Identification and Tracing of Ambiguous Names: Discriminative and Generative Approaches


 Claude Walsh
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1 Ientification an Tracing of Ambiguous Names: Discriminative an Generative Approaches Xin Li Paul Morie Dan Roth Department of Computer Science University of Illinois, Urbana, IL Abstract A given entity representing a person, a location or an organization may be mentione in text in multiple, ambiguous ways. Unerstaning natural language requires ientifying whether ifferent mentions of a name, within an across ocuments, represent the same entity. We present two machine learning approaches to this problem, which we call the Robust Reaing problem. Our first approach is a iscriminative approach, traine in a supervise way. Our secon approach is a generative moel, at the heart of which is a view on how ocuments are generate an how names (of ifferent entity types) are sprinkle into them. In its most general form, our moel assumes: (1) a joint istribution over entities (e.g., a ocument that mentions Presient Kenney is more likely to mention Oswal or White House than Roger Clemens ), (2) an author moel, that assumes that at least one mention of an entity in a ocument is easily ientifiable, an then generates other mentions via (3) an appearance moel, governing how mentions are transforme from the representative mention. We show that both approaches perform very accurately, in the range of 90% 95% F 1 measure for ifferent entity types, much better than previous approaches to (some aspects of) this problem. Our extensive experiments exhibit the contribution of relational an structural features an, somewhat surprisingly, that the assumptions mae within our generative moel are strong enough to yiel a very powerful approach, that performs better than a supervise approach with limite supervise information. Introuction Reaing an unerstaning text requires the ability to isambiguate at several levels, abstracting away etails an using backgroun knowlege in a variety of ways. One of the ifficulties that humans resolve instantaneously an unconsciously is that of reaing names. Most names of people, locations, organizations an others, have multiple writings that are being use freely within an across ocuments. The variability in writing a given concept, along with the fact that ifferent concepts may have very similar writings, poses a significant challenge to progress in natural language processing. Consier, for example, an open omain question answering system (Voorhees 2002) that attempts, given Copyright c 2004, American Association for Artificial Intelligence ( All rights reserve. a question like: When was Presient Kenney born? to search a large collection of articles in orer to pinpoint the concise answer: on May 29, The sentence, an even the ocument that contains the answer, may not contain the name Presient Kenney ; it may refer to this entity as Kenney, JFK or John Fitzgeral Kenney. Other ocuments may state that John F. Kenney, Jr. was born on November 25, 1960, but this fact refers to our target entity s son. Other mentions, such as Senator Kenney or Mrs. Kenney are even closer to the writing of the target entity, but clearly refer to ifferent entities. Even the statement John Kenney, born turns out to refer to a ifferent entity, as one can tell observing that the ocument iscusses Kenney s batting statistics. A similar problem exists for other entity types, such as locations an organizations. A hoc solutions to this problem, as we show, fail to provie a reliable an accurate solution. This paper presents two learning approaches to the problem of Robust Reaing, compares them to existing approaches an shows that an unsupervise learning approach can be applie very successfully to this problem, provie that it is use along with strong but realistic assumptions on the nature of the use of names in ocuments. Our first moel is a iscriminative approach that moels the problem as that of eciing whether any two names mentione in a collection of ocuments represent the same entity. This straightforwar moelling of the problem results in a classification problem as has been one by several other authors (Cohen, Ravikumar, & Fienberg 2003; Bilenko & Mooney 2003) allowing us to compare our results with these. This is a stanar pairwise classification task, uner a supervise learning protocol; our main contribution in this part is to show how relational (string an tokenlevel) features an structural features, representing transformations between names, can improve the performance of this classifier. Several attempts have been mae in the literature to improve the results by performing some global optimization, with the above mentione pairwise classifier as the similarity metric. The results of these attempts were not conclusive an we provie some explanation for why that is. We prove that, assuming optimal clustering, this approach reuces the error of a pairwise classifier in the case of two classes (corresponing to two entities); however, in the more general NATURAL LANGUAGE PROCESSING & INFORMATION EXTRACTION 419
