acl acl2012 acl2012-217 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Zhi Zhong ; Hwee Tou Ng
Abstract: Previous research has conflicting conclusions on whether word sense disambiguation (WSD) systems can improve information retrieval (IR) performance. In this paper, we propose a method to estimate sense distributions for short queries. Together with the senses predicted for words in documents, we propose a novel approach to incorporate word senses into the language modeling approach to IR and also exploit the integration of synonym relations. Our experimental results on standard TREC collections show that using the word senses tagged by a supervised WSD system, we obtain significant improvements over a state-of-the-art IR system.
Reference: text
sentIndex sentText sentNum sentScore
1 s g Abstract Previous research has conflicting conclusions on whether word sense disambiguation (WSD) systems can improve information retrieval (IR) performance. [sent-4, score-0.488]
2 Together with the senses predicted for words in documents, we propose a novel approach to incorporate word senses into the language modeling approach to IR and also exploit the integration of synonym relations. [sent-6, score-0.891]
3 Our experimental results on standard TREC collections show that using the word senses tagged by a supervised WSD system, we obtain significant improvements over a state-of-the-art IR system. [sent-7, score-0.439]
4 1 Introduction Word sense disambiguation (WSD) is the task of identifying the correct meaning of a word in context. [sent-8, score-0.341]
5 The 273 ambiguities of these query words can hurt retrieval precision. [sent-16, score-0.42]
6 Identifying the correct meaning of the ambiguous words in both queries and documents can help improve retrieval precision. [sent-17, score-0.364]
7 Some of the early research showed a drop in retrieval performance by using word senses (Krovetz and Croft, 1992; Voorhees, 1993). [sent-22, score-0.488]
8 Some other experiments observed improvements by integrating word senses in IR systems (Sch u¨tze and Pedersen, 1995; Gonzalo et al. [sent-23, score-0.372]
9 This paper proposes the use of word senses to improve the performance of IR. [sent-27, score-0.341]
10 We propose an approach to annotate the senses for short queries. [sent-28, score-0.341]
11 We incorporate word senses into the language modeling (LM) approach to IR (Ponte and Croft, 1998), and utilize sense synonym relations to further improve the performance. [sent-29, score-0.825]
12 c so2c0ia1t2io Ans fso rc Ciatoiomnp fuotart Cio nmaplu Ltiantgiounisatlic Lsi,n pgaugiestsi2c 7s3–282, generating word senses for query terms in Section 4, followed by presenting our novel method of incorporating word senses and their synonyms into the LM approach in Section 5. [sent-38, score-1.074]
13 Krovetz and Croft (1992) studied the sense matches between terms in query and the document collection. [sent-42, score-0.628]
14 They concluded that the benefits of WSD in IR are not as expected because query words have skewed sense distribution and the collocation effect from other query terms already performs some disambiguation. [sent-43, score-0.855]
15 Sanderson (1994; 2000) used pseudowords to introduce artificial word ambiguity in order to study the impact of sense ambiguity on IR. [sent-44, score-0.325]
16 They obtained significant improvements by representing documents and queries with accurate senses as well as synsets (synonym sets). [sent-48, score-0.589]
17 Several works attempted to disambiguate terms in both queries and documents with the senses predefined in hand-crafted sense inventories, and then used the senses to perform indexing and retrieval. [sent-52, score-1.303]
18 However, it is hard to judge the effect of word senses because of the overall poor performances of their baseline method and their system. [sent-60, score-0.374]
19 (2004) tagged words with 25 root senses of nouns in WordNet. [sent-62, score-0.365]
20 Their retrieval method maintained the stem-based index and adjusted the term weight in a document according to its sense matching result with the query. [sent-63, score-0.544]
21 They attributed the improvement achieved on TREC collections to their coarse-grained, consistent, and flexible sense tagging method. [sent-64, score-0.33]
