| { | |
| "paper_id": "I08-1021", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T07:40:39.719233Z" | |
| }, | |
| "title": "Modeling Context in Scenario Template Creation", | |
| "authors": [ | |
| { | |
| "first": "Long", | |
| "middle": [], | |
| "last": "Qiu", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National University of Singapore Singapore", | |
| "location": { | |
| "postCode": "117590" | |
| } | |
| }, | |
| "email": "qiul@comp.nus.edu.sg" | |
| }, | |
| { | |
| "first": "Min-Yen", | |
| "middle": [], | |
| "last": "Kan", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National University of Singapore Singapore", | |
| "location": { | |
| "postCode": "117590" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Tat-Seng", | |
| "middle": [], | |
| "last": "Chua", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National University of Singapore Singapore", | |
| "location": { | |
| "postCode": "117590" | |
| } | |
| }, | |
| "email": "chuats@comp.nus.edu.sg" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "We describe a graph-based approach to Scenario Template Creation, which is the task of creating a representation of multiple related events, such as reports of different hurricane incidents. We argue that context is valuable to identify important, semantically similar text spans from which template slots could be generalized. To leverage context, we represent the input as a set of graphs where predicate-argument tuples are vertices and their contextual relations are edges. A context-sensitive clustering framework is then applied to obtain meaningful tuple clusters by examining their intrinsic and extrinsic similarities. The clustering framework uses Expectation Maximization to guide the clustering process. Experiments show that: 1) our approach generates high quality clusters, and 2) information extracted from the clusters is adequate to build high coverage templates.", | |
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| "paper_id": "I08-1021", | |
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| "abstract": [ | |
| { | |
| "text": "We describe a graph-based approach to Scenario Template Creation, which is the task of creating a representation of multiple related events, such as reports of different hurricane incidents. We argue that context is valuable to identify important, semantically similar text spans from which template slots could be generalized. To leverage context, we represent the input as a set of graphs where predicate-argument tuples are vertices and their contextual relations are edges. A context-sensitive clustering framework is then applied to obtain meaningful tuple clusters by examining their intrinsic and extrinsic similarities. The clustering framework uses Expectation Maximization to guide the clustering process. Experiments show that: 1) our approach generates high quality clusters, and 2) information extracted from the clusters is adequate to build high coverage templates.", | |
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| "section": "Abstract", | |
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| "text": "Scenario template creation (STC) is the problem of generating a common semantic representation from a set of input articles. For example, given multiple newswire articles on different hurricane incidents, an STC algorithm creates a template that may include slots for the storm's name, current location, direction of travel and magnitude. Slots in such a scenario template are often to be filled by salient entities in the scenario instance (e.g., \"Hurricane Charley\" or \"the coast area\") but some can also be filled by prominent clauses, verbs or adjectives that describe these salient entities. Here, we use the term salient aspect (SA) to refer to any of such slot fillers that people would regard as important to describe a particular scenario. Figure 1 shows such a manuallybuilt scenario template in which details about important actions, actors, time and locations are coded as slots. STC is an important task that has tangible benefits for many downstream applications. In the Message Understanding Conference (MUC), manuallygenerated STs were provided to guide Information Extraction (IE). An ST can also be viewed as regularizing a set of similar articles as a set of attribute/value tuples, enabling multi-document summarization from filled templates.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 749, | |
| "end": 757, | |
| "text": "Figure 1", | |
| "ref_id": "FIGREF0" | |
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| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "Despite these benefits, STC has not received much attention by the community. We believe this is because it is considered a difficult task that requires deep NL understanding of the source articles. A problem in applications requiring semantic similarity is that the same word in different contexts may have different senses and play different roles. Conversely, different words in similar contexts may play similar roles. This problem makes approaches that rely on word similarity alone inadequate.", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "We propose a new approach to STC that incorporates the use of contextual information to address this challenge. Unlike previous approaches that concentrate on the intrinsic similarity of candidate slot fillers, our approach explicitly models contextual evidence. And unlike approaches to word sense disambiguation (WSD) and other semantic analyses that use neighboring or syntactically related words as contextual evidence, we define contexts by semantic relatedness which extends beyond sentence boundaries. Figure 2 illustrates a case in point with two excerpts from severe storm reports. Here, although the intrinsic similarity of the main verbs \"hit\" and \"land\" is low, their contextual similarity is high as both are followed by clauses sharing similar subjects (hurricanes) and the same verbs. Our approach encodes such contextual information as graphs, mapping the STC problem into a general graph overlay problem that is solvable by a variant of Expectation Maximization (EM).", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 509, | |