2 case, when the number of entities (classes) is greater than 2, global optimization mechanisms coul be worse than pairwise classification. Our experiments concur with this. However, as we show, if one first splits the ata in some coherent way e.g., to groups of ocuments originate at about the same time perio this can ai clustering significantly. This observation motivates our secon moel. We evelope a global probabilistic moel for Robust Reaing, etaile in (Li, Morie, & Roth 2004). Here we briefly illustrate one of its instantiations an concentrate on its experimental stuy an a comparison to the iscriminative moels. At the heart of this approach is a view on how ocuments are generate an how names (of ifferent types) are sprinkle into them. In its most general form, our moel assumes: (1) a joint istribution over entities, so that a ocument that mentions Presient Kenney is more likely to mention Oswal or White House than Roger Clemens ; (2) an author moel, that makes sure that at least one mention of a name in a ocument is easily ientifiable (after all, that s the author s goal), an then generates other mentions via (3) an appearance moel, governing how mentions are transforme from the representative mention. Our goal is to learn the moel from a large corpus an use it to support Robust Reaing. Given a collection of ocuments we learn the moel in an unsupervise way; that is, the system is not tol uring training whether two mentions represent the same entity. We only assume, as in the iscriminative moel above, the ability to recognize names, using a name entity recognizer run as a preprocessor. Our experimental results are somewhat surprising; we show that the unsupervise approach can solve the problem accurately, giving accuracies (F 1 ) above 90%, an better than our iscriminative classifier (obviously, with a lot more ata). In the rest of the paper, we further stuy the iscriminative an generative moel in an attempt to provie explanations to the success of the unsupervise approach, an she light on the problem of Robust Reaing an hopefully, many relate problems. Our stuy aresses two issues: (1) Learning Protocol: A supervise learning approach is traine on a training corpus, an teste on a ifferent one, necessarily resulting in some egraation in performance. An unsupervise metho learns irectly on the target corpus. The ifference, as we show, is significant. (2) Structural assumptions: we show that our generative moel benefits from the assumptions mae in esigning it. This is shown by evaluating a fairly weak initialization of moel parameters, which still results in a moel for Robust Reaing with respectable results. We leave open the question of how much ata is require for a supervise learning methos before it can match the results of our supervise learner. There is little previous work we know of that irectly aresses the problem of Robust Reaing in a principle way, but some relate problems have been stuie. From the natural language perspective, there has been a lot of work on the relate problem of coreference resolution (Soon, Ng, & Lim 2001; Ng & Carie 2003; Kehler 2002) which aims at linking occurrences of noun phrases an pronouns, typically occurring in a close proximity within a short ocument or a paragraph, base on their appearance an local context. In the context of atabases, several works have looke at the problem of recor linkage  recognizing uplicate recors in a atabase (Cohen & Richman 2002; Hernanez & Stolfo 1995; Bilenko & Mooney 2003; Doan et al. 2003). Specifically, (Pasula et al. 2002) consiers the problem of ientity uncertainty in the context of citation matching an suggests a probabilistic moel for that. One of the few works we are aware of that works irectly with text ata an across ocuments, is (Mann & Yarowsky 2003), which consiers one aspect of the problem that of istinguishing occurrences of ientical names in ifferent ocuments, an only of people (e.g., o occurrences of Jim Clark in ifferent ocuments refer to the same person?). The rest of this paper first presents the experimental methoology an ata use in our evaluation; it then presents the esign of our pairwise name classifier, a comparison to other classifiers in the literature, an a iscussion of clustering on top of a pairwise classifier. Finally, we present the generative moel an compare the iscriminative an generative approaches along several imensions. Experimental Methoology In our experimental stuy we evaluate ifferent moels on the problem of robust reaing for three entity types: People (Peop), Locations (Loc) an Organizations (Org). We collecte 8, 000 names from ranomly sample New York Times articles in the TREC corpus (Voorhees 2002) (about 4,000 personal names 1, 2,000 locations an 2, 000 organizations). The ocuments were annotate by a name entity tagger for these entity types. The annotation was then manually correcte an each name mention was labelle with its corresponing entity by two annotators. 