acl acl2013 acl2013-111 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Koichi Tanigaki ; Mitsuteru Shiba ; Tatsuji Munaka ; Yoshinori Sagisaka
Abstract: This paper proposes a novel smoothing model with a combinatorial optimization scheme for all-words word sense disambiguation from untagged corpora. By generalizing discrete senses to a continuum, we introduce a smoothing in context-sense space to cope with data-sparsity resulting from a large variety of linguistic context and sense, as well as to exploit senseinterdependency among the words in the same text string. Through the smoothing, all the optimal senses are obtained at one time under maximum marginal likelihood criterion, by competitive probabilistic kernels made to reinforce one another among nearby words, and to suppress conflicting sense hypotheses within the same word. Experimental results confirmed the superiority of the proposed method over conventional ones by showing the better performances beyond most-frequent-sense baseline performance where none of SemEval2 unsupervised systems reached.
Reference: text
sentIndex sentText sentNum sentScore
1 By generalizing discrete senses to a continuum, we introduce a smoothing in context-sense space to cope with data-sparsity resulting from a large variety of linguistic context and sense, as well as to exploit senseinterdependency among the words in the same text string. [sent-2, score-0.412]
2 Through the smoothing, all the optimal senses are obtained at one time under maximum marginal likelihood criterion, by competitive probabilistic kernels made to reinforce one another among nearby words, and to suppress conflicting sense hypotheses within the same word. [sent-3, score-0.96]
3 1 Introduction Word Sense Disambiguation (WSD) is a task to identify the intended sense of a word based on its context. [sent-5, score-0.351]
4 They compute dictionary-based sense similarity to find the most related senses among the words within a certain range of text. [sent-12, score-0.501]
5 However, those methods mainly focus on modeling sense distribution and have less attention to contextual smoothing/generalization beyond immediate context. [sent-17, score-0.372]
6 (2004) proposed a method to combine sense similarity with distributional similarity and configured predominant sense score. [sent-20, score-0.87]
7 (2009) used a k-nearest words on distributional similarity as context words. [sent-24, score-0.246]
8 They apply a LKB graph-based WSD to a target word together with the distributional context words, and showed that it yields better results on a domain dataset than just using immediate context words. [sent-25, score-0.423]
9 c A2s0s1o3ci Aatsiosonc fioartio Cno fmorpu Ctoamtiopnuatalt Lioin gauli Lsitnicgsu,i psatgices 8 4–893, studies are word-by-word WSD for target words, they demonstrated the effectiveness to enrich immediate context by corpus statistics. [sent-28, score-0.195]
10 This paper proposes a smoothing model that integrates dictionary-based semantic similarity and corpus-based context statistics, where a combinatorial optimization scheme is employed to deal with sense interdependency of the all-words WSD task. [sent-29, score-0.778]
11 A specific implementation of these metrics is described later in this paper, for now the context metric is generalized with a distance function dx(·, ·) and the sense metric with ds(·, ·). [sent-40, score-0.604]
12 For each xi, the intended sense of the word is to be found in a set of sense candidates Si = ⊆ S, where Mi is the number of sense c{ asnd}idates⊆ ⊆fo Sr t,h we hie-rthe word, S is the whole set of sense inventories in a dictionary. [sent-45, score-1.278]
13 Let the two-tuple hij = (xi, sij) be the hypothesis that the intended sense in xi is sij. [sent-46, score-0.88]
14 As (X, dx) eanntd o (S, ds) iereaccth composes a =m Xetri ×c space, H is also a metric space, provided a proper distance definition with dx and ds. [sent-48, score-0.197]
15 Here, we treat the space H as a continuous one, which means that we assume the relationship between context and sense can be generalized in continuous fashion. [sent-49, score-0.435]
16 According to the nature of continuity, once given a hypothesis hij for a certain word, we can extrapolate the hypothesis for another word of another sense hi′j′ = (xi′ , si′j′) sufficiently close to hij. [sent-53, score-0.931]
17 They control the∈ smoothing intensity in context and in sense, respectively. [sent-55, score-0.282]