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| "text": "Figure 2", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "Our work also contributes resources for STC research. Until now, few scenario templates have been publicly available (as part of MUC), rendering any potential evaluation of automated STC statistically insignificant. As part of our study, we have compiled a set of input articles with annotations that we are making available to the research community. ", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "A natural way to automate the process of STC is to cluster similar text spans in the input article set. SAs then emerge through clustering; if a cluster of text spans is large enough, the aspects contained in it will be considered as SAs. Subsequently, these SAs will be generalized into one or more slots in the template, depending on the definition of the text span. Assuming scenarios are mainly defined by actions, the focus should be on finding appropriate clusters for text spans each of which represents an action. Most of the related work (although they may not directly address STC) shares this assumption and performs", | |
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| "sec_num": "2" | |
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| "text": "Charley landed further south on Florida's Gulf coast than predicted, ... The hurricane ... has weakened and is moving over South Carolina.", | |
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| "text": "At least 21 others are missing after the storm hit on Wednesday. .... But Tokage had weakened by the time it passed over Japan's capital, Tokyo, where it left little damage before moving out to sea. Curved lines indicate similar contexts, providing evidence that \"land\" and \"hit\" from two articles are semantically similar.", | |
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| "text": "action clustering accordingly. While the target application varies, most systems that need to group text spans by similarity measures are verb-centric. In addition to the verb, many systems expand their representation by including named entity tags (Collier, 1998; Yangarber et al., 2000; Sudo et al., 2003; Filatova et al., 2006) , as well as restricting matches (using constraints on subtrees (Sudo et al., 2003; Filatova et al., 2006) , predicate argument structures (Collier, 1998; Riloff and Schmelzenbach, 1998; Yangarber et al., 2000; Harabagiu and Maiorano, 2002) or semantic roles).", | |
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| "text": "(Collier, 1998;", | |
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| "text": "Given these representations, systems then cluster similar text spans. To our knowledge, all current systems use a binary notion of similarity, in which pairs of spans are either similar or not. How they determine similarity is tightly coupled with their text span representation. One criterion used is pattern overlap: for example, (Collier, 1998; Harabagiu and Lacatusu, 2005 ) judge text spans to be similar if they have similar verbs and share the same verb arguments. Working with tree structures, Sudo et al. and Filatova et al. instead require shared subtrees.", | |
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| "text": "Calculating text span similarity ultimately boils down to calculating word phrase similarity. Approaches such as Yangarber's or Riloff and Schmelzenbach's do not employ a thesaurus and thus are easier to implement, but can suffer from over-or under-generalization. In certain cases, either the same actor is involved in different actions or different verbs realize the same action. Other systems (Collier, 1998; Sudo et al., 2003) do employ lexical similarity but threshold it to obtain binary judgments. Systems then rank clusters by cluster size and correlation with the relevant article set and equate top clusters as output scenario slots.", | |
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| "text": "Automating STC requires handling a larger degree of variations than most previous work we have surveyed. Note that the actors involved in actions in a scenario generally differ from event to event, which makes most related work on text span similarity calculation unsuitable. Also, action participants are not limited to named entities, so our approach needs to process all NPs. As both actions and actors may be realized using different words, a similarity thesaurus is necessary. Our approach to STC uses a thesaurus based on corpus statistics (Lin, 1998) for real-valued similarity calculation. In contrast to previous approaches, we do not threshold word similarity results; we retain their fractional values and incorporate these values holistically. Finally, as the same action can be realized in different constructions, the semantic (not just syntactic) roles of verb arguments must be considered, lest agent and patient roles be confused. For these reasons, we use a semantic role labeler (Pradhan et al., 2004) to provide and delimit the text spans that contain the semantic arguments of a predicate. We term the obtained text spans as predicate argument tuples (tuples) throughout the paper. The semantic role labeler reportedly achieves an F 1 measure equal to 68.7% on identificationclassification of predicates and core arguments on a newswire text corpus (LDC, 2002) . Within the confines of our study, we find it is able to capture most of the tuples of interest.", | |