5 pairs of training an test sets with 600 names in each ata set are constructe for each of the three entity types by ranomly picking from the 8,000 names. For the iscriminative classifier, given a training set, we generate positive training examples using all coreferring pairs of names, an negative examples by ranomly selecting (10 times that number of) pairs of names that o not refer to the same entity. The probabilistic moel is traine using a larger corpus. The results in all the experiments in this paper are evaluate using the same test sets, except when comparing the clustering schemes. For a comparative evaluation, the outcomes of each approach on a test set of names are converte to a classification over all possible pairs of names (incluing nonmatching pairs). Since most pairs are trivial negative examples, an the classification accuracy can always reach nearly 100%, only the set M p of examples that are preicate to belong to the same entity (positive preictions) are use in evaluation, an are compare with the set M a of examples annotate as positive. The performance of an approach is then evaluate by Precision P = Mp Ma M p, Recall R = Mp Ma M a, an F 1 = 2PR P+R. Only F 1 values are shown an compare in this paper. 1 Honorifics an postfixes like Jr. are parts of a personal name. 420 NATURAL LANGUAGE PROCESSING & INFORMATION EXTRACTION
3 A Discriminative Moel The Robust Reaing problem can be formalize as pairwise classification: Fin a function f : N N {0,1} which classifies two strings (representing entity writings) in the name space N, as to whether they represent the same entity (0) or not (1). Most prior work aopte fixe string similarity metrics an create the esire function by simply thresholing the similarity between two names. (Cohen, Ravikumar, & Fienberg 2003) compare experimentally a variety of string similarity metrics on the task of matching entity names an foun that, overall, the bestperforming metho is a hybri scheme (SoftTFIDF) combining a TFIDF weighting scheme of tokens with the JaroWinkler stringistance scheme. (Bilenko & Mooney 2003) propose a learning framework (Marlin) for improving entity matching using trainable measures of textual similarity. They compare a learne eit istance measure an a learne vector space base measure that employs a classifier (SVM) with fixe measures (but not the ones mentione above) an showe some improvements in performance. We propose a learning approach, LMR, that focuses on representing a given pair of names using a collection of relational (string an tokenlevel) an structural features. Over these we learn, for each entity type, a linear classifier, that we train using the SNoW (Sparse Network of Winnows (Carlson et al. 1999)) learning architecture. A feature extractor automatically extracts active features in a atariven way for each pair of names. Our ecision is thus of the form: f(n 1, n 2) = arg max f l (n 1, n 2) = arg max l {0,1} l {0,1} k w i,l f i (1) where w i,l is the weight of feature f i in the function f l. Feature Extraction An example is generate by extracting features from a pair of names. Two types of features are use: relational features representing mappings between tokens in the two names, an structural features, representing the structural transformations of tokens in one name into tokens of the other. Each name is moelle as a partition in a bipartite graph, with each token in that name as a vertex (see Fig. 1). At most one tokenbase relational feature is extracte for each ege in the graph, by traversing a prioritize list of feature types until a feature is activate; if no active features are foun, it goes to the next pair of tokens. This scheme guarantees that only the most important (expressive) feature is activate for each pair of tokens. An aitional constraint is that each token in the smaller partition can only activate one feature. We use 13 types of tokenbase features. Some of the interesting types are: Honorific Equal: active if both tokens are honorifics an equal. An honorific is a title