18 Our objective is to determine the optimal sense for all the target words simultaneously. [sent-56, score-0.402]
19 We relax the integer constraints by introducing a sense probability parameter πij corresponding to each hij. [sent-58, score-0.354]
20 πij denotes the probability by which hij is true. [sent-59, score-0.486]
21 The probabilit∑y density extrapolated a ≤t hi′j′ by a probabilistic hypothesis hij is given as follows: Qij (hi′j′) ∝ πij K(hij , hi′j′) . [sent-61, score-0.694]
22 Due to the limitation of drawing, both the context metric space and the sense metric space are drawn schematically as 1-dimensional spaces (axes), actually arbitrary metric spaces similarity-based or feature-based are applicable. [sent-63, score-0.713]
23 The product metric space of the context metric space and the sense metric space composes a hypothesis space. [sent-64, score-0.817]
24 In the hypothesis space, n sense hypotheses for a certain word is represented as n points on the hyperplane that spreads across the sense metric space. [sent-65, score-0.9]
25 The two small circles in the middle of the figure represent the two sense hypotheses for a single word. [sent-66, score-0.445]
26 The position of a hypothesis represents which sense is assigned to the current word in 885 "Invasive, exotic plants cause particular problems for wildlife. [sent-67, score-0.44]
27 In accordance with geometric intuition, intensity of extrapolation is affected by the distance from a hypothesis, and by the probability of the hypothesis by itself. [sent-73, score-0.277]
28 Extrapolated probability density is represented by shadow thickness and surface height. [sent-74, score-0.259]
29 If there is another word in nearby context, the kernels can validate the sense of that word. [sent-75, score-0.505]
30 In the figure, there are two kernels in the context “Invasive, exotic . [sent-76, score-0.229]
31 They are two competing hypothesis for the senses decoy and flora of the word plants. [sent-80, score-0.201]
32 These kernels affect the senses of another ambiguous word tree in nearby context “Exotic . [sent-81, score-0.419]
33 ”, and extrapolate the most at the sense t ree nearby flora. [sent-84, score-0.486]
34 It affects little to the word far away in context or in sense as is the case for the background word in the figure. [sent-86, score-0.397]
35 Wider bandwidths bring stronger effect of generalization to further hypotheses, but too wide bandwidths smooth out detailed structure. [sent-88, score-0.43]
36 The bandwidths are the key for disambiguation, therefore they are to be optimized on a dataset together with sense probabilities. [sent-89, score-0.563]
37 3 Simultaneous Optimization of All-words WSD Given the smoothing model to extrapolate the senses of other words, we now make its instances interact to obtain the optimal combination of senses for all the words. [sent-90, score-0.527]
38 The parameters consist of a context bandwidth σx, a sense bandwidth σs, and sense probabilities πij for all i and j. [sent-93, score-0.874]
39 For convenience of description, the sense probabilities are all together denoted as a vector π = (. [sent-94, score-0.347]
40 We consider all the mappings from context to sense are latent, and find the optimal parameters by maximizing marginal pseudo likelihood based on probability density. [sent-102, score-0.592]
41 The likelihood is defined as follows: L(π,σx,σs;X) ≡ ln∏∑πijQ(hij), ∏i (3) ∑j where ∏i denotes the product over xi ∈ X, ∑j denotes∏ the summation over all possible sen∑ses sij ∈ Si for the current i-th context. [sent-103, score-0.266]
42 W∑e take as the unit of LOOCV not a wor∈d i Snstance∑ but a word type, because the instances of the same word type invariably have the same sense candidates, which still cause over-fitting when optimizing the sense bandwidth. [sent-107, score-0.618]
43 (6) When we optimize the parameters, the first term of Equation (6) in the right-hand side acts to reinforce nearby hypotheses among different words, whereas the second term acts to suppress conflicting hypotheses of the same word. [sent-113, score-0.48]
44 (10) Qi′j′ (hij) denotes the∑ probability density at hij extrapolated by hi′j′ alone, defined as follows: Qi′j′(hij) ≡ N −1 Niπi′j′K(hij,hi′j′). [sent-116, score-0.727]
45 The right term requires πij to agree with the ratio of responsibility of hij to the whole. [sent-120, score-0.45]
46 As for sense probabilities, we set the uniform probability in accordance with the number of sense candidates, thereby πij ← |Si |−1, where |Si | denotes the size of Si. [sent-128, score-0.717]