22 The integration of senses into the traditional stem-based index overcomes some of the negative impact of disambiguation errors. [sent-65, score-0.447]
23 Different from using predefined sense inventories, Sch u¨tze and Pedersen (1995) induced the sense inventory directly from the text retrieval collection. [sent-66, score-0.726]
24 For each word, its occurrences were clustered into senses based on the similarities of their contexts. [sent-67, score-0.341]
25 Their experiments showed that using senses improved retrieval performance, and the combination of word-based ranking and sense-based ranking can further improve performance. [sent-68, score-0.488]
26 Because the sense inventory is collection dependent, it is also hard to expand the text collection without re-doing preprocessing. [sent-70, score-0.388]
27 Some researchers achieved improvements by expanding the disambiguated query words with synonyms and some other information from WordNet (Voorhees, 1994; Liu et al. [sent-72, score-0.358]
28 It is important to reduce the negative impact of erroneous disambiguation, and the integration of senses into traditional term index, such as stem-based index, is a possible solution. [sent-79, score-0.424]
29 It is also interesting to investigate the utilization of semantic relations among senses in IR. [sent-81, score-0.381]
30 1 The language modeling approach In the language modeling approach to IR, language models are constructed for each query q and each document d in a text collection C. [sent-84, score-0.362]
31 The documents in C are ranked by the distance to a given query q according to the language models. [sent-85, score-0.366]
32 One of the commonly used measures of the similarity between query model and document model is negative Kullback-Leibler (KL) divergence (Lafferty and Zhai, 2001). [sent-88, score-0.319]
33 In the first step, ranked documents are retrieved from C by a normal retrieval method with the original query q. [sent-98, score-0.625]
34 In the second step, a number of terms are selected from the top k ranked documents Dq for query expansion, under the assumption that these k documents are relevant to the query. [sent-99, score-0.523]
35 Then, the expanded query is used to retrieve the documents from C. [sent-100, score-0.366]
36 Finally, the relevance mPodel is interpolated with the original query model: + p(t|θpqrf) = λ p(t|θrq) (1 − λ)p(t|θq), (4) where parameter λ controls the amount of feedback. [sent-107, score-0.328]
37 The usage of X is supposed to provide more relevant feedback documents and feedback query terms. [sent-110, score-0.532]
38 Then, we propose the method of assigning senses to query terms. [sent-113, score-0.647]
39 1 Word sense disambiguation system Previous research shows that translations in another language can be used to disambiguate the meanings ofwords (Chan and Ng, 2005; Zhong and Ng, 2009). [sent-115, score-0.441]
40 2 Estimating sense distributions for query terms In IR, both terms in queries and the text collection can be ambiguous. [sent-135, score-0.781]
41 Similar to the PRF method, assuming that the top k documents retrieved by the basic method are relevant to the query, these k documents can be used to represent query q (Broder et al. [sent-143, score-0.603]
42 We propose a method to estimate the sense probabilities of each query term of q from these top k retrieved documents. [sent-146, score-0.703]
43 Given a query q, suppose Dq is the set of top k documents retrieved by the basic method, with the probability score p(q|θd) assigned to d ∈ Dq. [sent-149, score-0.507]
44 Basically, we utilized the sense distribution of the words with the same stem form in Dq as a proxy to estimate the sense probabilities of a query term. [sent-151, score-0.872]
45 The retrieval scores are used to weight the information from the corresponding retrieved documents in Dq. [sent-152, score-0.319]
46 5 Incorporating Senses into Language Modeling Approaches In this section, we propose to incorporate senses into the LM approach to IR. [sent-153, score-0.341]
47 Then, we describe the integration of sense synonym relations into our model. [sent-154, score-0.526]
48 1 Incorporating senses as smoothing With the method described in Section 4. [sent-156, score-0.414]
