emnlp emnlp2012 emnlp2012-62 knowledge-graph by maker-knowledge-mining
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Author: Yohei Takaku ; Nobuhiro Kaji ; Naoki Yoshinaga ; Masashi Toyoda
Abstract: Because the real world evolves over time, numerous relations between entities written in presently available texts are already obsolete or will potentially evolve in the future. This study aims at resolving the intricacy in consistently compiling relations extracted from text, and presents a method for identifying constancy and uniqueness of the relations in the context of supervised learning. We exploit massive time-series web texts to induce features on the basis of time-series frequency and linguistic cues. Experimental results confirmed that the time-series frequency distributions contributed much to the recall of constancy identification and the precision of the uniqueness identification.
Reference: text
sentIndex sentText sentNum sentScore
1 t oyoda @ t kl i s i Abstract Because the real world evolves over time, numerous relations between entities written in presently available texts are already obsolete or will potentially evolve in the future. [sent-9, score-0.392]
2 This study aims at resolving the intricacy in consistently compiling relations extracted from text, and presents a method for identifying constancy and uniqueness of the relations in the context of supervised learning. [sent-10, score-1.513]
3 We exploit massive time-series web texts to induce features on the basis of time-series frequency and linguistic cues. [sent-11, score-0.233]
4 Experimental results confirmed that the time-series frequency distributions contributed much to the recall of constancy identification and the precision of the uniqueness identification. [sent-12, score-1.221]
5 1 Introduction We have witnessed a number of success stories in acquiring semantic relations between entities from ever-increasing text on the web (Pantel and Pennacchiotti, 2006; Banko et al. [sent-13, score-0.267]
6 jp There exists, however, a great challenge to compile consistently relations extracted from text by these methods, because they assume a simplifying assumption that relations are time-invariant. [sent-25, score-0.437]
7 In other words, they implicitly disregard the fact that state- ments in texts actually reflect the state of the world at the time when they were written, which follows that relations extracted from such texts eventually become outdated as the real world evolves over time. [sent-26, score-0.33]
8 Let us consider that relations are extracted from the following sentences:1 (1) a. [sent-27, score-0.204]
9 The relations in statements 1a and 1b are true across time, so we can simply accumulate all the relation instances. [sent-38, score-0.426]
10 The relations in 1c and 1d in contrast evolve over time. [sent-39, score-0.25]
11 The relation written in 1c becomes outdated when the other person takes the position, so we need to supersede it when a new relation is extracted from text (e. [sent-40, score-0.493]
12 For the relation in 1d, we do not always need to supersede it with a new relation. [sent-44, score-0.228]
13 This study is motivated from the above consider- 1Since our task settings are language-independent, we hereafter employ English examples as much as possible to widen the potential readership of the paper, although we conducted experiments with relations between entities in Japanese. [sent-45, score-0.267]
14 lc L2a0n1g2ua Agseso Pcrioactieosnsi fnogr a Cnodm Cpoumtaptiuotna tilo Lnianlg Nuaist uircasl ations and proposes a method for identifying constancy and uniqueness of relations in order to select an appropriate strategy to maintain relation instances extracted from text. [sent-48, score-1.462]
15 For example, the relations written in statements 1a and 1b are constant, while those in 1c and 1d are non-constant; the relation in 1c is unique,2 whereas the relation in 1d is non-unique. [sent-49, score-0.613]
16 With these properties of relations in mind, we can accumulate constant relations while appropriately superseding non-constant, unique relations with newly acquired relations. [sent-50, score-0.85]
17 The key challenge in solving these classification tasks is how to induce an effective feature that identifies unique, non-constant relations (statement 1c) that seemingly appear as non-unique relations on text (statement 1b). [sent-52, score-0.529]