47 |ASs for, bandwidths, we set the mean squared distance in each metric; thereby σx2 ← N−1 ∑i,i′ dx2(xi, xi′) for context ban∑dwidth, and σs2 ← (∑i|Si|)−1 ∑i,i′ ∑j,j′ ds2(sij,si′j′) s∑ense ba|)ndw∑idth. [sent-129, score-0.183]
48 The sense hypotheses are depicted by twelve upward arrows. [sent-141, score-0.555]
49 Through the iterative parameter update, sense probabilities and kernel bandwidths were optimized to the dataset. [sent-144, score-0.562]
50 Figure 2(a) illustrates the initial status, where all the sense hypothesis are equivalently probable, thus they are in the most ambiguous status. [sent-145, score-0.375]
51 Initial bandwidths are set to the mean squared distance of all the hypotheses pairs, 887 Figure 2: Pseudo 2D data simulation to visualize the dynamics of the proposed simultaneous WSD with ambiguous five words and twelve sense hypotheses. [sent-146, score-0.902]
52 This is because our method is aiming at modeling not the disambiguation of clustermemberships but the disambiguation of senses for each word. [sent-159, score-0.393]
53 The context of word instances are tied to the distributional context of the word type in a large corpus. [sent-164, score-0.277]
54 To calculate sense similarities, we used the WordNet similarity package by Pedersen et al. [sent-165, score-0.366]
55 1, to obtain grammatical relations for the distributional similarity, as well as to obtain lemmata and part-of-speech (POS) tags which are required to look up the sense inventory of WordNet. [sent-174, score-0.41]
56 Based on the distributional similarity, we just used k-nearest neighbor words as the context of each target word. [sent-175, score-0.272]
57 To treat the above similarity functions of context and of sense as distance functions, we use the conversion: d(·, ·) ≡ −α ln(f(·, ·)/fmax), where cdo dnveneorsteiosn t:he d objective αdilstna(nfc(e· function, i. [sent-180, score-0.501]
58 , dx for context and ds for sense, while f and fmax denote the original similarity function and its maximum, respectively. [sent-182, score-0.215]
59 5 Evaluation To confirm the effect of the proposed smoothing model and its combinatorial optimization scheme, we conducted WSD evaluations. [sent-185, score-0.326]
60 For this reason, we evaluated all the sense probabilities as they were. [sent-200, score-0.347]
61 The context metric space was composed by knearest neighbor words of distributional similarity (Lin, 1998), as is described in Section 4. [sent-203, score-0.403]
62 Aluas efodr sense m 3,e 5tr,ic 10 space, we 0e,v 1a0lu0,a2t0e0d, t 3w0o0 measures i. [sent-205, score-0.309]
63 In every condition, stopping criterion of iteration is always the number of iteration (500 times), irrespective of the convergence in likelihood. [sent-208, score-0.198]
64 (2004), which determines the word sense based on sense similarity and distributional similarity to the k-nearest neighbor words of a target word by distributional similarity. [sent-212, score-1.017]
65 Our major advantage is the combinatorial optimization framework, while the conventional one employs word-by-word scheme. [sent-213, score-0.214]
66 (2007), which determines the word sense by maximizing the sum of sense similarity to the k immediate neighbor words of a target word. [sent-215, score-0.821]
67 Our major advantages are the combinatorial optimization scheme and the smoothing model to integrate distributional similarity. [sent-219, score-0.425]
68 Thus we can conclude that, though significance depends on metrics, our smoothing model and the optimization scheme are effective to improve accuracies. [sent-293, score-0.248]
69 Let us start by looking at the upper half of Figure 5, which shows the change of sense probabilities through iteration. [sent-314, score-0.387]
70 As iteration proceeded, the probabilities gradually spread out to either side of 1 or 0, and finally at iteration 500, we can observe that almost all the words were clearly disambiguated. [sent-316, score-0.236]
71 Vertical axis on the left is for the sense bandwidth, and on the right is for the context bandwidth. [sent-318, score-0.397]
72 We can observe those bandwidths became narrower as iteration proceeded. [sent-319, score-0.314]
73 Intensity of smoothing was dynamically adjusted by the whole disambiguation status. [sent-320, score-0.28]
74 6 Discussion This section discusses the validity of the proposed method as to i) sense-interdependent disambiguation and ii) reliability of data smoothing. [sent-322, score-0.197]
