{ "paper_id": "P09-1003", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T08:54:22.060163Z" }, "title": "A Comparative Study on Generalization of Semantic Roles in FrameNet", "authors": [ { "first": "Yuichiroh", "middle": [], "last": "Matsubayashi", "suffix": "", "affiliation": { "laboratory": "", "institution": "University of Tokyo", "location": { "country": "Japan" } }, "email": "" }, { "first": "Naoaki", "middle": [], "last": "Okazaki", "suffix": "", "affiliation": { "laboratory": "", "institution": "University of Tokyo", "location": { "country": "Japan" } }, "email": "okazaki@is.s.u-tokyo.ac.jp" }, { "first": "Jun", "middle": [ "'" ], "last": "Ichi Tsujii", "suffix": "", "affiliation": { "laboratory": "", "institution": "University of Tokyo", "location": { "country": "Japan" } }, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "A number of studies have presented machine-learning approaches to semantic role labeling with availability of corpora such as FrameNet and PropBank. These corpora define the semantic roles of predicates for each frame independently. Thus, it is crucial for the machine-learning approach to generalize semantic roles across different frames, and to increase the size of training instances. This paper explores several criteria for generalizing semantic roles in FrameNet: role hierarchy, human-understandable descriptors of roles, semantic types of filler phrases, and mappings from FrameNet roles to thematic roles of VerbNet. We also propose feature functions that naturally combine and weight these criteria, based on the training data. The experimental result of the role classification shows 19.16% and 7.42% improvements in error reduction rate and macro-averaged F1 score, respectively. We also provide in-depth analyses of the proposed criteria.", "pdf_parse": { "paper_id": "P09-1003", "_pdf_hash": "", "abstract": [ { "text": "A number of studies have presented machine-learning approaches to semantic role labeling with availability of corpora such as FrameNet and PropBank. These corpora define the semantic roles of predicates for each frame independently. Thus, it is crucial for the machine-learning approach to generalize semantic roles across different frames, and to increase the size of training instances. This paper explores several criteria for generalizing semantic roles in FrameNet: role hierarchy, human-understandable descriptors of roles, semantic types of filler phrases, and mappings from FrameNet roles to thematic roles of VerbNet. We also propose feature functions that naturally combine and weight these criteria, based on the training data. The experimental result of the role classification shows 19.16% and 7.42% improvements in error reduction rate and macro-averaged F1 score, respectively. We also provide in-depth analyses of the proposed criteria.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "Semantic Role Labeling (SRL) is a task of analyzing predicate-argument structures in texts. More specifically, SRL identifies predicates and their arguments with appropriate semantic roles. Resolving surface divergence of texts (e.g., voice of verbs and nominalizations) into unified semantic representations, SRL has attracted much attention from researchers into various NLP applications including question answering (Narayanan and Harabagiu, 2004; Shen and Lapata, 2007; Moschitti et al., 2007) , and information extraction (Surdeanu et al., 2003) .", "cite_spans": [ { "start": 419, "end": 450, "text": "(Narayanan and Harabagiu, 2004;", "ref_id": "BIBREF14" }, { "start": 451, "end": 473, "text": "Shen and Lapata, 2007;", "ref_id": "BIBREF17" }, { "start": 474, "end": 474, "text": "", "ref_id": null }, { "start": 475, "end": 498, "text": "Moschitti et al., 2007)", "ref_id": "BIBREF13" }, { "start": 528, "end": 551, "text": "(Surdeanu et al., 2003)", "ref_id": "BIBREF19" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "In recent years, with the wide availability of corpora such as PropBank (Palmer et al., 2005) and FrameNet (Baker et al., 1998) , a number of studies have presented statistical approaches to SRL . Figure 1 shows an example of the frame definitions for a verb buy in Prop-Bank and FrameNet. These corpora define a large number of frames and define the semantic roles for each frame independently. This fact is problematic in terms of the performance of the machinelearning approach, because these definitions produce many roles that have few training instances.", "cite_spans": [ { "start": 72, "end": 93, "text": "(Palmer et al., 2005)", "ref_id": "BIBREF16" }, { "start": 107, "end": 127, "text": "(Baker et al., 1998)", "ref_id": "BIBREF0" } ], "ref_spans": [ { "start": 197, "end": 205, "text": "Figure 1", "ref_id": "FIGREF0" } ], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "PropBank defines a frame for each sense of predicates (e.g., buy.01), and semantic roles are defined in a frame-specific manner (e.g., buyer and seller for buy.01). In addition, these roles are associated with tags such as ARG0-5 and AM-*, which are commonly used in different frames. Most SRL studies on PropBank have used these tags in order to gather a sufficient amount of training data, and to generalize semantic-role classifiers across different frames. However, Yi et al. (2007) reported that tags ARG2-ARG5 were inconsistent and not that suitable as training instances. Some recent studies have addressed