such as Mr., Mrs., Presient, Senator or Professor. Honorific Equivalence: active if both tokens are honorifics, not equal, but equivalent ( Professor, Prof. ). i=1 Honorific Mismatch : active for ifferent honorifics. NickName: active if tokens have a nickname relation ( Thomas an Tom ). John John Kenney J. Kenney Figure 1: There are two possible features for token 1 in the smaller partition but only the higher priority Equality feature is activate. F 1 (%) Marlin SoftTFIDF LMR Peop Loc Org Table 1: Performance of Different Pairwise Classifiers in an Ientical Setting. Results are evaluate in the average F 1 value over the 5 test sets for each entity type with 600 names in each of them. The columns are ifferent classifiers an LMR is our classifier. The learne classifiers are traine using corresponing training sets with 600 names. Equality: both names are equal. Eit Distance: the tokens have an eitistance of 1. Symbol Map: one token is a symbolic representative of the other ( an an & ). Relational features are not sufficient, since a nonmatching pair of names coul activate exactly the same set of tokenbase features as a matching pair. Consier, for example, two names that are all the same except that one has an aitional token. Our structural features were esigne to avoi this phenomenon. These features encoe information on the relative orer of tokens between the two names, by recoring the location of the participating tokens in the partition. E.g., for the pairs ( John Kenney, John Kenney ) an ( John Kenney, John Kenney Davis ), the active relational features are ientical; but, the first pair activates the structural features (1, 2) an (1, 2), while the secon pair activates (1,3) an (1,,2). Experimental Comparison Table 1 presents the average F 1 using three ifferent pairwise classifiers on the 5 test sets we escribe before. The best results of each classifier using a uniform threshol for each entity type are shown here. The LMR classifier outperforms the nonlearning SoftTFIDF classifier an the learne classifier (Marlin), uner ientical conitions. Table 2 shows the contribution of our feature types to the performance of LMR. The Baseline classifier only uses stringeitistance features an Equality features. The TokenBase classifier uses all relational tokenbase features while the Structural classifier uses, in aition, the F 1 (%) Baseline TokenBase Structural Peop Loc Org Table 2: Contribution of Different Feature Sets The LMR classifier is traine with ifferent feature sets using the 5 training sets. Results are evaluate in the average F 1 value over the 5 test sets for each entity type with 600 names in each of them. NATURAL LANGUAGE PROCESSING & INFORMATION EXTRACTION 421
4 structural features. Aing relational an structural features types is very significant, an more so to People ue to the larger amount of overlapping strings in ifferent entities. Does Clustering Help? There is a longhel intuition that using clustering on top of a pairwise classifier might reuce its error; several works (Cohen, Ravikumar, & Fienberg 2003; McCallum & Wellner 2003) have thus applie clustering in similar tasks. However, we prove here that while optimal clustering always helps to reuce the error of a pairwise classifier when there are two clusters (corresponing to two entities), in general, for k classes an k > 2, it is not the case. A typical clustering algorithm views ata points n N to be clustere as feature vectors n = (f 1,f 2,...,f ) in a imensional feature space. A ata point n is in one of k classes C = {C 1,C 2,...,C k }. From a generative view, the observe ata points D = {n 1,n 2,...,n t } are sample i.i.. from a joint probability istribution P efine over tuples (n,c) N C (P is a mixture of k moels). A istance metric ist(n 1,n 2 ) is use to quantify the ifference between two points. Clustering algorithms iffer in how they approximate the hien generative moels given D. Definition 1 Given two i.i. sample ata points (n 1,c 1 ) an (n 2,c 2 ) N C with only n 1,n 2 observe an c 1,c 2 hien, an P N C, the problem of Robust Reaing is to fin a function f : N N {0,1} which satisfies: f(n 1,n 2 ) = 0 iff c 1 = c 2 (2) Definition 2 The pairwise classification in this setting is: f p (n 1,n 2 ) = 0 ist(n 1,n 2 ) T (3) where T 0 is a threshol. The clustering base ecision: f c (n 1,n 2 ) = 0 argmax i p(n 1 C i ) = argmax i p(n 2 C i ) (4) Definition 3 The error rate of the pair function f is: err(f) = E P (f(n 1,n 2 ) I(n 1,n 2 )) (5) where I(n 1,n 2 ) = 0 iff c 1 = c 2 ;1, otherwise. Lemma 1 Assume ata is generate accoring to a uniform mixture of k Gaussians G = {g 1,g 2,...,g k } with the same covariance (i.e., a ata point is generate by first choosing