An authoring tool for decision support systems in context questions of ecological knowledge

Decision support systems (DSS) support business or organizational decision-making activities, which require the access to information that is internally stored in databases or data warehouses, and externally in the Web accessed by Information Retrieval (IR) or Question Answering (QA) systems. Graphical interfaces to query these sources of information ease to constrain dynamically query formulation based on user selections, but they present a lack of flexibility in query formulation, since the expressivity power is reduced to the user interface design. Natural language interfaces (NLI) are expected as the optimal solution. However, especially for non-expert users, a real natural communication is the most difficult to realize effectively. In this paper, we propose an NLI that improves the interaction between the user and the DSS by means of referencing previous questions or their answers (i.e. anaphora such as the pronoun reference in “What traits are affected by them?”), or by eliding parts of the question (i.e. ellipsis such as “And to glume colour?” after the question “Tell me the QTLs related to awn colour in wheat”). Moreover, in order to overcome one of the main problems of NLIs about the difficulty to adapt an NLI to a new domain, our proposal is based on ontologies that are obtained semi-automatically from a framework that allows the integration of internal and external, structured and unstructured information. Therefore, our proposal can interface with databases, data warehouses, QA and IR systems. Because of the high NL ambiguity of the resolution process, our proposal is presented as an authoring tool that helps the user to query efficiently in natural language. Finally, our proposal is tested on a DSS case scenario about Biotechnology and Agriculture, whose knowledge base is the CEREALAB database as internal structured data, and the Web (e.g. PubMed) as external unstructured information.This paper has been partially supported by the MESOLAP (TIN2010-14860), GEODAS-BI (TIN2012-37493-C03-03), LEGOLANGUAGE (TIN2012-31224) and DIIM2.0 (PROMETEOII/2014/001) projects from the Spanish Ministry of Education and Competitivity. Alejandro Maté is funded by the Generalitat Valenciana under an ACIF grant (ACIF/2010/298)

Artequakt: Generating tailored biographies from automatically annotated fragments from the web

The Artequakt project seeks to automatically generate narrativebiographies of artists from knowledge that has been extracted from the Web and maintained in a knowledge base. An overview of the system architecture is presented here and the three key components of that architecture are explained in detail, namely knowledge extraction, information management and biography construction. Conclusions are drawn from the initial experiences of the project and future progress is detailed

CHR as grammar formalism. A first report

Grammars written as Constraint Handling Rules (CHR) can be executed as efficient and robust bottom-up parsers that provide a straightforward, non-backtracking treatment of ambiguity. Abduction with integrity constraints as well as other dynamic hypothesis generation techniques fit naturally into such grammars and are exemplified for anaphora resolution, coordination and text interpretation.Comment: 12 pages. Presented at ERCIM Workshop on Constraints, Prague, Czech Republic, June 18-20, 200

A perceptually based computational framework for the interpretation of spatial language

The goal of this work is to develop a semantic framework to underpin the development of natural language (NL) interfaces for 3 Dimensional (3-D) simulated environments. The thesis of this work is that the computational interpretation of language in such environments should be based on a framework that integrates a model of visual perception with a model of discourse. When interacting with a 3-D environment, users have two main goals the first is to move around in the simulated environment and the second is to manipulate objects in the environment. In order to interact with an object through language, users need to be able to refer to the object. There are many different types of referring expressions including definite descriptions, pronominals, demonstratives, one-anaphora, other-expressions, and locative-expressions Some of these expressions are anaphoric (e g , pronominals, oneanaphora, other-expressions). In order to computationally interpret these, it is necessary to develop, and implement, a discourse model. Interpreting locative expressions requires a semantic model for prepositions and a mechanism for selecting the user’s intended frame of reference. Finally, many of these expressions presuppose a visual context. In order to interpret them this context must be modelled and utilised. This thesis develops a perceptually grounded discourse-based computational model of reference resolution capable of handling anaphoric and locative expressions. There are three novel contributions in this framework a visual saliency algorithm, a semantic model for locative expressions containing projective prepositions, and a discourse model. The visual saliency algorithm grades the prominence of the objects in the user's view volume at each frame. This algorithm is based on the assumption that objects which are larger and more central to the user's view are more prominent than objects which are smaller or on the periphery of their view. The resulting saliency ratings for each frame are stored in a data structure linked to the NL system’s context model. This approach gives the system a visual memory that may be drawn upon in order to resolve references. The semantic model for locative expressions defines a computational algorithm for interpreting locatives that contain a projective preposition. Specifically, the prepositions in front of behind, to the right of, and to the left of. There are several novel components within this model. First, there is a procedure for handling the issue of frame of reference selection. Second, there is an algorithm for modelling the spatial templates of projective prepositions. This algonthm integrates a topological model with visual perceptual cues. This approach allows us to correctly define the regions described by projective preposition in the viewer-centred frame of reference, in situations that previous models (Yamada 1993, Gapp 1994a, Olivier et al 1994, Fuhr et al 1998) have found problematic. Thirdly, the abstraction used to represent the candidate trajectors of a locative expression ensures that each candidate is ascribed the highest rating possible. This approach guarantees that the candidate trajector that occupies the location with the highest applicability in the prepositions spatial template is selected as the locative’s referent. The context model extends the work of Salmon-Alt and Romary (2001) by integrating the perceptual information created by the visual saliency algonthm with a model of discourse. Moreover, the context model defines an interpretation process that provides an explicit account of how the visual and linguistic information sources are utilised when attributing a referent to a nominal expression. It is important to note that the context model provides the set of candidate referents and candidate trajectors for the locative expression interpretation algorithm. These are restncted to those objects that the user has seen. The thesis shows that visual salience provides a qualitative control in NL interpretation for 3-D simulated environments and captures interesting and significant effects such as graded judgments. Moreover, it provides an account for how object occlusion impacts on the semantics of projective prepositions that are canonically aligned with the front-back axis in the viewer-centred frame of reference