alternative approaches to generalizing semantic roles across different frames (Gordon and Swanson, 2007; Zapi- rain et al., 2008) . FrameNet designs semantic roles as frame specific, but also defines hierarchical relations of semantic roles among frames. Figure 2 illustrates an excerpt of the role hierarchy in FrameNet; this figure indicates that the Buyer role for the Commerce buy frame (Commerce buy::Buyer hereafter) and the Commerce sell::Buyer role are inherited from the Transfer::Recipient role. Although the role hierarchy was expected to generalize semantic roles, no positive results for role classification have been reported (Baldewein et al., 2004) . Therefore, the generalization of semantic roles across different frames has been brought up as a critical issue for FrameNet (Gildea and Jurafsky, 2002; Shi and Mihalcea, 2005; Giuglea and Moschitti, 2006) In this paper, we explore several criteria for generalizing semantic roles in FrameNet. In addition to the FrameNet hierarchy, we use various pieces of information: human-understandable descriptors of roles, semantic types of filler phrases, and mappings from FrameNet roles to the thematic roles of VerbNet. We also propose feature functions that naturally combines these criteria in a machine-learning framework. Using the proposed method, the experimental result of the role classification shows 19.16% and 7.42% improvements in error reduction rate and macro-averaged F1, respectively. We provide in-depth analyses with respect to these criteria, and state our conclusions. Moschitti et al. (2005) first classified roles by using four coarse-grained classes (Core Roles, Adjuncts, Continuation Arguments and Co-referring Arguments), and built a classifier for each coarsegrained class to tag PropBank ARG tags. Even though the initial classifiers could perform rough estimations of semantic roles, this step was not able to solve the ambiguity problem in PropBank ARG2-5. When training a classifier for a seman-tic role, Baldewein et al. (2004) re-used the training instances of other roles that were similar to the target role. As similarity measures, they used the FrameNet hierarchy, peripheral roles of FrameNet, and clusters constructed by a EM-based method. Gordon and Swanson (2007) proposed a generalization method for the PropBank roles based on syntactic similarity in frames.", "cite_spans": [ { "start": 461, "end": 486, "text": "However, Yi et al. (2007)", "ref_id": null }, { "start": 692, "end": 718, "text": "(Gordon and Swanson, 2007;", "ref_id": "BIBREF9" }, { "start": 719, "end": 724, "text": "Zapi-", "ref_id": null }, { "start": 730, "end": 743, "text": "et al., 2008)", "ref_id": null }, { "start": 1254, "end": 1278, "text": "(Baldewein et al., 2004)", "ref_id": "BIBREF2" }, { "start": 1406, "end": 1433, "text": "(Gildea and Jurafsky, 2002;", "ref_id": "BIBREF7" }, { "start": 1434, "end": 1457, "text": "Shi and Mihalcea, 2005;", "ref_id": "BIBREF18" }, { "start": 1458, "end": 1486, "text": "Giuglea and Moschitti, 2006)", "ref_id": "BIBREF8" }, { "start": 2165, "end": 2188, "text": "Moschitti et al. (2005)", "ref_id": "BIBREF12" }, { "start": 2612, "end": 2635, "text": "Baldewein et al. (2004)", "ref_id": "BIBREF2" }, { "start": 2855, "end": 2880, "text": "Gordon and Swanson (2007)", "ref_id": "BIBREF9" } ], "ref_spans": [ { "start": 869, "end": 877, "text": "Figure 2", "ref_id": "FIGREF1" } ], "eq_spans": [], "section": "Introduction", "sec_num": "1" }, { "text": "Many previous studies assumed that thematic roles bridged semantic roles in different frames. Gildea and Jurafsky (2002) showed that classification accuracy was improved by manually replacing FrameNet roles into 18 thematic roles. Shi and Mihalcea (2005) and Giuglea and Moschitti (2006) employed VerbNet thematic roles as the target of mappings from the roles defined by the different semantic corpora. Using the thematic roles as alternatives of ARG tags, and demonstrated that the classification accuracy of PropBank roles was improved for ARG2 roles, but that it was diminished for ARG1. also described that ARG2-5 were mapped to a variety of thematic roles. Zapirain et al. (2008) evaluated PropBank ARG tags and VerbNet thematic roles in a state-ofthe-art SRL system, and concluded that PropBank ARG tags achieved a more robust generalization of the roles than did VerbNet thematic roles.", "cite_spans": [ { "start": 94, "end": 120, "text": "Gildea and Jurafsky (2002)", "ref_id": "BIBREF7" }, { "start": 231, "end": 254, "text": "Shi and Mihalcea (2005)", "ref_id": "BIBREF18" }, { "start": 259, "end": 287, "text": "Giuglea and Moschitti (2006)", "ref_id": "BIBREF8" }, { "start": 663, "end": 685, "text": "Zapirain et al. (2008)", "ref_id": "BIBREF21" } ], "ref_spans": [], "eq_spans": [], "section": "Related Work", "sec_num": "2" }, { "text": "SRL is a complex task wherein several problems are intertwined: frame-evoking word identification, frame disambiguation (selecting a correct frame from candidates for the evoking word), rolephrase identification (identifying phrases that fill semantic roles), and role classification (assigning correct roles to the phrases). In this paper, we focus on role classification, in which the role generalization is particularly critical to the machine learning approach.