18 We exploit massive time-series web text to observe actual evolutions of relation instances and induce features from the relation instances taken from a time sliding window and linguistic cues modifying the predicate and arguments of the target relation. [sent-53, score-0.651]
19 We evaluated our method on 1000 relations extracted from 6-year’s worth of Japanese blog posts with 2. [sent-54, score-0.409]
20 We have thereby confirmed that the features induced from this time-series text contributed much to improve the classification accuracy. [sent-56, score-0.188]
21 The main contributions of this paper are twofold: • We have introduced a novel task for identifying constancy druelcaetido ans. [sent-57, score-0.606]
22 n oSvienlce ta smko sfot ro ifd tehnet existing studies assume that relations are timeinvariant as discussed by Weikum et al. [sent-58, score-0.204]
23 (201 1), non-constant relations prevalent in their outcome incur a serious problem in maintaining the acquired relations. [sent-59, score-0.204]
24 The notion of constancy is meant to resolve this stalemate. [sent-60, score-0.546]
25 • We have for the first time demonstrated the uWseefu hlanveess f oofr a etim fiers-ster tiimese ete dxetm mino n restlraatiteodn acquisition and confirmed its impact in the two relation classification tasks. [sent-61, score-0.338]
26 The features induced from the time-series text have greatly contributed to the accuracy of the classification based on uniqueness as well as the recall of the classification based on constancy. [sent-62, score-0.682]
27 2This kind of relation is referred to as functional relation in the literature (Ritter et al. [sent-63, score-0.402]
28 884 Constant Non-constant arg1 was born in arg2arg1’s president is arg2 arg1 is a father of arg2 arg1 belongs to arg2 arg1 is written by arg2 arg1 lives in arg2 Table 1: Examples of constant, non-constant relations. [sent-66, score-0.348]
29 Section 2 introduces the two properties of relations (constancy and uniqueness) and then defines the task setting of this study. [sent-68, score-0.204]
30 Sections 3 and 4 describe the features induced from time-series text for constancy and uniqueness classification, respectively. [sent-69, score-1.045]
31 1 Constancy and uniqueness We introduce two properties of relations: constancy and uniqueness. [sent-74, score-1.012]
32 A relation is constant if, for most values of arg1, the value of arg2 is independent of time (Table 1). [sent-75, score-0.321]
33 A relation is unique if, for most values of arg1, there exists, at any given point in time, only one value of arg2 that satisfies the relation (Table 2). [sent-79, score-0.481]
34 In contrast, the relation harg1 is funded by arg2i eis. [sent-85, score-0.186]
35 2 Discussion Both constancy and uniqueness are properties that usually, not always, hold for most, not all, of the arg1’s values. [sent-88, score-1.012]
36 To see this, let us examine the relation harg1 ’s president is arg2i . [sent-89, score-0.353]
37 Although this relation is Unique Non-unique arg1 was born in arg2arg1 is funded by arg2 arg1 is headquartered in arg2 arg1 consists of arg2 arg1’s president is arg2 arg1 borders on arg2 Table 2: Examples of unique and non-unique relations. [sent-90, score-0.672]
38 For example, a country might exist in which the president has never changed; a country might have more than one president at the same time during civil war. [sent-92, score-0.376]
39 However, since such situations are rare, the relation harg1 ’s president his s arg2i niss c aorens riadreer,ed th as neleaitthioenr hcoarngst1an ’st nor non-unique. [sent-93, score-0.353]
40 The above discussion implies that the constancy and uniqueness of relations can not be determined completely objectively. [sent-94, score-1.216]
41 We, nevertheless, claim that these properties of relations are intuitively accept- able and thus they can be identified with moderate agreement by different people (see section 5). [sent-95, score-0.204]
42 3 Task and our approach This paper explores classifying given relations on the basis of constancy and uniqueness. [sent-97, score-0.857]
43 Section 3 presents features based on time-series frequency and linguistic cues for classifying constant and nonconstant relations. [sent-100, score-0.366]
44 Similarly, section 4 presents analogous features for classifying unique and nonunique relations. [sent-101, score-0.218]
45 1 Time-series frequency It is intuitive to identify constant relations by comparing frequency distributions over arg2 in different time periods. [sent-103, score-0.557]