one of k moels with probability p k (p k = 1/k), an then sampling accoring to the ith Gaussian chosen.), an suppose we fin the correct k Gaussian istributions after clustering, then for k = 2 an T, err(f c ) < err(f p ) (6) However, this oesn t hol in general for k > 2. Proof (sketch): 2 It s easy to see that the probability ensity over tuples in N C is f(n,c i ) = 1 k g i(n). The error rates err(f c ) an err(f p ) can be compute using the ensity function. Thus, for k = 2, we get err(f c ) = A2, 2 The assumption of Gaussian istributions can be relaxe. where A = R [g 1(X) g 2 (X)]X, an R is the area in the feature space that satisfies g 2 (X) > g 1 (X) (X N). R is a half space here. We also have: err(f p) = M(X 1,T) R [g2(x1) g1(x1)]x1 [g2(x2) g1(x2)]x2 > err(fc) where M(X 1,T) is the sphere area in the feature space whose point X satisfies ist(x 1,X) T. This is so since M(X [g 1,T) 2(X 2 ) g 1 (X 2 )]X 2 < R [g 2(X 2 ) g 1 (X 2 )]X 2. For k > 2, we can compute err(f c ) an err(f p ) in a similar way an compare them. We foun that in this case, each can be smaller than the other in ifferent cases. Specifically, when k i=1 g i(n) > 2 g j (n) for all j an n, an T 0, we have err(f c ) > err(f p ). To stuy this issue experimentally, we esigne an compare several clustering schemes for the Robust Reaing task 3. In a Direct clustering approach, we cluster name entities from a collection of ocuments with regar to the target entities, all at once. That is, the entities are viewe as the hien classes that generate the observe name entities in text. The clustering algorithm use is a stanar agglomerative clustering algorithm base on completelink (which performe better than other clustering methos we attempte). The evaluation, column 2 of Table 3, shows a egraation in the results relative to pairwise classification. Although, in general, clustering oes not help, we attempte to see if clustering can be helpe by exploiting some structural properties of the omain. We split the set of ocuments into three groups, each originate at about the same time perio, an clustere again. In this case (columns Hier (Date) Hierarchically clustering accoring to Dates), the results are significantly better than Direct clustering. We then performe a control experiment (Hier (Ranom)), in which we split the ocument set ranomly into three sets of the same sizes as before; the eterioration in the results in this case inicate that exploiting the structure is significant. The ata set use here was ifferent from the one use in other experiments. They were create by ranomly selecting 600 names for each entity type from the ocuments of years The 1,800 names for each entity type were ranomly split into equal training an test set. We traine the LMR pairwise classifier for each entity type using the corresponing annotate training set an cluster the test set with LMR evaluate as a similarity metric. A Generative Moel for Robust Reaing Motivate by the above observation, we escribe next a generative moel for Robust Reaing, esigne to exploit ocuments structure an assumptions on how they are generate. The etails of this moel are escribe in (Li, Morie, & Roth 2004). Here we briefly escribe one instantiation 3 Given the activation values of the classifier, we efine the istance metric as follows. Let p, n be the positive an negative activation values, respectively, for two names n 1 an n 2; then, ist(n 1, n 2) = en. e p +e n 422 NATURAL LANGUAGE PROCESSING & INFORMATION EXTRACTION
5 F 1 (%) LMR Direct Hier. (Date) Hier. (Ranom) Peop Loc Org Table 3: Best Performance of Different Clustering Approaches (Various parameter settings, incluing ifferent numbers of clusters were experimente within Direct Clustering.) LMR represents our pairwise classifier. The other three columns are ifferent clustering schemes. Results are evaluate by the F 1 values using the best threshol in experiments. The test set has 900 names for each entity type. John Fitzgeral Kenney M i e John Fitzgeral Kenney e i Presient John F. Kenney r i {Presient Kenney, Kenney, JFK} E House of Representatives E House of Representatives R House of Representatives M {House of Representatives, The House} Figure 2: Generating a ocument of the moel (Moel II there) an concentrate on its experimental stuy an a comparison to the iscriminative moels. We efine a probability istribution over ocuments = {E,R,M }, by escribing how ocuments are being generate. In its most general form the moel has the following three components: (1) A joint probability istribution P(E ) that governs how entities (of ifferent types) are istribute into a ocument an reflects their cooccurrence epenencies. (When initializing the moel escribe here, we assume