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Role Classification", "sec_num": "3" }, { "text": "In the role classification task, we are given a sentence, a frame evoking word, a frame, and phrases that take semantic roles. We are interested in choosing the correct role from the candidate roles for each phrase in the frame. Figure 3 shows a concrete example of input and output; the semantic roles for the phrases are chosen from the candidate roles: Seller, Buyer, Goods, Reason, ... , and Place.", "cite_spans": [], "ref_spans": [ { "start": 229, "end": 237, "text": "Figure 3", "ref_id": "FIGREF2" } ], "eq_spans": [], "section": "Role Classification", "sec_num": "3" }, { "text": "We formalize the generalization of semantic roles as the act of grouping several roles into a class. We define a role group as a set of role labels grouped by a criterion. Figure 4 shows examples of role groups; a group Giving::Donor (in the hierarchical-relation groups) contains the roles Giving::Donor and Commerce pay::Buyer. The remainder of this section describes the grouping criteria in detail.", "cite_spans": [], "ref_spans": [ { "start": 172, "end": 180, "text": "Figure 4", "ref_id": null } ], "eq_spans": [], "section": "Design of Role Groups", "sec_num": "4" }, { "text": "FrameNet defines hierarchical relations among frames (frame-to-frame relations). Each relation is assigned one of the seven types of directional relationships (Inheritance, Using, Perspective on, Causative of, Inchoative of, Subframe, and Precedes). Some roles in two related frames are also connected with role-to-role relations. We assume that this hierarchy is a promising resource for generalizing the semantic roles; the idea is that the role at a node in the hierarchy inherits the characteristics of the roles of its ancestor nodes. For example, Commerce sell::Seller in Figure 2 inherits the property of Giving::Donor. For Inheritance, Using, Perspective on, and Subframe relations, we assume that descendant roles in these relations have the same or specialized properties of their ancestors. Hence, for each role y i , we define the following two role groups,", "cite_spans": [], "ref_spans": [ { "start": 578, "end": 586, "text": "Figure 2", "ref_id": "FIGREF1" } ], "eq_spans": [], "section": "Hierarchical relations among roles", "sec_num": "4.1" }, { "text": "H child y i = {y|y = y i \u2228 y is a child of y i }, H desc y i = {y|y = y i \u2228 y is a descendant of y i }.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Hierarchical relations among roles", "sec_num": "4.1" }, { "text": "The hierarchical-relation groups in Figure 4 are the illustrations of H desc y i . For the relation types Inchoative of and Causative of, we define role groups in the opposite direction of the hierarchy,", "cite_spans": [], "ref_spans": [ { "start": 36, "end": 44, "text": "Figure 4", "ref_id": null } ], "eq_spans": [], "section": "Hierarchical relations among roles", "sec_num": "4.1" }, { "text": "H parent y i = {y|y = y i \u2228 y is a parent of y i }, H ance y i = {y|y = y i \u2228 y is an ancestor of y i }.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Hierarchical relations among roles", "sec_num": "4.1" }, { "text": "This is because lower roles of Inchoative of and Causative of relations represent more neutral stances or consequential states; for example, Killing::Victim is a parent of Death::Protagonist in the Causative of relation.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Hierarchical relations among roles", "sec_num": "4.1" }, { "text": "Finally, the Precedes relation describes the sequence of states and events, but does not specify the direction of semantic inclusion relations. Therefore, we simply try H child", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Hierarchical relations among roles", "sec_num": "4.1" }, { "text": "y i , H desc y i , H parent y i", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Hierarchical relations among roles", "sec_num": "4.1" }, { "text": ", and H ance y i for this relation type.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Hierarchical relations among roles", "sec_num": "4.1" }, { "text": "FrameNet defines each role as frame-specific; in other words, the same identifier does not appear in different frames. However, in FrameNet, human experts assign a human-understandable name to each role in a rather systematic manner. Some names are shared by the roles in different frames, whose identifiers are different. Therefore, we examine the semantic commonality of these names; we construct an equivalence class of the roles sharing the same name.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Human-understandable role descriptor", "sec_num": "4.2" }, { "text": "We call these human-understandable names role descriptors. In Figure 4 , the roledescriptor group Buyer collects the roles Commerce pay::Buyer, Commerce buy::Buyer, and Commerce sell::Buyer.", "cite_spans": [], "ref_spans": [ { "start": 62, "end": 70, "text": "Figure 4", "ref_id": null } ], "eq_spans": [], "section": "Human-understandable role descriptor", "sec_num": "4.2" }, { "text": "This criterion may be effective in collecting similar roles since the descriptors have been annotated by intuition of human experts. As illustrated in Figure 2 , the role descriptors group the semantic roles which are similar to the roles that the FrameNet hierarchy connects as sister or parentchild relations. However, role-descriptor groups cannot express the relations between the roles as