, score-0.214]
8 Shallow methods handle ten times more sentences than dependency parsing methods, which in turn handle ten times more sentences than semantic parsing methods. [sent-26, score-0.218]
9 hc o2d0s1 i3n A Nsastoucria lti Loan fgoura Cgoem Ppruotcaetsiosin agl, L piang eusis 4t4ic7s–457, tify the precise connection between the argument and the predicate words in a relation) over a dependency parse tree (i. [sent-30, score-0.221]
10 One side of the argument favors shallow methods, claiming deep NLP costs orders of magnitude more and provide much less dramatic gains in terms of effectiveness (Christensen et al. [sent-40, score-0.27]
11 , 2012) extracts textual patterns from sentences based on paths in the dependency graph. [sent-54, score-0.186]
12 For all pairs of named entities, PATTY finds the shortest 448 WDEatBa-5se0t0SearcShou Srnceippets# Se5n0te0nces# R4e6la1tions PNEYNTN-5-10000NPeewnn Yo Trrkee Tbiamneks15000015510 Table 1: Binary relation datasets. [sent-55, score-0.221]
13 OLLIE merges binary relations that differ only in the preposition and second argument to produce n-ary extractions, as in: (A, “met with”, B) and (A, “met in”, C) leading to (A, “met”, [with B, in C]). [sent-62, score-0.368]
14 The shortest path between the two entities along with the shortest path between relational words and an entity are used as input to the tree kernel. [sent-65, score-0.212]
15 An expanded set of syntactic patterns based on those from ReVerb are used to generate relation candidates. [sent-66, score-0.221]
16 One of its major limitations is that it is only able to label arguments with verb predicates. [sent-73, score-0.175]
17 Lund, on the other hand, is based on dependency parsing and is trained on both PropBank and NomBank, making it able to extract relations with both verb and noun predicates. [sent-74, score-0.458]
18 We manually annotated the relations for WEB-500 and NYT500 and use the PENN-100 annotations provided by TreeKernel’s authors (Xu et al. [sent-86, score-0.167]
19 We identify exactly two entities and a trigger (a single token indicating a relation–see Section A. [sent-89, score-0.397]
20 In addition, we specify a window of tokens allowed to be in a relation, including modifiers of the trigger and prepositions connecting triggers to their arguments. [sent-91, score-0.579]
21 For each sentence annotated with two entities, a system must extract a string representing the relation between them. [sent-92, score-0.219]
22 Our evaluation method deems an extraction as correct if it contains the trigger and al449 lowed tokens only. [sent-93, score-0.328]
23 where “Google” and “YouTube” are entities of the type organization, “acquisition” is the trigger and the allowed tokens are “acquisition”, “’s” and “of”. [sent-95, score-0.478]
24 Since every method uses a different tool to recognize entities, we try to ensure every method is able to recognize the entities marked by our annotators. [sent-103, score-0.165]
25 For methods that extract relations between noun phrases (ReVerb, OLLIE, SwiRL and Lund), there is the additional task of identifying whether a noun phrase containing additional words surrounding “Europe” and “Asia” is still a reference to the annotated entity. [sent-108, score-0.331]
26 Additional steps to merge relations and remove infrequent relations are not applied. [sent-116, score-0.262]
27 In addition, we assume there is only one relation between a pair of entities in a sentence. [sent-117, score-0.278]
28 The number of entity pairs with more than one relation was insignificant in our datasets (less than 0. [sent-118, score-0.224]
29 This is because WEB500 sentences were collected by querying a search engine with known relation instances. [sent-126, score-0.209]
30 For efficiency, there is a clear separation of approximately one order of magnitude among methods based on shallow parsing (ReVerb and SONEX), dependency parsing (OLLIE, EXEMPLAR[M], EXEMPLAR[S], and PATTY) and semantic parsing (SwiRL and Lund). [sent-139, score-0.311]
31 This is possible because EXEMPLAR looks at each argument separately, as opposed to the whole subtree connecting two arguments. [sent-144, score-0.21]
32 PATTY relies on redundancy of extractions to normalize the produced relations in order to recover from mistakes done in the sentence-level. [sent-147, score-0.202]
33 Figure 2 illustrates the dominance relation differently, using precision versus recall. [sent-148, score-0.183]
34 The importance of relations triggered by nouns is illustrated by the higher recall of SONEX and Lund when compared, respectively, to ReVerb and SwiRL, similar methods that handle verb triggers only. [sent-154, score-0.451]