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| "end": 557, | |
| "text": "(Lin, 1998)", | |
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| "text": "(Pradhan et al., 2004)", | |
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| "text": "Our approach explicitly captures contextual evidence. We define a tuple's contexts as other tuples in the same article segment where no topic shift occurs. This definition refines the n-surrounding word constraint commonly used in spelling correction (for example, (Hirst and Budanitsky, 2005) ), Word Sense Disambiguation ( (Preiss, 2001) , (Lee and Ng, 2002) , for instance), etc. while still ensures the relatedness between a tuple and its contexts. Specifically, a tuple is contextually related to other tuples by two quantifiable contextual relations: argument-similarity and position-similarity. For our experiments, we use the leads of newswire articles as they normally summarize the news. We also assume a lead qualifies as a single article segment, thus making all of its tuples as potential contexts to each other. Figure 3: Being similar contexts, \"weakened\" and \"moving\" provide contextual evidence that \"land\" and \"hit\" are similar.", | |
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| "end": 339, | |
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| "text": "from A 2 from A 1 weakened(storm) v 2 1 hit(storm) v 2 2 moving(storm) v 2 3 weakened(hurricane) v 1 1 landed(hurricane) v 1 2 moving(hurricane) v 1 3 e 2 1,", | |
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| "text": "First, we split the input article leads into sentences and perform semantic role labeling immediately afterwards. Our system could potentially benefit from additional pre-processing such as co-reference resolution. Currently these pre-processing steps have not been properly integrated with the rest of the system, and thus we have not yet measured their impact.", | |
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| "text": "We then transform each lead A i into a graph", | |
| "cite_spans": [], | |
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| "text": "G i = {V i , E i }. As shown in Figure 3, vertices V i = {v i j }(j = 1, ..., N )", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "Context-Sensitive Clustering (CSC)", | |
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| { | |
| "text": "are the N predicate argument tuples extracted from the ith article, and directed edges", | |
| "cite_spans": [], | |
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| "text": "E i = {e i m,n = (v i m , v i n )} reflect contextual relations between tuple v i m and v i n .", | |
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| "text": "Edges only connect tuples from the same article, i.e., within each graph G i . We differentiate between two types of edges. One is argument-similarity, where the two tuples have semantically similar arguments. This models tuple cohesiveness, where the edge weight is determined by the similarity score of the most similar inter-tuple argument pair. The other is positionsimilarity, represented as the offset of the ending tuple with respect to the other, measured in sentences. This edge type is directional to account for simple causality.", | |
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| "text": "Given this set of graphs, the clustering task is to find an optimal alignment of all graphs (i.e., superimposing the set of article graphs to maximize vertex overlap, constrained by the edges). We adapt Expectation Maximization (Dempster et al., 1977) to find an optimal clustering. This process assigns tuples to suitable clusters where they are semantically similar and share similar contexts with other tuples. Algorithm 1 outlines this alignment process. During initialization, tuples whose pairwise similarity higher than a threshold \u03c4 are merged to form highly cohesive seed clusters. To compute a continuous similarity Sim(t a , t b ) of tuples t a and t b , we use the similarity measure described in (Qiu et al., 2006) , which linearly combines similarities between the semantic roles shared by the two tuples. Some other tuples are related to these seed clusters by argument-similarity. These related tuples are temporarily put into a special \"other\" cluster. The cluster membership of these related tuples, together with those currently in the seed clusters, are to be further adjusted. The \"other\" cluster is so called because a tuple will end up being assigned to it if it is not found to be similar to any other tuple. Tuples that are neither similar to nor contextually related by argument-similarity to another tuple are termed singletons and excluded from being clustered.", | |
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| "text": "(Dempster et al., 1977)", | |
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| "text": "Algorithm 1 Graph Alignment(G) /*G is a set of graph {G i }*/ T \u2190 all tuples in G C \u2190", | |
