Recognizing Textual Entailment Using Description Logic And Semantic Relatedness

Textual entailment (TE) is a relation that holds between two pieces of text where one reading the first piece can conclude that the second is most likely true. Accurate approaches for textual entailment can be beneficial to various natural language processing (NLP) applications such as: question answering, information extraction, summarization, and even machine translation. For this reason, research on textual entailment has attracted a significant amount of attention in recent years. A robust logical-based meaning representation of text is very hard to build, therefore the majority of textual entailment approaches rely on syntactic methods or shallow semantic alternatives. In addition, approaches that do use a logical-based meaning representation, require a large knowledge base of axioms and inference rules that are rarely available. The goal of this thesis is to design an efficient description logic based approach for recognizing textual entailment that uses semantic relatedness information as an alternative to large knowledge base of axioms and inference rules. In this thesis, we propose a description logic and semantic relatedness approach to textual entailment, where the type of semantic relatedness axioms employed in aligning the description logic representations are used as indicators of textual entailment. In our approach, the text and the hypothesis are first represented in description logic. The representations are enriched with additional semantic knowledge acquired by using the web as a corpus. The hypothesis is then merged into the text representation by learning semantic relatedness axioms on demand and a reasoner is then used to reason over the aligned representation. Finally, the types of axioms employed by the reasoner are used to learn if the text entails the hypothesis or not. To validate our approach we have implemented an RTE system named AORTE, and evaluated its performance on recognizing textual entailment using the fourth recognizing textual entailment challenge. Our approach achieved an accuracy of 68.8 on the two way task and 61.6 on the three way task which ranked the approach as 2nd when compared to the other participating runs in the same challenge. These results show that our description logical based approach can effectively be used to recognize textual entailment

Towards a Gold Standard Corpus for Variable Detection and Linking in Social Science Publications

In this paper, we describe our effort to create a new corpus for the evaluation of detecting and linking so-called survey variables in social science publications (e.g., "Do you believe in Heaven?"). The task is to recognize survey variable mentions in a given text, disambiguate them, and link them to the corresponding variable within a knowledge base. Since there are generally hundreds of candidates to link to and due to the wide variety of forms they can take, this is a challenging task within NLP. The contribution of our work is the first gold standard corpus for the variable detection and linking task. We describe the annotation guidelines and the annotation process. The produced corpus is multilingual - German and English - and includes manually curated word and phrase alignments. Moreover, it includes text samples that could not be assigned to any variables, denoted as negative examples. Based on the new dataset, we conduct an evaluation of several state-of-the-art text classification and textual similarity methods. The annotated corpus is made available along with an open-source baseline system for variable mention identification and linking

Data distribution satellite

A description is given of a data distribution satellite (DDS) system. The DDS would operate in conjunction with the tracking and data relay satellite system to give ground-based users real time, two-way access to instruments in space and space-gathered data. The scope of work includes the following: (1) user requirements are derived; (2) communication scenarios are synthesized; (3) system design constraints and projected technology availability are identified; (4) DDS communications payload configuration is derived, and the satellite is designed; (5) requirements for earth terminals and network control are given; (6) system costs are estimated, both life cycle costs and user fees; and (7) technology developments are recommended, and a technology development plan is given. The most important results obtained are as follows: (1) a satellite designed for launch in 2007 is feasible and has 10 Gb/s capacity, 5.5 kW power, and 2000 kg mass; (2) DDS features include on-board baseband switching, use of Ku- and Ka-bands, multiple optical intersatellite links; and (3) system user costs are competitive with projected terrestrial communication costs

Recherche d'information précise dans des sources d'information structurées et non structurées: défis, approches et hybridation.

National audienceCet article propose une synthèse d'une part sur les approches développées en questions-réponses (QR) sur du texte, en insistant plus particulièrement sur les modèles exploitant des représentations structurées des textes, et d'autre part sur les approches récentes en QR sur des bases de connaissances. Notre objectif est de montrer les problématiques communes et le rapprochement possible de ces deux types de recherche de réponses en prenant appui sur la reconnaissance des relations présentes dans les énoncés textuels et dans les bases de connaissances. Nous présentons les quelques travaux relevant de ce type d'approche afin de mettre en perspective les questions ouvertes pour aller vers des systèmes réellement hybrides ancrés sur des représentations sémantiques

Human impact on the Cape of Good Hope Nature Reserve

The problem investigated in this study is the environmental effect of outdoor recreation on a valuable conservation area, the Cape of Good Hope nature reserve. The approach adopted views the reserve as a business concern that produces service commodities from the resources of the natural environment. Supply of these commodities was estimated from a visitor activity profile obtained by combining traffic count data with timed observations on visitor behaviour. Demand was assessed from the results of a visitor survey and from information obtained from a literature review. The results of these investigations provided a data base for formulating a business management policy for the reserve. The findings of the study were that the shortage of open space in Cape Town and the Western Cape is a human ecological problem and that a business management policy which reinforces human behavioural links with the environment would be both an economic solution and an eco- logical solution to the current controversy surrounding matters related to conservation in the Cape of Good Hope nature reserve

Data-Driven Natural Language Inference

Natural Language Inference (NLI) research involves the development of models that can mimic human inference processes based on natural language and classify the inference relation between sentences. For example, given the premise that ``In 2019, the Raptors won their first Eastern Conference title, and the team's first NBA Finals", it follows that ``The Raptors beat another team in the 2019 NBA Finals". but it does not follow that ``The Golden State Warriors won the last game of the NBA Finals in 2019".The goal of NLI is to build machines that can take pairs of premise and hypothesis as input and correctly predict the inference relation between them, reverse-engineering the inference process of a human. NLI is a fundamental task with a simple and generic formalization such that NLI models can be practically useful in all kinds of NLP applications. In recent years, there has been emerging interest and research in data-driven natural language inference.This thesis starts with several key applications of data-driven NLI modules, including sentence-based NLI modeling, how to effectively use the NLI model as a key natural language understanding (NLU) module in both an automatic fact-checking system for claim verification and in an open-domain dialogue system for improving dialogue consistency. Empirical results not only demonstrate valuable use cases of NLI models in NLP applications but, more importantly, reveal the fact that the data is a key factor that contributes to the success of the usage of NLI models. That leads to the second part of this thesis, namely, adversarial NLI, a research endeavor that embodies a dynamic human-and-model-in-the-loop learning paradigm for NLI via competitive iterations between model training and crowd-sourcing to push the limit of NLU.Doctor of Philosoph