Anaphora resolution for Arabic machine translation :a case study of nafs

PhD ThesisIn the age of the internet, email, and social media there is an increasing need for processing online information, for example, to support education and business. This has led to the rapid development of natural language processing technologies such as computational linguistics, information retrieval, and data mining. As a branch of computational linguistics, anaphora resolution has attracted much interest. This is reflected in the large number of papers on the topic published in journals such as Computational Linguistics. Mitkov (2002) and Ji et al. (2005) have argued that the overall quality of anaphora resolution systems remains low, despite practical advances in the area, and that major challenges include dealing with real-world knowledge and accurate parsing. This thesis investigates the following research question: can an algorithm be found for the resolution of the anaphor nafs in Arabic text which is accurate to at least 90%, scales linearly with text size, and requires a minimum of knowledge resources? A resolution algorithm intended to satisfy these criteria is proposed. Testing on a corpus of contemporary Arabic shows that it does indeed satisfy the criteria.Egyptian Government

Desiderata for an Every Citizen Interface to the National Information Infrastructure: Challenges for NLP

In this paper, I provide desiderata for an interface that would enable ordinary people to properly access the capabilities of the NII. I identify some of the technologies that will be needed to achieve these desiderata, and discuss current and future research directions that could lead to the development of such technologies. In particular, I focus on the ways in which theory and techniques from natural language processing could contribute to future interfaces to the NII. Introduction The evolving national information infrastructure (NII) has made available a vast array of on-line services and networked information resources in a variety of forms (text, speech, graphics, images, video). At the same time, advances in computing and telecommunications technology have made it possible for an increasing number of households to own (or lease or use) powerful personal computers that are connected to this resource. Accompanying this progress is the expectation that people will be able to more..

Entity-centric knowledge discovery for idiosyncratic domains

Technical and scientific knowledge is produced at an ever-accelerating pace, leading to increasing issues when trying to automatically organize or process it, e.g., when searching for relevant prior work. Knowledge can today be produced both in unstructured (plain text) and structured (metadata or linked data) forms. However, unstructured content is still themost dominant formused to represent scientific knowledge. In order to facilitate the extraction and discovery of relevant content, new automated and scalable methods for processing, structuring and organizing scientific knowledge are called for. In this context, a number of applications are emerging, ranging fromNamed Entity Recognition (NER) and Entity Linking tools for scientific papers to specific platforms leveraging information extraction techniques to organize scientific knowledge. In this thesis, we tackle the tasks of Entity Recognition, Disambiguation and Linking in idiosyncratic domains with an emphasis on scientific literature. Furthermore, we study the related task of co-reference resolution with a specific focus on named entities. We start by exploring Named Entity Recognition, a task that aims to identify the boundaries of named entities in textual contents. We propose a newmethod to generate candidate named entities based on n-gram collocation statistics and design several entity recognition features to further classify them. In addition, we show how the use of external knowledge bases (either domain-specific like DBLP or generic like DBPedia) can be leveraged to improve the effectiveness of NER for idiosyncratic domains. Subsequently, we move to Entity Disambiguation, which is typically performed after entity recognition in order to link an entity to a knowledge base. We propose novel semi-supervised methods for word disambiguation leveraging the structure of a community-based ontology of scientific concepts. Our approach exploits the graph structure that connects different terms and their definitions to automatically identify the correct sense that was originally picked by the authors of a scientific publication. We then turn to co-reference resolution, a task aiming at identifying entities that appear using various forms throughout the text. We propose an approach to type entities leveraging an inverted index built on top of a knowledge base, and to subsequently re-assign entities based on the semantic relatedness of the introduced types. Finally, we describe an application which goal is to help researchers discover and manage scientific publications. We focus on the problem of selecting relevant tags to organize collections of research papers in that context. We experimentally demonstrate that the use of a community-authored ontology together with information about the position of the concepts in the documents allows to significantly increase the precision of tag selection over standard methods