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| "text": "We then iteratively (re-)estimate clusters of tuples across the set of article graphs G. In the E-step of the EM algorithm, all contextual relations between each pair of clusters are collected as two set of edges.", | |
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| "text": "Here we assume argument-similarity and positionsimilarity are independent and thus we differentiate them in the computation. Accordingly, there are two sets: edges as and edges ps . For simplicity, we assume independent normal distributions for the strength of each set (inter-tuple argument similarity for edges as and sentence distance for edges ps ). The edge strength distribution parameters for both sets between each pair of clusters are re-estimated based on current edges in edges as and edges ps .", | |
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| "text": "In the M-step, we examine each tuple's fitness for belonging to its cluster and relocate some tuples to new clusters to maximize the likelihood given the latest estimated edge strength distributions. In the following equations, we denote the proposition that predicate argument tuple t a belongs to cluster c m as t a \u2208c m ; a typical tuple (the centroid) of the cluster c m as t cm ; and the cluster of t a as c ta . The objective function to maximize is:", | |
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| { | |
| "text": "EQUATION", | |
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| "start": 0, | |
| "end": 8, | |
| "text": "EQUATION", | |
| "ref_id": "EQREF", | |
| "raw_str": "Obj(G) = X ta\u2208G log(P (ta\u2208ct a )),", | |
| "eq_num": "(1)" | |
| } | |
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| "sec_num": "3" | |
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| { | |
| "text": "where P (ta\u2208cm) = 2Ps(ta\u2208cm) Pc(ta\u2208cm) Ps(ta\u2208cm) + Pc(ta\u2208cm) .", | |
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| "section": "Context-Sensitive Clustering (CSC)", | |
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| "text": "Equation 2 takes the harmonic mean of two factors: a contextual factor P c and and a semantic factor P s :", | |
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| "section": "Context-Sensitive Clustering (CSC)", | |
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| { | |
| "text": "EQUATION", | |
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| { | |
| "start": 0, | |
| "end": 8, | |
| "text": "EQUATION", | |
| "ref_id": "EQREF", | |
| "raw_str": "Pc(ta\u2208cm) = max{P (edges(ta, t b )| t b :edges(ta,t b ) =null edges(cm, ct b ))},", | |
| "eq_num": "(3)" | |
| } | |
| ], | |
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| "sec_num": "3" | |
| }, | |
| { | |
| "text": "Ps(ta\u2208cm) = ( sim def ault , cm = c other , Sim(ta, tc m ), otherwise.", | |
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| "section": "Context-Sensitive Clustering (CSC)", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "Here the contextual factor P c models how likely t a belongs to c m according to the contextual information, i.e., the conditional probability of the contextual relations between c m and c t b given the contextual relations between t a and one particular context t b , which maximizes this probability. According to Bayes' theorem, it is computed as shown in Equation 3. In practice, we multiply two conditional probabilities: P (edge as (t a , t b )|edges as (c m , c t b )) and P (edge ps (t a , t b )|edges ps (c m , c t b )), assuming independence between edges as and edges ps . We assume there are still singleton tuples that are not semantically similar to another tuple and should belong to the special \"other\" cluster. Given that they are dissimilar to each other, we set sim def ault to a small nonzero value in Equation 4 to prevent the \"other\" cluster from expelling them based on their low semantic similarity. Tuples' cluster memberships are recalculated, and the parameters describing the contextual relations between clusters are reestimated. New EM iterations are performed as long as one or more tuple relocations occur. Once the EM halts, clusters of equivalent tuples are formed. Among these clusters, some correspond to salient actions that, together with their actors, are all SAs to be generalized into template slots. Cluster size is a good indicator of salience, and each large cluster (excluding the \"other\" cluster) can be viewed as containing instances of a salient action.", | |
| "cite_spans": [], | |
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| "section": "Context-Sensitive Clustering (CSC)", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "Formulating the clustering process as a variant of iterative EM is well-motivated as we consider the similarity scores as noisy and having missing observations. Calculating semantic similarity is at best inaccurate. Thus it is difficult to cluster tuples correctly based only on their semantic similarity. Also to check whether a tuple shares contexts with a cluster of tuples, the cluster has to be relatively clean. An iterative EM as we have proposed naturally improve the cleanness of these tuple clusters gradually as new similarity information comes to light.", | |