46 Time-series text For a time-series text, we used Japanese blog posts that had been gathered from Feb. [sent-105, score-0.205]
47 These posts were aggregated on a monthly basis by using time stamps attached with them, i. [sent-110, score-0.311]
48 , the unit of time is one month 885 Figure 1: Time-series frequency distribution of harg1 belFoigngusr eto 1 :a Trgim2ie w-sehreien arg1 tuaeknecsy K deiissturikbue tHioonn odaf. [sent-112, score-0.194]
49 Averaging such similarities over representative values of arg1, we have N1e∈∑EN(r)cos(Fw1(r,e),Fw2(r,e) , where r is a relation (e. [sent-127, score-0.186]
50 , harg1 ’s president is arg2i), e i ss a n raemlateido entity (e. [sent-129, score-0.224]
51 Nominal modifiers We observe that non-constant relations could be indicated by some nominal modifiers: (2) a. [sent-154, score-0.304]
52 The use of the prefix ex- and the adjective first implies that the president changes, and hence the relation harg1 ’s president is arg2i is not constant. [sent-158, score-0.52]
53 • • Next, we extract nouns from a relation to bNee xctl,as wsiefie dex (e. [sent-164, score-0.186]
54 A given relation is transformed into different forms by attaching the suffixes to a verb in the relation, and their frequencies are counted. [sent-181, score-0.256]
55 1 Time-series frequency Number of entity types A straightforward approach to identifying unique relations is, for a given arg1, to count the number of entity types appearing in arg2 (Lin et al. [sent-189, score-0.609]
56 Even if the estimate is contaminated by noise, a small number of entity types can still be considered to indicate the uniqueness of the relation. [sent-192, score-0.523]
57 Presume counting the number of entity types in arg2 of the relation harg1 is headquartered in arg2i, owfhi tchhe i rse nlaotnio-nco nhsatragn1t a isnd h unique. [sent-194, score-0.307]
58 eIfr we use large size of time window to obtain counts, we will observe multiple types of entities in arg2, not because the relation is non-unique, but because it is nonconstant. [sent-195, score-0.347]
59 Ratio of entity frequency Since it is not reliable enough to use only the number of entity types, we also exploit the frequency of the entity. [sent-202, score-0.304]
60 If the frequency of e1st is much larger than that of e2nd, the relation is likely to be constant. [sent-204, score-0.281]
61 The coordination structure in the first example implies an entity can border on more than one entity, and hence the relation harg1 borders on arg2i is not unique. [sent-212, score-0.423]
62 To capture this intuition, we introduce two types of linguistic features for classifying unique and nonunique relations. [sent-216, score-0.218]
63 The feature is fired if the number of times that coordination structures are found in arg2 exceeds threshold θ3. [sent-218, score-0.19]
64 The feature is fired if the number of times that an entity in arg2 is followed by one of the four keywords exceeds threshold θ3. [sent-225, score-0.207]
65 1 Data We built a dataset for evaluation by extracting relations from the time-series text (section 3. [sent-229, score-0.204]
66 Then, annotators were asked to label 1000 relations as not only constant or non-constant but also unique or non-unique. [sent-244, score-0.438]
67 We have briefly investigated the relations whose labels assigned by the annotators conflicted. [sent-250, score-0.236]
68 Constancy classification Figure 2 illustrates the recall-precision curve in constancy classification. [sent-265, score-0.666]
69 Because we are unaware of any previous methods for classifying constant and non-constant relations, a simple method based on the cosine similarity was Recall Figure 3: Recall-precision curve (uniqueness classification). [sent-266, score-0.211]
70 used as a baseline: N1e∈∑EN(r)cos(Fw1(r,e),Fw2(r,e) , where the time windows w1 and w2 are determined as the first and last month in which the relation r is observed. [sent-267, score-0.395]
71 A given relation is classified as nonconstant if the above similarity exceeds a threshold. [sent-268, score-0.306]
72 Uniqueness classification Figure 3 illustrates the recall-precision curve in uniqueness classification. [sent-274, score-0.586]
73 889 Recall Figure 4: Comparison with the methods varying a value of N for constancy classification. [sent-280, score-0.546]
74 Recall Figure 5: Comparison with the methods varying a value of N for uniqueness classification. [sent-281, score-0.466]