35 ” According to our annotation style, there is a relation “rivals” between “P&G;” and “Kao Corp. [sent-172, score-0.183]
36 On the other hand, the original annotations for PENN-100 consider only the relation between “Kap Corp. [sent-174, score-0.183]
37 These differences in annotation illustrate the challenges of producing a benchmark for open relation extraction. [sent-176, score-0.183]
38 Corpuslevel evaluations consider an extracted relation as correct regardless of whether a method was able to identify one or all sentences that describe this relation. [sent-180, score-0.238]
39 , merge near-duplicate relations and co-referential entities) all relations described in a corpus. [sent-183, score-0.262]
40 Musician–Musician), as opposed to the relation itself, which includes its two arguments. [sent-195, score-0.183]
41 The evaluations of ReVerb and OLLIE consider any noun phrase as a potential argument, while the evaluations of TreeKernel and SONEX consider named entities only. [sent-196, score-0.235]
42 Every sentence is annotated with a single relation trigger and its arguments. [sent-202, score-0.521]
43 , an), where r is the relation name and ai is an argument. [sent-207, score-0.214]
44 We only use the extracted relation whose name contains the annotated trigger, if it exists. [sent-208, score-0.25]
45 An argument of such relation is deemed a correct extraction if it is annotated in the sentence; otherwise, it is deemed incorrect. [sent-209, score-0.42]
46 Our automatic annotator identifies pairs of entities and a trigger of the relation between them. [sent-219, score-0.633]
47 Given two entities appearing within 10 tokens of each other in a sentence, our an- notator checks whether there is a relation connecting them in Freebase. [sent-221, score-0.356]
48 If such a relation exists, the annotator looks for a trigger in the sentence. [sent-222, score-0.565]
49 A trigger must be a synonym for the Freebase relation (according to WordNet) and its distance to the nearest entity cannot be more than 5 tokens. [sent-223, score-0.526]
50 Our evaluation is able to assess the effectiveness of different methods by specifying a trigger and a window of allowed tokens for each relation. [sent-239, score-0.389]
51 Rules could be learned from both dependency parsing and shallow parsing, or just shallow parsing if computing time is extremely limited. [sent-244, score-0.314]
52 A EXEMPLAR ORE methods must recognize relations from the text alone. [sent-247, score-0.166]
53 Banko and Etzioni claim that more than 90% of binary relations are expressed through a few syntactic patterns, such as “verb” and “noun+preposition” (Banko and Etzioni, 2008). [sent-249, score-0.164]
54 This section presents a study focusing on how nary relations (n > 2) are expressed in English, based on 100 distinct relations manually annotated from a random sample of 5 14 sentences in the New York Times Corpus (Sandhaus, 2008). [sent-251, score-0.324]
55 For instance, a relation “met with” indicates that the first argument is the subject and the second one is the object of “with”. [sent-255, score-0.344]
56 In order to represent n-ary relations, our patterns do not contain prepositions, possessives or any other word connecting the relation to the argu- ment. [sent-256, score-0.273]
57 For instance, the sentence “Obama met with Putin in Russia” contains the relation “meet” along three arguments: “Obama” (subject), “Putin” (object of preposition with) and “Russia” (object of preposition in). [sent-257, score-0.379]
58 A single relation can be represented in different ways using the patterns shown in Table 5. [sent-259, score-0.221]
59 For instance, the relation “donate” can be expressed as an active verb (“donates”), passive voice (“was donated by”) and normalized verb (“donation”). [sent-260, score-0.417]
60 In addition, an apposition+noun relation can be expressed as an copula+noun relation by replacing apposition for the copula verb “be”. [sent-261, score-0.637]
61 Table 6 present the relation types found through our analysis. [sent-271, score-0.183]
62 We developed EXEMPLAR to specifically recognize these relation types, including their variations. [sent-272, score-0.218]
63 An argument role defines how an argument participates in a relation. [sent-274, score-0.295]
64 In ORE, the roles for each relation are not provided and must also be recognized from the text. [sent-275, score-0.266]
65 We use the following roles: sub j ect, dire ct ob j ect and prep ob j e ct . [sent-276, score-0.31]
66 An argument has a role prep ob j ect when its connected to the relation by a preposition. [sent-277, score-0.541]
67 The roles of prepositional objects consist of their preposition and the suffix “ object”, indicating that each preposition corresponds to a different role. [sent-278, score-0.229]
