Developing a knowledge base for preposition sense disambiguation: A view from Role and Reference Grammar and FunGramKB

Prepositions represent a grammatical category of frequent use in many European languages. The combination of their semantics with other lexical categories usually makes them difficult to be computationally tractable. As far as natural language processing is concerned, some studies have contributed to make progress on the usage of prepositions. However, there still exists a need to develop a model that allows tackling the problems which result from the disambiguation of prepositional semantics. The goal of this paper is to describe a lexico-conceptual model which can store the knowledge required to disambiguate predicate prepositions, as well as how this model can be exploited by a parser to extract the semantic representation of a text. The theoretical foundation of this approach, which is grounded on the premises of Role and Reference Grammar and FunGramKB, is illustrated with temporal adjuncts expressed by prepositional phrases in English.Financial support for this research has been provided by the DGI, Spanish Ministry of Education and Science, grant FFI2011-29798-C02-01. Moreover, much of this work has resulted from the first author's ongoing PhD thesis "La desambiguacion semantica de los sintagmas prepositivos como adjuntos perifericos en el marco de la Gramatica del Papel y la Referencia: un enfoque desde la linguistica computacional y la ingenieria del conocimiento", to be presented in Universidad Nacional de Educacion a Distancia (UNED).Hernández-Pastor, D.; Periñán Pascual, JC. (2016). Developing a knowledge base for preposition sense disambiguation: A view from Role and Reference Grammar and FunGramKB. Onomázein : Revista de Lingüística, Filología y Traducción. 33:251-288. https://doi.org/10.7764/onomazein.33.16S2512883

Diagnosing Reading strategies: Paraphrase Recognition

Paraphrase recognition is a form of natural language processing used in tutoring, question answering, and information retrieval systems. The context of the present work is an automated reading strategy trainer called iSTART (Interactive Strategy Trainer for Active Reading and Thinking). The ability to recognize the use of paraphrase—a complete, partial, or inaccurate paraphrase; with or without extra information—in the student\u27s input is essential if the trainer is to give appropriate feedback. I analyzed the most common patterns of paraphrase and developed a means of representing the semantic structure of sentences. Paraphrases are recognized by transforming sentences into this representation and comparing them. To construct a precise semantic representation, it is important to understand the meaning of prepositions. Adding preposition disambiguation to the original system improved its accuracy by 20%. The preposition sense disambiguation module itself achieves about 80% accuracy for the top 10 most frequently used prepositions. The main contributions of this work to the research community are the preposition classification and generalized preposition disambiguation processes, which are integrated into the paraphrase recognition system and are shown to be quite effective. The recognition model also forms a significant part of this contribution. The present effort includes the modeling of the paraphrase recognition process, featuring the Syntactic-Semantic Graph as a sentence representation, the implementation of a significant portion of this design demonstrating its effectiveness, the modeling of an effective preposition classification based on prepositional usage, the design of the generalized preposition disambiguation module, and the integration of the preposition disambiguation module into the paraphrase recognition system so as to gain significant improvement

Prepositional Phrase Attachment Disambiguation Using WordNet

In this thesis we use a knowledge-based approach to disambiguating prepositional phrase attachments in English sentences. This method was first introduced by S. M. Harabagiu. The Penn Treebank corpus is used as the training text. We extract 4-tuples of the form VP, NP1, Prep, NP2 and sort them into classes according to the semantic relationships between parts of each tuple. These relationships are extracted from WordNet. Classes are sorted into different tiers based on the strictness of their semantic relationship. Disambiguation of prepositional phrase attachments can be cast as a constraint satisfaction problem, where the tiers of extracted classes act as the constraints. Satisfaction is achieved when the strictest possible tier unanimously indicates one kind of attachment. The most challenging kind of problems for disambiguation of prepositional phrases are ones where the prepositional phrase may attach to either the closest verb or noun. We first demonstrate that the best approach to extracting tuples from parsed texts is a top-down postorder traversal algorithm. Following that, the various challenges in forming the prepositional classes utilizing WordNet semantic relations are described. We then discuss the actions that need to be taken towards applying the prepositional classes to the disambiguation task. A novel application of this method is also discussed, by which the tuples to be disambiguated are also expanded via WordNet, thus introducing a client-side application of the algorithms utilized to build prepositional classes. Finally, we present results of different variants of our disambiguating algorithm, contrasting the precision and recall of various combinations of constraints, and comparing our algorithm to a baseline method that falls back to attaching a prepositional phrase to the closest left phrase. Our conclusion is that our algorithm provides improved performance compared to the baseline and is therefore a useful new method of performing knowledge-based disambiguation of prepositional phrase attachments

An Application of WordNet to Prepositional Attachment

This paper presents a method for word sense disambiguation and coherence understanding of prepositional relations. The method relies on information provided by WordNet 1.5. We first classify prepositional attachments according to semantic equivalence of phrase heads and then apply inferential heuristics for understanding the validity of prepositional structures. 1 Problem description In this paper, we address the problem of disambiguation and understanding prepositional attachment. The arguments of prepositional relations are automatically categorized into semantically equivalent classes of WordNet (Miller and Teibel, 1991) concepts. Then by applying inferential heuristics on each class, we establish semantic connections between arguments that explain the validity of that prepositional structure. The method uses information provided by WordNet, such as semantic relations and textual glosses. We have collected prepositional relations from the Wall Street Journal tagged articles of the..