inclusions since they are equivalence classes. For example, the roles Commerce sell::Buyer and Commerce buy::Buyer are included in the role descriptor group Buyer in Figure 2 ; however, it is difficult to merge Giving::Recipient and Commerce sell::Buyer because the Commerce sell::Buyer has the extra property that one gives something of value in exchange and a human assigns different descriptors to them. We expect that the most effective weighting of these two criteria will be determined from the training data.", "cite_spans": [], "ref_spans": [ { "start": 151, "end": 159, "text": "Figure 2", "ref_id": "FIGREF1" }, { "start": 560, "end": 568, "text": "Figure 2", "ref_id": "FIGREF1" } ], "eq_spans": [], "section": "Human-understandable role descriptor", "sec_num": "4.2" }, { "text": "We consider that the selectional restriction is helpful in detecting the semantic roles. FrameNet provides information concerning the semantic types of role phrases (fillers); phrases that play specific roles in a sentence should fulfill the semantic constraint from this information. For instance, FrameNet specifies the constraint that Self motion::Area should be filled by phrases whose semantic type is Location. Since these types suggest a coarse-grained categorization of semantic roles, we construct role groups that contain roles whose semantic types are identical.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Semantic type of phrases", "sec_num": "4.3" }, { "text": "VerbNet thematic roles are 23 frame-independent semantic categories for arguments of verbs, such as Agent, Patient, Theme and Source. These categories have been used as consistent labels across verbs.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Thematic roles of VerbNet", "sec_num": "4.4" }, { "text": "We use a partial mapping between FrameNet roles and Verb-Net thematic roles provided by SemLink. 1 Each group is constructed as a set T t i = 1 http://verbs.colorado.edu/semlink/ {y|SemLink maps y into the thematic role t i }.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Thematic roles of VerbNet", "sec_num": "4.4" }, { "text": "SemLink currently maps 1,726 FrameNet roles into VerbNet thematic roles, which are 37.61% of roles appearing at least once in the FrameNet corpus. This may diminish the effect of thematic-role groups than its potential.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Thematic roles of VerbNet", "sec_num": "4.4" }, { "text": "We are given a frame-evoking word e, a frame f and a role phrase x detected by a human or some automatic process in a sentence s. Let Y f be the set of semantic roles that FrameNet defines as being possible role assignments for the frame f , and let x = {x 1 , . . . , x n } be observed features for x from s, e and f . The task of semantic role classification can be formalized as the problem of choosing the most suitable role\u1ef9 from Y f . Suppose we have a model P (y|f, x) which yields the conditional probability of the semantic role y for given f and x. Then we can choose\u1ef9 as follows:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Traditional approach", "sec_num": "5.1" }, { "text": "EQUATION", "cite_spans": [], "ref_spans": [], "eq_spans": [ { "start": 0, "end": 8, "text": "EQUATION", "ref_id": "EQREF", "raw_str": "y = argmax y\u2208Y f P (y|f, x).", "eq_num": "(1)" } ], "section": "Traditional approach", "sec_num": "5.1" }, { "text": "A traditional way to incorporate role groups into this formalization is to overwrite each role y in the training and test data with its role group m(y) according to the memberships of the group. For example, semantic roles Commerce sell::Seller and Giving::Donor can be replaced by their thematic-role group Theme::Agent in this approach. We determine the most suitable role groupc as follows:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Traditional approach", "sec_num": "5.1" }, { "text": "EQUATION", "cite_spans": [], "ref_spans": [], "eq_spans": [ { "start": 0, "end": 8, "text": "EQUATION", "ref_id": "EQREF", "raw_str": "c = argmax c\u2208{m(y)|y\u2208Y f } P m (c|f, x).", "eq_num": "(2)" } ], "section": "Traditional approach", "sec_num": "5.1" }, { "text": "Here, P m (c|f, x) presents the probability of the role group c for f and x. The role\u1ef9 is determined uniquely iff a single role y \u2208 Y f is associated withc. Some previous studies have employed this idea to remedy the data sparseness problem in the training data (Gildea and Jurafsky, 2002) . However, we cannot apply this approach when multiple roles in Y f are contained in the same class. For example, we can construct a semantic-type group In addition, it may be more effective to use various groupings of roles together in the model. For instance, the model could predict the correct role Commerce sell::Seller for the phrase \"you\" in Figure 3 more confidently, if it could infer its thematic-role group as Theme::Agent and its parent group Giving::Donor correctly. Although the ensemble of various groupings seems promising, we need an additional procedure to prioritize the groupings for the case where the models for multiple role groupings disagree; for example, it is unsatisfactory if two models assign the groups Giving::Theme and Theme::Agent to the same phrase.", "cite_spans": [ { "start": 262, "end": 289, "text": "(Gildea and Jurafsky, 2002)", "ref_id": "BIBREF7" } ], "ref_spans": [ { "start": 639, "end": 647, "text": "Figure 3", "ref_id": "FIGREF2" } ], "eq_spans": [], "section": "Traditional approach", "sec_num": "5.1" }, { "text": "We thus propose another approach that incorporates group information as feature functions. We model the conditional probability P (y|f, x) by using the maximum entropy framework,", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Role groups as feature functions", "sec_num": "5.2" }, { "text": "p(y|f, x) = exp( \u2211 i \u03bb i g i (x, y)) \u2211 y\u2208Y f exp( \u2211 i \u03bb i g i (x, y))", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Role groups as feature functions", "sec_num": "5.2" }, { "text": ". 