75 We hence conclude time-series information is useful for classifying not only constant but also unique relations. [sent-282, score-0.269]
76 ll S feotr- constancy classification and the better precision for uniqueness classification (Figures 4 and 5). [sent-286, score-1.15]
77 Recall Figure 6: Comparison with the methods using only a single value of T for constancy classification. [sent-288, score-0.546]
78 0 TT == 11,3,6,12 Recall Figure 7: Comparison with the methods using only a single value of T for uniqueness classification. [sent-290, score-0.466]
79 4 Investigation into the window size, T Our method uses multiple time windows of different sizes (i. [sent-292, score-0.208]
80 The results in the uniqueness classification task demonstrated that our method achieves better overall results than the methods using a single value of T. [sent-298, score-0.535]
81 On the other hand, we could not confirm the effect of using multiple time windows of different sizes in the constancy classification task. [sent-300, score-0.767]
82 5 Error analysis We randomly selected and analyzed 200 misclassified relations for both tasks. [sent-302, score-0.204]
83 Paraphrases We observed that constant relations are prone to be miss-classified as non-constant when more than one paraphrase appear in arg2 and thus the value of arg2 is pretended to change. [sent-304, score-0.297]
84 A similar problem was observed for unique relations as well. [sent-306, score-0.313]
85 Topical bias Topics mentioned in the blog posts are sometimes biased, and such bias can have a neg- ative effect on classification, especially when a relation takes a small number of entity types in arg2 for given arg1. [sent-307, score-0.448]
86 i Short-/Long-term evolution Since we have aggregated on a monthly basis the 6-year’s worth of blog posts, the induced features cannot capture evolutions that occur in shorter or longer intervals. [sent-310, score-0.271]
87 Reference to past, future, or speculative facts The blog authors sometimes refer to relations that do not occur around when they write their posts; such relations actually occurred in the past, will occur in the future, or even speculative. [sent-314, score-0.498]
88 Since our method exploits the time stamps attached to the posts to associate the relations with time, those relations introduce noises in the frequency distributions. [sent-315, score-0.731]
89 6 Related Work In recent years, much attention has been given to extracting relations from a massive amount of textual data, especially the web (cf. [sent-317, score-0.25]
90 Most of those studies, however, explored just extracting relations from text. [sent-319, score-0.204]
91 There has been no previous work on identifying the constancy of relations. [sent-321, score-0.606]
92 Such temporal information alone is not sufficient for identifying the constancy of relations, while we think it would be helpful. [sent-330, score-0.657]
93 On the other hand, the uniqueness of relations has so far been discussed in some studies. [sent-331, score-0.67]
94 (2008) have pointed out the importance of identifying unique relations for various NLP tasks such as contradiction detection, quantifier scope disambiguation, and synonym resolution. [sent-333, score-0.402]
95 They proposed an EM-style algorithm for scoring the uniqueness of relations. [sent-334, score-0.466]
96 While those studies discussed the same problem as this paper, they did not point out the importance of the constancy in identifying unique relations (cf. [sent-337, score-0.919]
97 7 Conclusion This paper discussed that the notion of constancy is essential in compiling relations between entities extracted from real-world text and proposed a method for classifying relations on the basis of constancy and uniqueness. [sent-340, score-1.703]
98 The time-series web text was fully exploited to induce frequency-based features from time-series frequency distribution on relation instances as well as language-based features tailored for individual classification tasks. [sent-341, score-0.402]
99 We will utilize the identified properties of the relations to adopt an appropriate strategy to compile 891 their instances. [sent-343, score-0.233]
100 We consider that the notion of constancy will even be beneficial in acquiring world knowledge, other than relations between entities, from text; we aim at extending the notion of constancy to other types of knowledge involving real-world entities, such as concept-instance relations. [sent-345, score-1.296]
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