49 2, both the terms in queries and documents have been sense tagged. [sent-157, score-0.526]
50 Suppose p(t, s, q) is the probability of tagging a query term t ∈ q as sense s, and p(w, s, d) is the probability o tf tagging a nwseord s, occurrence w ∈ sd t as sense s. [sent-159, score-1.014]
51 tGyi voefn t a query q aorndd a cdoucruremncenet w wd ∈in dte axst collection C, we want to re-estimate the language models by making use of the sense information assigned to them. [sent-160, score-0.593]
52 Define the frequency of s in d as: stf (s, d) = Pw∈dp(w, s, d), and the frequency of s iPn C as: stf (s, C) = Pd∈C stf (s, d). [sent-161, score-0.694]
53 Define the frequencies ofP sense set S in d and C as: stf (S, d) = Ps∈S stf (s, d), stf (S, C) = PPs∈S stf (s, C). [sent-162, score-1.153]
54 , p(t, sn, q)} :is{ sthe vecto}r, souf probabilities assigned to the senses hofe vt catnodr W:{stf (s1 , d) , . [sent-169, score-0.341]
55 (6) In sen(t, q, d), the last item stf (S(t, q) , d) calculates the sum of the sense frequencies of t senses in d, which represents the amount of t’s sense information in d. [sent-176, score-1.173]
56 The first item α∆cos(t,q,d) is a weight of the sense information concerning the relative sense similarity ∆cos(t, q, d), where α is a positive parameter to control the impact of sense similarity. [sent-177, score-0.831]
57 When ∆cos(t, q, d) is larger than zero, such that the sense similarity of d and q according to t is above the av- erage, the weight for the sense information is larger than 1; otherwise, it is less than 1. [sent-178, score-0.554]
58 For t ∈/ q, because rteh,e sense set S(t, q) gish empty, stf (S(t, q) , d) equals to zero and tfsen (t, d) is identical to tf (t, d). [sent-180, score-0.575]
59 With sense incorporated, the term frequency is influenced by the sense information. [sent-181, score-0.624]
60 In this part, we further integrate the synonym relations of senses into the LM approach. [sent-186, score-0.548]
61 Suppose R(s) is the set of senses having synonym relation with sense s. [sent-187, score-0.785]
62 Define S(q) as the set of senses of query q, S(q) = St∈q S(t, q), and de- fine R(s, q) =R(s)−S(q). [sent-188, score-0.614]
63 We Supdate the frequency ofinf a query t=erRm( ts i−n Sd( by integrating hthee synonym relations as follows: + tfsyn (t, d) = tfsen (t, d) syn(t, q, d) , (8) where syn(t, q, d) is a function measuring the synonym information in d: syn(t,q,d) = X β(s,q)p(t,s,q)stf (R(s,q),d). [sent-189, score-0.72]
64 s∈XS(t) The last item stf (R(s, q) , d) in syn(t, q, d) is the sum of the sense frequencies of R(s, q) in d. [sent-190, score-0.517]
65 Notice that the synonym senses already appearing in S(q) are not included in the calculation, because the information of these senses has been used in some other places in the retrieval function. [sent-191, score-0.996]
66 The frequency of synonyms, stf (R(s, q) , d), is weighted by p(t, s, q) together with a scaling function β(s, q) : β(s, q) = min(1, stf (sRtf ( ss,,qC),)C) . [sent-192, score-0.453]
67 When stf (s, C), the frequency of sense s in C, is less than stf (R(s, q) , C), the frequency of R(s, q) in C, the function β(s, q) scales down the impact of synonyms according to the ratio of these two frequencies. [sent-193, score-0.813]
68 The scaling function makes sure that the overall impact of the synonym senses is not greater than the original word senses. [sent-194, score-0.508]
69 With this language mPodel, thPe probability of a query term in a document is enlarged by the synonyms of its senses; The more its synonym senses in a document, the higher the probability. [sent-196, score-0.951]
70 Consequently, documents with more synonym senses of the query terms will get higher retrieval rankings. [sent-197, score-1.053]
71 We use 50 queries from TREC6 Ad Hoc task as the development set, and evaluate on 50 queries from TREC7 Ad Hoc task, 50 queries from TREC8 Ad Hoc task, 50 queries from ROBUST 2003 (RB03), and 49 queries from ROBUST 2004 (RB04). [sent-203, score-0.647]
72 The first column lists the query topics, and the column #qry is the number of queries. [sent-207, score-0.353]
73 The column Ave gives the average query length, and the column Rels is the total number of relevant documents. [sent-208, score-0.385]