that entities are chosen inepenently of each other.) (2) One mention of a name in a ocument, a representative r is easily ientifiable an other mentions are generate from it. The number of entities in a ocument, size(e ), an the number of mentions of each entity in E, size(mi ), are assume in the current evaluation to be istribute uniformly over a small plausible range so that they can be ignore in later inference. (3) The appearance probability of a name generate (transforme) from its representative is moelle as a prouct istribution over relational transformations of attribute values. This moel captures the similarity between appearances of two names. In the current evaluation the same appearance moel is use to calculate both the probability P(r e) that generates a representative r given an entity e an the probability P(m r) that generates a mention m given a representative r. Attribute transformations are relational, in the sense that the istribution is over transformation types an inepenent of the specific names. Fig. 2 epicts the generation process of a ocument. Thus, in orer to generate a ocument, after picking size(e ) an {size(m1 ),size(m2 ),...}, each entity e i is selecte into inepenently of others, accoring to P(e i ). Next, F 1 (%) Marlin SoftTFIDF LMR Generative Peop Loc Org Table 4: Discriminative an Generative Moels Results are evaluate by the average F 1 values over the 5 test sets for each entity type. Marlin, SoftTFIDF an LMR are the three pairwise classifiers; Generative is the generative moel. F 1 (%) LMR Generative (all) Generative (unseen ata) Peop Loc Org Table 5: Results of Different Learning Protocols for the Generative Moel. The table shows the results of our supervise classifier (LMR) traine with 600 names, the generative moel traine with all the 8, 000 names an the generative moel traine with the part of 8, 000 names not use in the corresponing test set. Results are evaluate an average over 5 test sets for each entity type. the representative ri for each entity e i is selecte accoring to P(ri e i ) an for each representative the actual mentions are selecte inepenently accoring to P(m j r j ). Assuming conitional inepenency between M an E given R, the probability istribution over ocuments is therefore P() = P(E, R, M ) = P(E )P(R E )P(M R ) E [P(e i )P(ri e i )] i=1 (r j,m j ) P(m j r j) after we ignore the size components. Given a mention m in a ocument (M is the set of observe mentions in ), the key inference problem is to etermine the most likely entity e m that correspons to it. This is one by computing: E = argmax E EP(E, R, M θ), (7) where θ is the learne moel s parameters. This gives the assignment of the most likely entity e m for m. An the probability of the ocument collection D is: P(D) = D P(). The moel parameters are learne in an unsupervise way given a collection of ocuments: that is, the information whether two mentions represent the same entity is unavailable uring training. A greey search algorithm moifie after the stanar EM algorithm is aopte here to simplify the computation. Given a set of ocuments D to be stuie an the observe mentions M in each ocument, this F 1 (%) Generative Initial Peop Loc Org Table 6: Performance of Simple Initialization. Generative the generative moel learne in a normal way. Initial the parameters of the generative moel initialize using some simple heuristics an use to cluster names. Results are evaluate by the average F 1 values over the 5 test sets for each entity type. NATURAL LANGUAGE PROCESSING & INFORMATION EXTRACTION 423
6 algorithm iteratively upates the moel parameter θ (several unerlying probabilistic istributions escribe before) an the structure (that is, E an R ) of each ocument by improving P(D θ). Unlike the stanar EM algorithm, in the Estep, it seeks the most likely E an R for each ocument rather than the expecte assignment. There is an initial step to acquire a coarse guess of ocument structures an to seek the set of entities E in a close collection of ocuments D. A local clustering is performe to group all mentions insie each ocument. A set of simple heuristics of matching attributes 4 is applie to computing the similarity between mentions, an pairs of mentions with similarity above a threshol are clustere together. A Comparison between the Moels We traine the generative moel in an unsupervise way with all 8, 000 names. The somewhat surprising results are shown in Table 4. The generative moel outperforme the supervise classifier for People an Organizations. That is, by incorporating a lot of unlabelle ata, the unsupervise learning coul o better. To unerstan this better, an to compare our iscriminative an generative approaches further, we