3Here, G = {g i } denotes a set of n feature functions, and \u039b = {\u03bb i } denotes a weight vector for the feature functions. In general, feature functions for the maximum entropy model are designed as indicator functions for possible pairs of x j and y. For example, the event where the head word of x is \"you\" (x 1 = 1) and x plays the role Commerce sell::Seller in a sentence is expressed by the indicator function,", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Role groups as feature functions", "sec_num": "5.2" }, { "text": "g role 1 (x, y) = \uf8f1 \uf8f4 \uf8f2 \uf8f4 \uf8f3 1 (x 1 = 1 \u2227 y = Commerce sell::Seller) 0 (otherwise) . (4)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Role groups as feature functions", "sec_num": "5.2" }, { "text": "We call this kind of feature function an x-role.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Role groups as feature functions", "sec_num": "5.2" }, { "text": "In order to incorporate role groups into the model, we also include all feature functions for possible pairs of x j and role groups. Equation 5 is an example of a feature function for instances where the head word of x is \"you\" and y is in the role group Theme::Agent,", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Role groups as feature functions", "sec_num": "5.2" }, { "text": "g theme 2 (x, y) = \uf8f1 \uf8f4 \uf8f2 \uf8f4 \uf8f3 1 (x 1 = 1 \u2227 y \u2208 Theme::Agent) 0 (otherwise) . (5)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Role groups as feature functions", "sec_num": "5.2" }, { "text": "Thus, this feature function fires for the roles wherever the head word \"you\" plays Agent (e.g., Commerce sell::Seller, Commerce buy::Buyer and Giving::Donor). We call this kind of feature function an x-group function.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Role groups as feature functions", "sec_num": "5.2" }, { "text": "In this way, we obtain x-group functions for all grouping methods, e.g., g theme k , g hierarchy k . The role-group features will receive more training instances by collecting instances for fine-grained roles. Thus, semantic roles with few training instances are expected to receive additional clues from other training instances via role-group features. Another advantage of this approach is that the usefulness of the different role groups is determined by the training processes in terms of weights of feature functions. Thus, we do not need to assume that we have found the best criterion for grouping roles; we can allow a training process to choose the criterion. We will discuss the contributions of different groupings in the experiments. Baldewein et al. (2004) suggested an approach that uses role descriptors and hierarchical relations as criteria for generalizing semantic roles in FrameNet. They created a classifier for each frame, additionally using training instances for the role A to train the classifier for the role B, if the roles A and B were judged as similar by a criterion. This approach performs similarly to the overwriting approach, and it may obscure the differences among roles. Therefore, they only re-used the descriptors as a similarity measure for the roles whose coreness was peripheral. 2 In contrast, we use all kinds of role descriptors to construct groups. Since we use the feature functions for both the original roles and their groups, appropriate units for classification are determined automatically in the training process.", "cite_spans": [ { "start": 747, "end": 770, "text": "Baldewein et al. (2004)", "ref_id": "BIBREF2" }, { "start": 1323, "end": 1324, "text": "2", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Role groups as feature functions", "sec_num": "5.2" }, { "text": "We used the training set of the Semeval-2007 Shared task (Baker et al., 2007) in order to ascertain the contributions of role groups. This dataset consists of the corpus of FrameNet release 1.3 (containing roughly 150,000 annotations), and an additional full-text annotation dataset. We randomly extracted 10% of the dataset for testing, and used the remainder (90%) for training.", "cite_spans": [ { "start": 57, "end": 77, "text": "(Baker et al., 2007)", "ref_id": "BIBREF1" } ], "ref_spans": [], "eq_spans": [], "section": "Experiment and Discussion", "sec_num": "6" }, { "text": "Performance was measured by micro-and macro-averaged F1 (Chang and Zheng, 2008) with respect to a variety of roles. The micro average biases each F1 score by the frequencies of the roles, and the average is equal to the classification accuracy when we calculate it with all of the roles in the test set. In contrast, the macro average does not bias the scores, thus the roles having a small number of instances affect the average more than the micro average.", "cite_spans": [ { "start": 56, "end": 79, "text": "(Chang and Zheng, 2008)", "ref_id": "BIBREF4" } ], "ref_spans": [], "eq_spans": [], "section": "Experiment and Discussion", "sec_num": "6" }, { "text": "We constructed a baseline classifier that uses only the x-role features.