A Knowledge-Intensive Model for Prepositional Phrase Attachment

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pages 365–375, Beijing, China, July 26-31, 2015. c⃝2015 Association for Computational Linguistics A Knowledge-Intensive Model for Prepositional Phrase Attachment Ndapandula Nakashole Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA, 15213 ndapa@cs.cmu.edu Tom M. Mitchell Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA, 15213 tom.mitchell@cs.cmu.edu Abstract Prepositional phrases (PPs) express cru- cial information that knowledge base con- struction methods need to extract. How- ever, PPs are a major source of syntactic ambiguity and still pose problems in pars- ing. We present a method for resolving ambiguities arising from PPs, making ex- tensive use of semantic knowledge from various resources. As training data, we use both labeled and unlabeled data, utilizing an expectation maximization algorithm for parameter estimation. Experiments show that our method yields improvements over existing methods including a state of the art dependency parser. 1 Introduction Machine reading and information extraction (IE) projects have produced large resources with many millions of facts (Suchanek et al., 2007; Mitchell et al., 2015). This wealth of knowledge creates a positive feedback loop for automatic knowledge base construction efforts: the accumulated knowl- edge can be leveraged to improve machine read- ing; in turn, improved reading methods can be used to better extract knowledge expressed using complex and potentially ambiguous language. For example, prepositional phrases (PPs) express cru- cial information that IE methods need to extract. However, PPs are a major source of syntactic am- biguity. In this paper, we propose to use semantic knowledge to improve PP attachment disambigua- tion. PPs such as “in”, “at”, and “for” express de- tails about the where, when, and why of relations and events. PPs also state attributes of nouns. As an example, consider the following sen- tences: S1.) Alice caught the butterfly with the spots. S2.) Alice caught the butterfly with the net. S NP VP VP NP Alice caught butterfly PP with spots S1.) Noun attachment S NP VP VP NP PP Alice caught butterfly with net S2.) Verb attachment Figure 1: Parse trees where the prepositional phrase (PP) attaches to the noun, and to the verb. Relations Noun-Noun binary relations (Paris, located in, France) (net, caught, butterfly) Nouns Noun semantic categories (butterfly, isA, animal) Verbs Verb roles caught(agent, patient, instrument) Prepositions Preposition definitions f(for)= used for, has purpose, ... f(with)= has, contains, ... Discourse Context n0 ∈ {n0, v, n1, p, n2} Table 1: Types of background knowledge used in this paper to determine PP attachment. S1 and S2 are syntactically different, this is evi- dent from their corresponding parse trees in Fig- ure 1. Specifically, S1 and S2 differ in where their PPs attach. In S1, the butterfly has spots and there- fore the PP, “with the spots”, attaches to the noun. For relation extraction, we obtain a binary relation of the form: ⟨Alice⟩ caught ⟨butterfly with spots⟩. However, in S2, the net is the instrument used for catching and therefore the …

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