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| "section": "Context-Sensitive Clustering (CSC)", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "For STC, we argue that it is crucial to cluster tuples with high recall so that an SA's various surface forms can be captured and the size of clusters can serve as a salience indicator. Meanwhile, precision should not be sacrificed, as more noise will hamper the downstream generalization process which outputs template slots. We conduct experiments designed to answer two relevant research questions: 1) Cluster Quality: Whether using contexts (in CSC) produces better clustering results than ignoring it (in the K-means baseline); and 2) Template Coverage: Whether slots generalized from CSC clusters cover human-defined templates.", | |
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| "section": "Evaluation", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "A straightforward evaluation of a STC system would compare its output against manually-prepared gold standard templates, such as those found in MUC.", | |
| "cite_spans": [], | |
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| "section": "Data Set and Baseline", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "Unfortunately, such scenario templates are severely limited and do not provide enough instances for a proper evaluation. To overcome this problem, we have prepared a balanced news corpus, where we have manually selected articles covering 15 scenarios. Each scenario is represented by a total of 45 to 50 articles which describe 10 different events.", | |
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| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "Our baseline is a standard K-means clusterer. Its input is identical to that of CSC -the tuples extracted from relevant news articles and are not excluded from being clustered by CSC in the initialization stage (refer to Section 3) -and employs the same tuple similarity measure (Qiu et al., 2006) . The differentiating factor between CSC and K-means is the use of contextual evidence. A standard K-means clusterer requires a k to be specified. For each scenario, we set its k as the number of clusters generated by CSC for direct comparison.", | |
| "cite_spans": [ | |
| { | |
| "start": 279, | |
| "end": 297, | |
| "text": "(Qiu et al., 2006)", | |
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| "section": "Data Set and Baseline", | |
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| }, | |
| { | |
| "text": "We fix the test set for each scenario as ten randomly selected news articles, each reporting a different instance of the scenario; the development set (which also serves as the training set for determining the EM initialization threshold \u03c4 and sim def ault in Equation 4) is a set of ten articles from the \"Air-linerCrash\" scenario, which are excluded from the test set. Both systems analyze the first 15 sentences of each article, and sentences generate 2 to 3 predicate argument tuples on average, resulting in a total of 10 \u00d7 15 \u00d7 (2 to 3) = 300 to 450 tuples for each scenario.", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "Data Set and Baseline", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "This experiment compares the clustering results of CSC and K-means. We use the standard clustering metrics of purity and inverse purity (Hotho et al., 2003) . The first author manually constructed the gold standard clusters for each scenario using a GUI before conducting any experiments. A special cluster, corresponding to the \"other\" cluster in the CSC clusters, was created to hold the singleton tuples for each scenario. Table 1 shows this under the column \"#Gold Standard Clusters\".", | |
| "cite_spans": [ | |
| { | |
| "start": 136, | |
| "end": 156, | |
| "text": "(Hotho et al., 2003)", | |
| "ref_id": "BIBREF6" | |
| } | |
| ], | |
| "ref_spans": [ | |
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| "start": 426, | |
| "end": 433, | |
| "text": "Table 1", | |
| "ref_id": null | |
| } | |
| ], | |
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| "section": "Cluster Quality", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "Using the manual clusters as the gold standard, we obtain the purity (P) and inverse purity (IP) scores of CSC and K-means on each scenario. In Table 1 , we see that CSC outperforms K-means on 10 of 15 scenarios for both P and IP. For the remaining 5 scenarios, where CSC and K-means have comparable P scores, the IP scores of CSC are all significantly higher than that of K-means. This suggests clusters tend to be split apart more in K-means than in CSC when they have similar purity. One thing worth mentioning here is that the \"other\" cluster normally is relatively large for each scenario, and thus may skew the results. To remove this effect, we excluded tuples belonging to the CSC \"other\" cluster from the K-means input, generating one fewer cluster. Running the evaluation again, the resulting P-IP scores again show that CSC outperforms the baseline Kmeans. We only report the results for all tuples in our paper for simplicity. Table 1 : CSC outperforms K-means with respect to the purity (P) and inverse purity (IP) scores.", | |
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| "ref_spans": [ | |
| { | |
| "start": 144, | |
| "end": 151, | |
| "text": "Table 1", | |