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Experimental settings", "sec_num": "6.1" }, { "text": "The feature design is similar to that of the previous studies . The characteristics of x are: frame, frame evoking word, head word, content word (Surdeanu et al., 2003) , first/last word, head word of left/right sister, phrase type, position, voice, syntactic path (directed/undirected/partial), governing category (Gildea and Jurafsky, 2002) , WordNet supersense in the phrase, combination features of frame evoking word & headword, combination features of frame evoking word & phrase type, and combination features of voice & phrase type. We also used PoS tags and stem forms as extra features of any word-features.", "cite_spans": [ { "start": 145, "end": 168, "text": "(Surdeanu et al., 2003)", "ref_id": "BIBREF19" }, { "start": 315, "end": 342, "text": "(Gildea and Jurafsky, 2002)", "ref_id": "BIBREF7" } ], "ref_spans": [], "eq_spans": [], "section": "Experimental settings", "sec_num": "6.1" }, { "text": "We employed Charniak and Johnson's reranking parser (Charniak and Johnson, 2005) to analyze syntactic trees. As an alternative for the traditional named-entity features, we used Word-Net supersenses: 41 coarse-grained semantic categories of words such as person, plant, state, event, time, location. We used Ciaramita and Altun's Super Sense Tagger (Ciaramita and Altun, 2006) to tag the supersenses. The baseline system achieved 89.00% with respect to the micro-averaged F1.", "cite_spans": [ { "start": 52, "end": 80, "text": "(Charniak and Johnson, 2005)", "ref_id": "BIBREF5" }, { "start": 349, "end": 376, "text": "(Ciaramita and Altun, 2006)", "ref_id": "BIBREF6" } ], "ref_spans": [], "eq_spans": [], "section": "Experimental settings", "sec_num": "6.1" }, { "text": "The x-group features were instantiated similarly to the x-role features; the x-group features combined the characteristics of x with the role groups presented in this paper. The total number of features generated for all x-roles and x-groups was 74,873,602. The optimal weights \u039b of the features were obtained by the maximum a posterior (MAP) estimation. We maximized an L 2regularized log-likelihood of the training set using the Limited-memory BFGS (L-BFGS) method (Nocedal, 1980) . Table 1 shows the micro and macro averages of F1 scores. Each role group type improved the micro average by 0.5 to 1.7 points. The best result was obtained by using all types of groups together. The result indicates that different kinds of group com- plement each other with respect to semantic role generalization. Baldewein et al. (2004) reported that hierarchical relations did not perform well for their method and experimental setting; however, we found that significant improvements could also be achieved with hierarchical relations. We also tried a traditional label-replacing approach with role descriptors (in the third row of Table 1 ). The comparison between the second and third rows indicates that mixing the original fine-grained roles and the role groups does result in a more accurate classification. By using all types of groups together, the model reduced 19.16 % of the classification errors from the baseline. Moreover, the macro-averaged F1 scores clearly showed improvements resulting from using role groups. In order to determine the reason for the improvements, we measured the precision, recall, and F1-scores with respect to roles for which the number of training instances was at most 10, 20, and 50. In Table 2 , we show that the micro-averaged F1 score for roles having 10 instances or less was improved (by 15.46 points) when all role groups were used. This result suggests the reason for the effect of role groups; by bridging similar semantic roles, they supply roles having a small number of instances with the information from other roles.", "cite_spans": [ { "start": 467, "end": 482, "text": "(Nocedal, 1980)", "ref_id": "BIBREF15" }, { "start": 801, "end": 824, "text": "Baldewein et al. (2004)", "ref_id": "BIBREF2" } ], "ref_spans": [ { "start": 485, "end": 492, "text": "Table 1", "ref_id": "TABREF3" }, { "start": 1122, "end": 1129, "text": "Table 1", "ref_id": "TABREF3" }, { "start": 1717, "end": 1724, "text": "Table 2", "ref_id": "TABREF4" } ], "eq_spans": [], "section": "Experimental settings", "sec_num": "6.1" }, { "text": "In Table 1 , the largest improvement was obtained by the use of role descriptors. We analyze the effect of role descriptors in detail in Tables 3 and 4 . semantic roles when we use role-descriptor groups constructed from each type of coreness (core 3 , peripheral, and extra-thematic) individually. The peripheral type generated the largest improvements. Table 4 shows the number of roles associated with each type of coreness (#roles), the number of instances for the original roles (#instances/#role), the number of groups for each type of coreness (#groups), the number of instances for each group (#instances/#group), and the number of roles per each group (#roles/#group). In the peripheral type, the role descriptors subdivided 1,924 distinct roles into 250 