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Based on these similarity estimates we construct a graph whose vertices represent argument instances and whose edges express similarities between these instances. The graphs consist of multiple edge layers, each capturing one particular type of argument-instance similarity. For example, one layer will be used to represent whether argument instances occur in the same frame, and another layer will represent whether two arguments have a similar head word, and so on. Given this graph representation of the data, we formalize role induction as the problem of partitioning the graph into clusters of similar vertices. We present two algorithms for partitioning multi-layer graphs, which are adaptations of standard graph partitioning algorithms to the multi-layer setting. The algorithms differ in the way they exploit the similarity information encoded in the graph. The first one is based on agglomeration, where two clusters containing similar instances are grouped into a larger cluster. The second one is based on propagation, where role-label information is transferred from one cluster to another based on their similarity.
To understand how the aforementioned principles might allow us to handle the ambiguity stemming from alternate linkings, consider again Example (1). The most important thing to note is that, whereas the subject position is ambiguous with respect to the semantic roles it can express (it can be A0, A1, or A2), we can resolve the ambiguity by exploiting overt syntactic cues of the underlying linking. For example, the predicate break is transitive in sentences (1a) and (1b), and intransitive in sentence (1c). Thus, by taking into account the argument's syntactic position and the predicate's transitivity, we can guess that the semantic role expressed by the subject in sentence (1c) is different from the roles expressed by the subjects in sentences (1a,b). Now consider the more difficult case of distinguishing between the subjects in sentences (1a) and (1b). One linking cue that could help here is the prepositional phrase in sentence (1a), which results in a syntactic frame different from sentence (1b). Were the prepositional phrase omitted, we would attempt to disambiguate the linkings by resorting to lexical-semantic cues (e.g., by taking into account whether the subject is animate). In sum, if we encode sufficiently many linking cues, then the resulting fine-grained syntactic information will discriminate ambiguous semantic roles. In cases where syntactic cues are not discerning enough, we can exploit lexical information and group arguments together based on their lexical content.
The syntactic position of an argument is directly given by the parse tree and can be encoded, for example, by the full path from predicate to argument head, or for practical purposes, in order to reduce sparsity, simply through the relation governing the argument head and its linear position relative to the predicate (left or right). In contrast, linkings are not directly observed, but we can resort to overt syntactic cues as a proxy. Examples include the verb's voice (active/passive), whether it is transitive, the part-of-speech of the subject, and so on. We argue that in principle, if sufficiently many cues are taken into account, they will capture one particular linking, although there may be several encodings for the same linking. Note that syntactic similarity is not used to construct another graph layer; rather, it will be used for deriving initial clusters of instances, as we explain in Section 4.1.
This motivates a baseline that directly assigns instances to clusters according to their syntactic position. The pseudo-code is given in Algorithm 4. For each verb we allocate N = 22 clusters (the maximal number of gold standard clusters together with a default cluster). Apart from the default cluster, each cluster is associated with a syntactic position and all instances occurring in that position are mapped into the cluster. Despite being relatively simple, this baseline has been previously used as a point of comparison by other unsupervised semantic role labeling systems (Grenager and Manning 2006; Lang and Lapata 2010) and shown difficult to outperform. This is partly due to the fact that almost two thirds of the PropBank arguments are either A0 or A1. Identifying these two roles correctly is therefore the most important distinction to make, and because this can be largely achieved on the basis of the arguments' syntactic position (see Table 2), the baseline yields high scores.
The syntactic structure of a sentence is represented through a constituent tree whose terminal nodes are tokens and non-terminal nodes are phrases (see Figure 6). In addition to labeling each node with a constituent type such as Sentence, Noun Phrase, and Verb Phrase, the edges between a parent and a child node are labeled according to the function of the child within the parent constituent, for example, Accusative Object, Noun Kernel, or Head. Edges can cross, allowing local and non-local dependencies to be encoded in a uniform way and eliminating the need for traces. This approach has significant advantages for non-configurational languages such as German, which exhibit a rich inventory of discontinuous constituents and considerable freedom with respect to word order (Smith 2003). Compared with the Penn TreeBank (Marcus, Santorini, and Marcinkiewicz 1993), tree structures are relatively flat. For example, the tree does not encode whether a constituent is a verbal argument or adjunct; this information is encoded through the edge labels instead. 2b1af7f3a8