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| "section": "Cluster Quality", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "A close inspection of the results reveals some problematic cases. One issue worth mentioning is that for certain actions both CSC and K-means produce split clusters. In the CSC case, we traced this problem back to the thesaurus, where predicates for one action seem to belong to two or more totally dissimilar semantic categories. The corresponding tuples are thus assigned to different clusters as their low semantic similarity forces the tuples to remain separate, despite the shared contexts trying to join them. One example is \"blast (off)\" and \"lift (off)\" in the \"Launch Event\" scenario. The thesaurus shows the two verbs are dissimilar and the corresponding tuples end up being in two split clusters. This can not be solved easily without an improved thesaurus. We are considering adding a prior to model the op-timal size for clusters, which may help to compact such cases.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Cluster Quality", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "We also assess how well the resulting, CSCgenerated tuple clusters serve in creating good scenario template slots. We start from the top largest clusters from each scenario, and decompose each of them into six sets: the predicates, agents, patients, predicate modifiers, agent modifiers and patient modifiers. For each of the first three sets for each cluster, we create a generalized term to represent it using an extended version of a generalization algorithm (Tseng et al., 2006) . These terms are deemed output slots, and are put into the template with their agent-predicate-patient relations preserved. The size of the template may increase when more clusters are generalized, as new slots may result.", | |
| "cite_spans": [ | |
| { | |
| "start": 462, | |
| "end": 482, | |
| "text": "(Tseng et al., 2006)", | |
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| } | |
| ], | |
| "ref_spans": [], | |
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| "section": "Template Coverage", | |
| "sec_num": "4.3" | |
| }, | |
| { | |
| "text": "We manually compare the slots that are output from the system with those defined in existing scenario templates in MUC. The results here are only indicative and not conclusive, as there are only two MUC7 templates available for comparison: Aviation Disaster and Launch Event. shows an excerpt of the automatically generated template \"AviationDisaster\" (\"Airliner-Crash\" in our corpus) where the semantic roles in the top two biggest clusters have been generalized. Their modifiers are quite semantically diverse, as shown in Table 2 . Thus, generalization (probably after a categorization operation) remains as a challenging problem.", | |
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| "ref_spans": [ | |
| { | |
| "start": 525, | |
| "end": 532, | |
| "text": "Table 2", | |
| "ref_id": "TABREF2" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Template Coverage", | |
| "sec_num": "4.3" | |
| }, | |
| { | |
| "text": "Nonetheless, the information contained in these semantic roles and their modifiers covers human-semantic role modifier head samples agent:aircraft A, U.N., The, Swiss, Canadianbuilt, AN, China, military, Iranian, Air, refueling, US, ... action:crash Siberia, mountain, rain, Tuesday, flight, Sharjah, flames, Sunday, board, Saturday, 225 , Rockaway, approach, United, mountain, hillside patient:people all, 255, 71 Figure 5 : MUC-7 template coverage: asterisks marking all the slots that could be automatically generated.", | |
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| "start": 183, | |
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| "text": "AN,", | |
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| "text": "China,", | |
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| "start": 194, | |
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| "start": 233, | |
| "end": 258, | |
| "text": "... action:crash Siberia,", | |
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| "start": 259, | |
| "end": 268, | |
| "text": "mountain,", | |
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| "start": 269, | |
| "end": 274, | |
| "text": "rain,", | |
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| "text": "Tuesday,", | |
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| "start": 284, | |
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| "text": "flight,", | |
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| "end": 300, | |
| "text": "Sharjah,", | |
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| "start": 301, | |
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| "text": "flames,", | |
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| "start": 309, | |
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| "text": "Sunday,", | |
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| "start": 317, | |
| "end": 323, | |
| "text": "board,", | |
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| "text": "Saturday,", | |
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| "text": "225", | |
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| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 415, | |
| "end": 423, | |
| "text": "Figure 5", | |
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| ], | |
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| "section": "Template Coverage", | |
| "sec_num": "4.3" | |
| }, | |
| { | |
| "text": "defined scenario templates quite well. The two MUC7 templates are shown as a list of slots in Figure 5 , where horizontal lines delimit slots about different semantic roles, and asterisks mark all the slots that could be automatically generated by our system once it has an improved generalizer. We can see substantial amount of overlap, indicating that a STC system powered by CSC is able to capture scenarios' important facts.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 94, | |