groups, each of which contained 7.7 roles on average. The peripheral type included semantic roles such as place, time, reason, duration. These semantic roles appear in many frames, because they have general meanings that can be shared by different frames. Moreover, the semantic roles of peripheral type originally occurred in only a small number (25.24) of training instances on average. Thus, we infer that the peripheral type generated the largest improvement because semantic roles in this type acquired the greatest benefit from the generalization.", "cite_spans": [], "ref_spans": [ { "start": 3, "end": 10, "text": "Table 1", "ref_id": "TABREF3" }, { "start": 137, "end": 151, "text": "Tables 3 and 4", "ref_id": "TABREF5" }, { "start": 355, "end": 362, "text": "Table 4", "ref_id": "TABREF6" } ], "eq_spans": [], "section": "Analyses of role descriptors", "sec_num": "6.3" }, { "text": "We analyzed the contributions of the FrameNet hierarchy for each type of role-to-role relations and for different depths of grouping. Table 5 shows the micro-averaged F1 scores obtained from various relation types and depths. The Inheritance and Using relations resulted in a slightly better accuracy than the other types. We did not observe any real differences among the remaining five relation types, possibly because there were few se- 2, 4, 6, 8, 10, 12, 14) 90.25 mantic roles associated with these types. We obtained better results by using not only groups for parent roles, but also groups for all ancestors. The best result was obtained by using all relations in the hierarchy. Table 6 reports the precision, recall, and microaveraged F1 scores of semantic roles with respect to each coreness type. 4 In general, semantic roles of the core coreness were easily identified by all of the grouping criteria; even the baseline system obtained an F1 score of 91.93. For identifying semantic roles of the peripheral and extra-thematic types of coreness, the simplest solution, the descriptor criterion, outperformed other criteria. In Table 7 , we categorize feature functions whose weights are in the top 1000 in terms of greatest absolute value. The behaviors of the role groups can be distinguished by the following two characteristics. Groups of role descriptors and semantic types have large weight values for the first word and supersense features, which capture the characteristics of adjunctive phrases. The original roles and hierarchical-relation groups have strong associations with lexical and structural characteristics such as the syntactic path, content word, and head word. Table 7 suggests that role-descriptor groups and semantic-type groups are effective for peripheral or adjunctive roles, and hierarchical relation groups are effective for core roles.", "cite_spans": [ { "start": 440, "end": 442, "text": "2,", "ref_id": null }, { "start": 443, "end": 445, "text": "4,", "ref_id": null }, { "start": 446, "end": 448, "text": "6,", "ref_id": null }, { "start": 449, "end": 451, "text": "8,", "ref_id": null }, { "start": 452, "end": 455, "text": "10,", "ref_id": null }, { "start": 456, "end": 459, "text": "12,", "ref_id": null }, { "start": 460, "end": 463, "text": "14)", "ref_id": null }, { "start": 808, "end": 809, "text": "4", "ref_id": null } ], "ref_spans": [ { "start": 134, "end": 141, "text": "Table 5", "ref_id": "TABREF9" }, { "start": 687, "end": 694, "text": "Table 6", "ref_id": "TABREF11" }, { "start": 1138, "end": 1145, "text": "Table 7", "ref_id": null }, { "start": 1693, "end": 1700, "text": "Table 7", "ref_id": null } ], "eq_spans": [], "section": "Hierarchical relations and relation types", "sec_num": "6.4" }, { "text": "We have described different criteria for generalizing semantic roles in FrameNet. They were: role hierarchy, human-understandable descriptors of roles, semantic types of filler phrases, and mappings from FrameNet roles to thematic roles of VerbNet. We also proposed a feature design that combines and weights these criteria using the training data. The experimental result of the role classification task showed a 19.16% of the error reduction and a 7.42% improvement in the macroaveraged F1 score. In particular, the method we have presented was able to classify roles having few instances. We confirmed that modeling the role generalization at feature level was better than the conventional approach that replaces semantic role labels. Each criterion presented in this paper improved the accuracy of classification. The most successful criterion was the use of human-understandable role descriptors. Unfortunately, the FrameNet hierarchy did not outperform the role descriptors, contrary to our expectations. A future direction of this study would be to analyze the weakness of the FrameNet hierarchy in order to discuss possible improvement of the usage and annotations of features of x class type or hr rl st vn frame 0 4 0 1 0 evoking word 3 4 7 3 0 ew & hw stem 9 34 20 8 0 ew & phrase type 11 7 11 3 1 head word 13 19 8 3 1 hw stem 11 17 8 8 1 content word 7 19 12 3 0 cw stem 11 26 13 5 0 cw PoS 4 5 14 15 2 directed path 19 27 24 6 7 undirected path 21 35 17 2 6 partial path 15 18 16 13 5 last word 15 18 12 3 2 first word 11 23 53 26 10 supersense 7 7 35 25 4 position 4 6 30 9 5 others 27 29 33 19 6 total 188 298 313 152 50 Table 7 : The analysis of the top 1000 feature functions. Each number denotes the number of feature functions categorized in the corresponding cell. Notations for the columns are as follows. 