68 ” is an object of the preposition “of” and has the role o f ob j e ct . [sent-283, score-0.213]
69 Multiple entities can play the same role in a relation instance. [sent-284, score-0.311]
70 Verb relations accept all three roles, while copula+noun and verb+noun relations accept sub j ect and prep ob j ect only. [sent-287, score-0.635]
71 sub ject: relat ion : o f ob j ect : in ob j ect : “NFL” “approve new stadium” “Falcons” “Atlanta” Figure 3: A relation instance extracted by EXEMPLAR for the sentence “NFL approves Falcons’ new stadium in Atlanta”. [sent-292, score-0.776]
72 nsubj paomsosd NFL approves Falcons' new stadium in Atlanta. [sent-293, score-0.232]
73 Entities are in bold, triggers are underline and arrows represent dependencies. [sent-295, score-0.174]
74 syntactic roles correspond to the same semantic role across different relations (Chambers and Jurafsky, 2011). [sent-296, score-0.247]
75 1 The method EXEMPLAR takes a stream of textual documents and extracts instances of n-ary relations as illustrated in Figure 3. [sent-299, score-0.163]
76 In this example, “stadium” depends on “approves” and the arrow connecting them can be read as “the direct object of approves is stadium”. [sent-306, score-0.207]
77 3 Detecting triggers After recognizing entities, EXEMPLAR detects relation triggers. [sent-313, score-0.351]
78 A trigger is a single token that indicates the presence of a relation. [sent-314, score-0.302]
79 EXEMPLAR also uses triggers to determine the relation name, as discussed later. [sent-317, score-0.322]
80 A trigger can be any noun or verb that was not tagged as being part of an entity mention. [sent-318, score-0.542]
81 A verb relation is triggered by a verb that does not include a noun as its direct object. [sent-321, score-0.536]
82 A noun must be a nominalized verb to be a trigger for verb relations. [sent-323, score-0.668]
83 The name of a relation triggered by a nominalized verb is the trigger’s original verb (before nominalization). [sent-326, score-0.535]
84 The copula used in the relation name can be a verb with cop- ula dependency to the trigger, or the verb “be” for 3http : //wordnetcode . [sent-330, score-0.653]
85 The relation name is the concatenation of the copula’s lemma and the trigger’s lemma along its modifiers. [sent-334, score-0.276]
86 For instance, the relation in the sentence “Jaden and Willow, the famous children of Will Smith” is “be famous child”. [sent-335, score-0.183]
87 EXEMPLAR recognizes two triggers for each verb+noun relation: a verb and a noun acting as its direct object. [sent-337, score-0.338]
88 The relation name is defined by concatenating the verb’s lemma with the the noun and its modifiers. [sent-338, score-0.327]
89 In our running example, “approves” and “stadium” trigger the relation “approve new stadium”. [sent-339, score-0.485]
90 4 Detecting candidate arguments After relation triggers are identified, EXEMPLAR proceeds to detect their candidate arguments. [sent-341, score-0.38]
91 For this, we look at the dependency between each entity and a trigger separately. [sent-342, score-0.433]
92 EXEMPLAR relies on two observations: (1) an argument is often adjacent to a trigger in the dependency graph, and (2) the type of the dependency can accurately predict whether an entity is an argument for the relation or not. [sent-343, score-0.994]
93 EXEMPLAR identifies as a candidate argument every entity that is connected to trigger, as long as their dependency type is listed in Table 7. [sent-345, score-0.288]
94 The entities “NFL” and “Atlanta” depends on the trigger “approves” and “Falcons” depend on the trigger “stadium”. [sent-347, score-0.725]
95 Since their dependency types are listed in Table 7, these entities are marked as candidate arguments. [sent-348, score-0.185]
96 5 Role Detection EXEMPLAR determines the role of an argument based on the trigger type (noun or verb), the type of dependency between the trigger and argument and the direction of the dependency. [sent-350, score-1.041]
97 To take into account the dependency direction, we prefix each dependency type with “>” when an entity depends on the trigger and “<” when the trigger depends on the entity. [sent-351, score-0.903]
98 Table 8 shows EXEMPLAR’s rules that assign roles to arguments for each relation type. [sent-352, score-0.352]
99 If trigger type = trigger and dependency type = dependency then assign role. [sent-355, score-0.836]
100 For example, the first rule in Table 8a specifies that an argument must be assigned the role of a subject if this argument depends on a verb trigger and the dependency type is >nsubj. [sent-356, score-0.856]
wordName wordTfidf (topN-words)
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