aresse the following two issues: Learning protocol: A supervise learning approach is traine on a training corpus, an teste on a ifferent one resulting, necessarily, in some egraation in performance. An unsupervise metho learns irectly on the target corpus. This, as we show, can be significant. In a secon experiment, in which we o not train the generative moel on names it will see in the test set, results clearly egrae (Table 5). Structural assumptions: Our generative moel benefits from the structural assumptions mae in esigning the moel. We exhibit this by evaluating a fairly weak initialization of the moel, an showing that, nevertheless, this results in a Robust Reaing moel with respectable results. Table 6 shows that after initializing the moel parameters with the heuristics use in the EMlike algorithm, an without further training (but with the inference of the probabilistic moel), the generative moel can perform reasonably well. Conclusion The paper presents two learning approaches to the robust reaing problem crossocument ientification of names espite ambiguous writings. In aition to a stanar moelling of the problem as a classification task, we evelope a moel that escribes the natural generation process of a ocument an the process of how names are sprinkle into them, taking into account epenencies between entities across types an an author moel. We have shown that both moels gain a lot from incorporating structural an relational information features in the classification moel; coherent ata splits for clustering an the natural generation process in the case of the probabilistic moel. The robust reaing problem is a very significant barrier on our way towar supporting robust natural language processing, an the reliable an accurate results we show are 4 E.g., one example of a heuristics is: If mention n 1 is a substring of mention n 2, then their similarity is 0.8. thus very encouraging. Also important is the fact that our unsupervise moel performs so well, since the availability of annotate ata is, in many cases, a significant obstacle to goo performance in NLP tasks. In aition to further stuies of the iscriminative moel, incluing going beyon the current noisy supervision (given at a global annotation level, although learning is one locally). exploring how much ata is neee for a supervise moel to perform as well as the unsupervise moel, an whether the initialization of the unsupervise moel can gain from supervision, there are several other critical issues we woul like to aress from the robust reaing perspective. These inclue (1) incorporating more contextual information (like time an place) relate to the target entities, both to support a better moel an to allow temporal tracing of entities; (2) stuying an incremental approach to learning the moel an (3) integrating this work with other aspect of coreference resolution (e.g., pronouns). Acknowlegment This research is supporte by NSF grants IIS , ITR IIS an EIA , an ONR MURI Awar an an equipment onation from AMD. References Bilenko, M., an Mooney, R Aaptive uplicate etection using learnable string similarity measures. In KDD. Carlson, A.; Cumby, C.; Rosen, J.; an Roth, D The SNoW learning architecture. Technical Report UIUCDCSR , UIUC Computer Science Department. Cohen, W., an Richman, J Learning to match an cluster large highimensional ata sets for ata integration. In KDD. Cohen, W.; Ravikumar, P.; an Fienberg, S A comparison of string metrics for namematching tasks. In IIWeb Workshop Doan, A.; Lu, Y.; Lee, Y.; an Han, J Profilebase object matching for information integration. IEEE Intelligent Systems 18(5): Hernanez, M., an Stolfo, S The merge/purge problem for large atabases. In SIGMOD. Kehler, A Coherence, Reference, an the Theory of Grammar. CSLI Publications. Li, X.; Morie, P.; an Roth, D Robust reaing: Ientification an tracing of ambiguous names. In HLTNAACL. Mann, G., an Yarowsky, D Unsupervise personal name isambiguation. In CoNLL. McCallum, A., an Wellner, B Towar conitional moels of ientity uncertainty with application to proper noun coreference. In IJCAI Workshop on Information Integration on the Web. Ng, V., an Carie, C Improving machine learning approaches to coreference resolution. In ACL. Pasula, H.; Marthi, B.; Milch, B.; Russell, S.; an Shpitser, I Ientity uncertainty an citation matching. In NIPS. Soon, W.; Ng, H.; an Lim, D A machine learning approach to coreference resolution of noun phrases. Computational Linguistics (Special Issue on Computational Anaphora Resolution) 27: Voorhees, E Overview of the TREC2002 question answering track. In TREC, NATURAL LANGUAGE PROCESSING & INFORMATION EXTRACTION
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