'or': original role, 'hr': hierarchical relation, 'rd': role descriptor, 'st': semantic type, and 'vn': VerbNet thematic role.", "cite_spans": [], "ref_spans": [ { "start": 1188, "end": 1741, "text": "x class type or hr rl st vn frame 0 4 0 1 0 evoking word 3 4 7 3 0 ew & hw stem 9 34 20 8 0 ew & phrase type 11 7 11 3 1 head word 13 19 8 3 1 hw stem 11 17 8 8 1 content word 7 19 12 3 0 cw stem 11 26 13 5 0 cw PoS 4 5 14 15 2 directed path 19 27 24 6 7 undirected path 21 35 17 2 6 partial path 15 18 16 13 5 last word 15 18 12 3 2 first word 11 23 53 26 10 supersense 7 7 35 25 4 position 4 6 30 9 5 others 27 29 33 19 6 total 188 298 313", "ref_id": "TABREF3" }, { "start": 1749, "end": 1756, "text": "Table 7", "ref_id": null } ], "eq_spans": [], "section": "Conclusion", "sec_num": "7" }, { "text": "the hierarchy.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conclusion", "sec_num": "7" }, { "text": "Since we used the latest release of FrameNet in order to use a greater number of hierarchical role-to-role relations, we could not make a direct comparison of performance with that of existing systems; however we may say that the 89.00% F1 micro-average of our baseline system is roughly comparable to the 88.93% value of Bejan and Hathaway (2007) for SemEval-2007 (Baker et al., 2007 . 5 In addition, the methodology presented in this paper applies generally to any SRL resources; we are planning to determine several grouping criteria from existing linguistic resources and to apply the methodology to the PropBank corpus.", "cite_spans": [ { "start": 322, "end": 347, "text": "Bejan and Hathaway (2007)", "ref_id": "BIBREF3" }, { "start": 352, "end": 364, "text": "SemEval-2007", "ref_id": null }, { "start": 365, "end": 384, "text": "(Baker et al., 2007", "ref_id": "BIBREF1" }, { "start": 387, "end": 388, "text": "5", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Conclusion", "sec_num": "7" }, { "text": "In FrameNet, each role is assigned one of four different types of coreness (core, core-unexpressed, peripheral, extrathematic) It represents the conceptual necessity of the roles in the frame to which it belongs.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "We include Core-unexpressed in core, because it has a property of core inside one frame.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "The figures of role descriptors in Tables 4 and 6differ. InTable 4, we measured the performance when we used one or all types of coreness for training. In contrast, inTable 6, we used all types of coreness for training, but computed the performance of semantic roles for each coreness separately.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "There were two participants that performed whole SRL inSemEval-2007. Bejan andHathaway (2007) evaluated role classification accuracy separately for the training data.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null } ], "back_matter": [ { "text": "The authors thank Sebastian Riedel for his useful comments on our work. 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In Proceedings of ACL-08: HLT, pages 550-558.", "links": null } }, "ref_entries": { "FIGREF0": { "type_str": "figure", "text": "A comparison of frames for buy.v defined in PropBank and FrameNet", "num": null, "uris": null }, "FIGREF1": { "type_str": "figure", "text": "An example of role groupings using different criteria.", "num": null, "uris": null }, "FIGREF2": { "type_str": "figure", "text": "An example of input and output of role classification.", "num": null, "uris": null }, "FIGREF3": { "type_str": "figure", "text": "St::State of affairs in which Giving::Reason and Giving::Means are included, as illustrated in Figure 4. Ifc = St::State of affairs, we cannot disambiguate which original role is correct.", "num": null, "uris": null }, "TABREF3": { "type_str": "table", "num": null, "html": null, "text": "The accuracy and error reduction rate of role classification for each type of role group.", "content": "
Feature#instancesPre.Rec.Micro
baseline\u2264 1063.8938.0047.66
\u2264 2069.0151.2658.83
\u2264 5075.8465.8570.50
+ all groups\u2264 1072.5755.8563.12
\u2264 2076.3065.4170.43
\u2264 5080.8674.5977.60
" }, "TABREF4": { "type_str": "table", "num": null, "html": null, "text": "The effect of role groups on the roles with few instances.", "content": "" }, "TABREF5": { "type_str": "table", "num": null, "html": null, "text": "", "content": "
shows the micro-averaged F1 scores of all
" }, "TABREF6": { "type_str": "table", "num": null, "html": null, "text": "The analysis of the numbers of roles, instances, and role-descriptor groups, for each type of coreness.", "content": "
CorenessMicro
Baseline89.00
Core89.51
Peripheral90.12
Extra-thematic89.09
All90.77
" }, "TABREF7": { "type_str": "table", "num": null, "html": null, "text": "The effect of employing role-descriptor groups of each type of coreness.", "content": "" }, "TABREF9": { "type_str": "table", "num": null, "html": null, "text": "Comparison of the accuracy with different types of hierarchical relations.", "content": "
" }, "TABREF11": { "type_str": "table", "num": null, "html": null, "text": "", "content": "
: The precision and recall of each type of
coreness with role groups. Type represents the
type of coreness; c denotes core, p denotes periph-
eral, and e denotes extra-thematic.
" } } } }