| "end": 102, | |
| "text": "Figure 5", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Template Coverage", | |
| "sec_num": "4.3" | |
| }, | |
| { | |
| "text": "We have introduced a new context-sensitive approach to the scenario template creation (STC) problem. Our method leverages deep NL processing, using semantic role labeler's structured semantic tuples as input. Despite the use of deeper semantics, we believe that intrinsic semantic similarity by itself is not sufficient for clustering. We have shown this through examples and argue that an approach that considers contextual similarity is necessary. A key aspect of our work is the incorporation of such contextual information. Our approach uses a notion of context that combines two aspects: positional similarity (when two tuples are adjacent in the text), and argument similarity (when they have similar arguments). The set of relevant articles are represented as graphs where contextual evidence is encoded. By mapping our problem into a graphical formalism, we cast the STC clustering problem as one of multiple graph alignment. Such a graph alignment is solved by an adaptation of EM, which handles contexts and real-valued similarity by treating both as noisy and potentially unreliable observations.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "Conclusion", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "While scenario template creation (STC) is a difficult problem, its evaluation is arguably more difficult due to the dearth of suitable resources. We have compiled and released a corpus of over 700 newswire articles that describe different instances of 15 scenarios, as a suitable input dataset for further STC research. Using this dataset, we have evaluated and analyzed our context-sensitive approach. While our results are indicative, they show that considering contextual evidence improves performance.", | |
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| "section": "Conclusion", | |
| "sec_num": "5" | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "The authors are grateful to Kathleen R. McKeown and Elena Filatova at Columbia University for their stimulating discussions and comments over different stages of the preparation of this paper.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "Acknowledgments", | |
| "sec_num": null | |
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| "FIGREF0": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "An example scenario template (filled).", | |
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| }, | |
| "FIGREF1": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "Contextual evidence of similarity.", | |
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| "FIGREF2": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "highly cohesive tuples clusters other \u2190 remaining tuples semantically connected with C C[C.length] \u2190 other repeat /*E step*/ for each i such that i < C.length do for each j such that j < C.length do if i == j then continue; re-estimate parameters[C[i], C[j]] /*distribution parameters of edges between two clusters*/ tupleReassigned = f alse /*reset*/ /*M step*/ for each i such that i < T.length do aBestLikelihood = T [i].likelihood; /*likelihood of being in its current cluster*/ for each tuple tcontxt that contextually related with T [i] do for each cluster c cand , any candidate cluster that contextually related with tcontxt.cluster do P (T [i] \u2208 c cand ) = comb(Ps, Pc) likelihood = log(P (T [i] \u2208 c cand )) if likelihood > aBestLikelihood then aBestLikelihood = likelihood T [i].cluster = c cand tupleReassigned = true until tupleReassigned == f alse /*alignment stable*/ return", | |
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| }, | |
| "FIGREF3": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "Figure 4: Automated scenario template of \"Avia-tionDisaster\".", | |
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| }, | |
| "FIGREF4": { | |
| "uris": null, | |
| "type_str": "figure", | |
| "text": "Figure 4 shows an excerpt of the automatically generated template \"AviationDisaster\" (\"Airliner-Crash\" in our corpus) where the semantic roles in the top two biggest clusters have been generalized. Their modifiers are quite semantically diverse, as shown in Table 2. Thus, generalization (probably after a categorization operation) remains as a challenging problem. Nonetheless, the information contained in these semantic roles and their modifiers covers human-", | |
| "num": null | |
| }, | |
| "TABREF0": { | |
| "num": null, | |
| "html": null, | |
| "content": "<table><tr><td>Storm Name Charley Storm Action landed Location Florida's Gulf coast Time Friday at 1950GMT Speed 145 mph Victim Category 1 13 people Action died Victim Category 2 over one million Action affected</td></tr></table>", | |
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| "TABREF2": { | |
| "num": null, | |
| "html": null, | |
| "content": "<table><tr><td>AviationDisaster * AIRCRAFT * AIRLINE DEPARTURE POINT DEPARTURE DATE * AIRCRAFT TYPE * CRASH DATE * CRASH SITE CAUSE INFO * VICTIMS NUM</td><td>LaunchEvent * VEHICLE * VEHICLE TYPE * VEHICLE OWNER * PAYLOAD PAYLOAD TYPE PAYLOAD FUNC * PAYLOAD OWNER PAYLOAD ORIGIN * LAUNCH DATE * LAUNCH SITE MISSION TYPE MISSION FUNCTION MISSION STATUS</td></tr></table>", | |
| "type_str": "table", | |
| "text": "Sample automatically detected modifier heads of different semantic roles." | |
| } | |
| } | |
| } | |
| } |