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

Aprendizaje basado en datos y enseñanza de preposiciones en ESL: un estudio de estudiantes árabes

This paper explores the effectiveness of data driven learning approach in teaching prepositions to Arab learners. English prepositions have been reported to be a typical feature to learn by the Arab ESL learners. Moreover, intermediate or advance level learners have also reported their difficulties in the usage of English prepositions; reasons might be perceptual and cultural differences bet the two language communities. Teacher trained materials prepared from online resources along with TPP corpus, the aim of the present study was to compare DDL and traditional prescriptive rule based method of grammar teaching. Two distinct classes; experimental (n = 41) and control (n = 19) groups were formed who were study English major. Standardized test was conducted as a pre-test to ensure participants equal level of proficiency in using prepositions. During the study duration for a semester, experimental group was exposed to teacher-prepared materials along with TPP to see prepositions in context. Moreover they were asked to explore further through the prepared materials to reinforce their learning from the data. A post-test was administered at the end of the course. The results showed that the experimental group outperformed the control group. It was found that learners' active role to direct their own learning process caused this encouraging outcome and better learning experience for the learners.Este documento explora la efectividad del enfoque de aprendizaje basado en datos en la enseñanza de preposiciones para estudiantes árabes. Se ha informado que las preposiciones en inglés son un rasgo característico que los estudiantes árabes de ESL deben aprender. Además, los estudiantes de nivel intermedio o avanzado también han informado sobre sus dificultades en el uso de preposiciones en inglés; Las razones pueden ser las diferencias perceptivas y culturales entre las dos comunidades lingüísticas. El objetivo del presente estudio fue comparar sustancias formadas por docentes preparadas a partir de recursos en línea junto con el corpus de TPP, para comparar el método de enseñanza de gramática basado en reglas prescriptivas tradicionales y DDL. Dos clases distintas; Se formaron grupos experimentales (n = 41) y de control (n = 19) que estudiaban inglés principal. La prueba estandarizada se realizó como una prueba previa para garantizar a los participantes el mismo nivel de competencia en el uso de preposiciones. Durante la duración del estudio durante un semestre, el grupo experimental fue expuesto a materiales preparados por el profesor junto con TPP para ver las preposiciones en contexto. Además, se les pidió que exploraran más a través de los materiales preparados para reforzar su aprendizaje a partir de los datos. Se administró una prueba posterior al final del curso. Los resultados mostraron que el grupo experimental superó al grupo de control. Se encontró que el rol activo de los estudiantes para dirigir su propio proceso de aprendizaje causó este resultado alentador y una mejor experiencia de aprendizaje para los estudiantes.Universidad Pablo de Olavid

CATENA: CAusal and Temporal relation Extraction from NAtural language texts

We present CATENA, a sieve-based system to perform temporal and causal relation extraction and classification from English texts, exploiting the interaction between the temporal and the causal model. We evaluate the performance of each sieve, showing that the rule-based, the machinelearned and the reasoning components all contribute to achieving state-of-the-art performance on TempEval-3 and TimeBank-Dense data. Although causal relations are much sparser than temporal ones, the architecture and the selected features are mostly suitable to serve both tasks. The effects of the interaction between the temporal and the causal components, although limited, yield promising results and confirm the tight connection between the temporal and the causal dimension of texts

Extracting Temporal and Causal Relations between Events

Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events. In this thesis we present a framework for an integrated temporal and causal relation extraction system. We first develop a robust extraction component for each type of relations, i.e. temporal order and causality. We then combine the two extraction components into an integrated relation extraction system, CATENA---CAusal and Temporal relation Extraction from NAtural language texts---, by utilizing the presumption about event precedence in causality, that causing events must happened BEFORE resulting events. Several resources and techniques to improve our relation extraction systems are also discussed, including word embeddings and training data expansion. Finally, we report our adaptation efforts of temporal information processing for languages other than English, namely Italian and Indonesian.Comment: PhD Thesi

Real-Time Event Analysis and Spatial Information Extraction From Text Using Social Media Data

Since the advent of websites that enable users to participate and interact with each other by sharing content in different forms, a plethora of possibly relevant information is at scientists\u27 fingertips. Consequently, this thesis elaborates on two distinct approaches to extract valuable information from social media data and sketches out the potential joint use case in the domain of natural disasters

Pattern Dictionary of English Prepositions

Abstract We present a new lexical resource for the study of preposition behavior, the Pattern Dictionary of English Prepositions (PDEP). This dictionary, which follows principles laid out in Hanks' theory of norms and exploitations, is linked to 81,509 sentences for 304 prepositions, which have been made available under The Preposition Project (TPP). Notably, 47,285 sentences, initially untagged, provide a representative sample of preposition use, unlike the tagged sentences used in previous studies. Each sentence has been parsed with a dependency parser and our system has near-instantaneous access to features developed with this parser to explore and annotate properties of individual senses. The features make extensive use of WordNet. We have extended feature exploration to include lookup of FrameNet lexical units an

Disambiguating Spatial Prepositions Using Deep Convolutional Networks

We address the coarse-grained disambiguation of the spatial prepositions as the first step towards spatial role labeling using deep learning models. We propose a hybrid feature of word embeddings and linguistic features, and compare its performance against a set of linguistic features, pre-trained word embeddings, and corpus-trained embeddings using seven classical machine learning classifiers and two deep learning models. We also compile a dataset of 43,129 sample sentences from Pattern Dictionary of English Prepositions (PDEP). The comprehensive experimental results suggest that the combination of the hybrid feature and a convolutional neural network outperforms state-of-the-art methods and reaches the accuracy of 94.21% and F1-score of 0.9398