74 11as the basic retrieval tool, and select the default unigram LM approach based on KLdivergence and Dirichlet-prior smoothing method in Lemur as our basic retrieval approach. [sent-211, score-0.39]
75 We set the smoothing parameter in Equation 3 to 400 by tuning on TREC6 query set in a range of {100, 400, 700, 1000, 1500, 2000, 3000, 4000, 5000}. [sent-215, score-0.313]
76 {W1i0th0 t4h0is0 ,b7a0si0c, method, up to 100, top 0r,a4n0k0ed0, 5do00cu0-} ments Dq are retrieved for each query q from the extended text collection C ∪ X, for the usage of performing xPRt Fco alnledc generating query senses. [sent-216, score-0.71]
77 To estimate the sense distributions for terms in query q, the method described in Section 4. [sent-235, score-0.615]
78 The method Even assigns equal probabilities to all senses for each word, and the method MFS tags the words with their corresponding most frequent senses. [sent-239, score-0.407]
79 Assume that the senses with the same Chinese part are synonyms, therefore, we can generate a set of synonyms for each sense, and then utilize these synonym relations in the method proposed in Section 5. [sent-244, score-0.635]
80 The column Comb shows the results on the union of the four test query sets. [sent-252, score-0.313]
81 The rows Stemprf+{MFS, Even, WSD} are the results of Stemprf incorporating weni,th W tSheD senses generated for the original query terms, by applying the approach proposed in Section 5. [sent-257, score-0.644]
82 Comparing to the baseline method, all methods with sense integrated achieve consistent improvements on all query sets. [sent-259, score-0.581]
83 The integration of senses into the baseline method has two aspects of impact. [sent-261, score-0.416]
84 First, the morphological roots of senses conquer the irregular inflection problem. [sent-262, score-0.368]
85 Thus, the documents containing the irregular inflections are retrieved when senses are integrated. [sent-263, score-0.54]
86 2 6A s{ fseirnrky i ssi an irregular verb, the usage of senses improves the retrieval recall by retrieving the documents containing the inflection forms sunk, sank, and sunken. [sent-265, score-0.65]
87 Second, the senses output by supervised WSD system help identify the meanings of query terms. [sent-266, score-0.681]
88 Take topic 357 {territorial waters dispute} for example, tphiec s3t5em7 {foterrmri oofr waters eirss water aten}d oitsr appropriate sense in this query should be water 水域 (body of water) instead of the most frequent sense of water 水 (H2O). [sent-267, score-0.992]
89 In Stemprf +WSD, we correctly identify the minority sense for this query term. [sent-268, score-0.55]
90 Ay}l-, though the most frequent sense counterfeit 冒 牌 (not genuine) is not wrong, another sense counterfeit 伪钞 (forged money) is more accurate for this query term. [sent-270, score-0.887]
91 The integration of synonym FrSel,a Etivoenns, fWuSrtDhe}r+ improves the performance no matter what kind of sense tagging method is applied. [sent-276, score-0.542]
92 It shows that the WSD technique can help choose the appropriate senses for synonym expansion. [sent-281, score-0.508]
93 We proposed a method for annotating senses to terms in short queries, and also described an approach to integrate senses into an LM approach for IR. [sent-287, score-0.747]
94 In the experiment on four query sets of TREC collection, we compared the performance of a supervised WSD method and two WSD baseline methods. [sent-288, score-0.343]
95 Our experimental results showed that the incorporation of senses improved a state-of-the-art baseline, a stem-based LM approach with PRF method. [sent-289, score-0.341]
96 Enhancing query translation with relevance feedback in translingual information retrieval. [sent-391, score-0.374]
97 Information retrieval using word senses: root sense tagging approach. [sent-401, score-0.471]
98 Using WordNet to disambiguate word senses for text retrieval. [sent-509, score-0.411]
99 Word sense disambiguation for all words without hard labor. [sent-534, score-0.341]
100 It Makes Sense: A widecoverage word sense disambiguation system for free text. [sent-541, score-0.341]
wordName wordTfidf (topN-words)
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