75 This means that the ranks of sense candidates for each word were frequently altered through iteration, which further means that some new information not obtained earlier was delivered one after another to sense disambiguation for each word. [sent-331, score-0.747]
76 From these results, we could confirm the expected sense-interdependency effect that a sense disambiguation of certain word affected to other words. [sent-332, score-0.483]
77 2 Reliability of Smoothing as Supervision Let us now discuss the reliability of our smoothing model. [sent-334, score-0.219]
78 In our method, sense disambiguation of a word is guided by its nearby words’ extrapolation (smoothing). [sent-335, score-0.634]
79 If we take sufficient number of random words as nearby words, the sense distribution comes close to the true distribution, and then we expect the statistically true sense distribution should find out the true sense of the target word, according to the distributional hypotheses (Harris, 1954). [sent-338, score-1.328]
80 On the contrary, if we take nearby words that are biased to particular words, the sense distribution also becomes biased, and the extrapolation becomes less reliable. [sent-339, score-0.505]
81 We can compute the randomness of words that affect for sense disambiguation, by word perplexity. [sent-340, score-0.309]
82 T Hhe| condit∑ional probability p(w′|w) denotes the probability with which a cert|wai)n dwenoordte sw ′t ∈ oVb \ {w} determines the sense of w, whi∈ch can {bew }def dienteedrm as sthe t d∑ensity r oaftio: w p(w′|w) ∝ ∑i:wi=w ∑i′:wi′=w′ ∑j,j′ Qi′j′(hij). [sent-344, score-0.488]
83 The rela∑tion betwee∑n worQd perplexity and probability change for ground-truth senses of nouns (JCN/k = 30) is shown in Figure 7. [sent-345, score-0.274]
84 The upper histogram shows the change in iteration 1-100, and the lower shows that of iteration 101-500. [sent-346, score-0.238]
85 We divide the analysis at iteration 100, because roughly until the 100th iteration, the change in bandwidths converged, and the number of words to interact settled, as can be seen in Figure 5. [sent-347, score-0.354]
86 I onc contrast, argte tlhye ( l7o9w%er) tloef tth oef c tohrefigure, where perplexity is small (< 30) and bandwidths has been narrowed at iteration 101-500, correct change occupied only 32% of the whole. [sent-353, score-0.408]
87 Therefore, we can conclude that if sufficiently random samples of nearby words are provided, our smoothing model is reliable, though it is trained in an unsupervised fashion. [sent-354, score-0.353]
88 knowledge-based approaches typically regard senses as vertices (see Section 1), and corpusbased approaches such as (V´ eronis, 2004) regard words as vertices or (Niu et al. [sent-358, score-0.275]
89 Mihalcea (2005) proposed graph-based methods, whose vertices are sense label hypotheses on word sequence. [sent-361, score-0.515]
90 They disambiguated each target word using its distributionally similar words instead of its immediate context words. [sent-374, score-0.195]
91 Second, we extend the definition of density from Euclidean distance to general metric, which makes the proposed method applicable to a wide variety of corpus-based context similarities and dictionarybased sense similarities. [sent-379, score-0.577]
92 8 Conclusions We proposed a novel smoothing model with a combinatorial optimization scheme for all-words WSD from untagged corpora. [sent-380, score-0.384]
93 Moreover, our smoothing model, though unsupervised, provides reliable supervision when sufficiently random samples of words are available as nearby words. [sent-383, score-0.317]
94 Thus it was confirmed that this method is valid for finding the optimal combination of word senses with large untagged corpora. [sent-384, score-0.244]
95 Knowledgebased wsd on specific domains: performing better than generic supervised wsd. [sent-396, score-0.188]
96 Semeval-2010 task 17: All-words word sense disambiguation on a specific domain. [sent-400, score-0.438]
97 Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. [sent-428, score-0.438]
98 Unsupervised large-vocabulary word sense disambiguation with graph-based algorithms for sequence data labeling. [sent-444, score-0.438]
99 Word sense disambiguation using label propagation based semi-supervised learning. [sent-456, score-0.438]
100 UMND1 : Unsupervised word sense disambiguation using contextual semantic relatedness. [sent-464, score-0.438]
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