Abstract Typically, automatic Question Answering (QA) approaches use the question in its entirety in the search for potential answers. We argue that decomposing complex factoid questions into separate facts about their answers is beneficial to QA, since an answer candidate with support coming from multiple independent facts is more likely to be the correct one. We broadly categorize decomposable questions as parallel or nested, and we present a novel question decomposition framework for enhancing the ability of single-shot QA systems to answer complex factoid questions. Essential to the framework are components for decomposition recognition, question rewriting, and candidate answer synthesis and re-ranking. We discuss the interplay among these, with particular emphasis on decomposition recognition, a process which, we argue, can be sufficiently informed by lexico-syntactic features alone. We validate our approach to decomposition by implementing the framework on top of IBM Watson™, a state-of-the-art QA system, and showing a statistically significant improvement over its accuracy.

State-of-the-art approaches to token labeling within text documents typically cast the problem either as a classification task, without using complex structural characteristics of the input, or as a sequential labeling task, carried out by a Conditional Random Field (CRF) classifier. Here we explore principled ways for structure to be brought to bear on the task. In line with recent trends in statistical learning of structured natural language input, we use a Support Vector Machine (SVM) classification framework deploying tree kernels. We then propose tree transformations and decorations, as a methodology for modeling complex linguistic phenomena in highly multi-dimensional feature spaces. We develop a general purpose tree engineering framework, which enables us to transcend the typically complex and laborious process of feature engineering. We build kernel based classifiers for two token labeling tasks: fine-grained event recognition, and lexical answer type detection in questions. For both, we show that in comparison with a corresponding linear kernel SVM, our method of using tree kernels improves recognition, thanks to appropriately engineering tree structures for use by the tree kernel. We also observe significant improvements when comparing with a CRF-based realization of structured prediction, itself performing at levels comparable to state-of-the-art.

Factoid questions often contain one or more assertions (facts) about their answers. However, existing question-answering (QA) systems have not investigated how the multiple facts may be leveraged to enhance system performance. We argue that decomposing complex factoid questions can benefit QA, as an answer candidate is more likely to be correct if multiple independent facts support it. We categorize decomposable questions as parallel or nested, depending on processing strategy required. We present a novel decomposition framework---for parallel and nested questions---which can be overlaid on top of traditional QA systems. It contains decomposition rules for identifying fact sub-questions, a question-rewriting component and a candidate re-ranker. In a particularly challenging domain for our baseline QA system, our framework shows a statistically significant improvement in end-to-end QA performance.

We present 1) a novel linguistic engine made of configurable linguistic components for understanding natural language use case specification; and 2) results of the first of a kind large scale experiment of application of linguistic techniques to industrial use cases. Requirement defects are well known to have adverse effects on dependability of software systems. While formal techniques are often cited as a remedy for specification errors, natural language remains the predominant mode for specifying requirements. Therefore, for dependable system development, a natural language processing technique is required that can translate natural language textual requirements into validation ready computer models. In this paper, we present the implementation details of such a technique and the results of applying a prototype implementation of our technique to 80 industrial and academic use case descriptions. We report on the accuracy and effectiveness of our technique. The results of our experiment are very encouraging.

This paper describes a novel approach to multi-document summarization, which explicitly addresses the problem of detecting, and retaining for the summary, multiple themes in document collections. We place equal emphasis on the processes of theme identification and theme presentation. For the former, we apply Iterative Residual Rescaling (IRR); for the latter, we argue for graphical display elements. IRR is an algorithm designed to account for correlations between words and to construct multi-dimensional topical space indicative of relationships among linguistic objects (documents, phrases, and sentences). Summaries are composed of objects with certain properties, derived by exploiting the many-to-many relationships in such a space. Given their inherent complexity, our multi-faceted summaries benefit from a visualization environment. We discuss some essential features of such an environment.

We present the architecture and data model for TEXTRACT, a robust, scalable and configurable document analysis framework. TEXTRACT has been engineered as a pipeline architecture, allowing for rapid prototyping and application development by freely mixing reusable, existing, language analysis plugins and custom, new, plugins with customizable functionality. We discuss design issues which arise from requirements of industrial strength efficiency and scalability, and which are further constrained by plugin interactions, both among themselves, and with a common data model comprising an annotation store, document vocabulary and a lexical cache. We exemplify some of these by focusing on a meta-plugin: an interpreter for annotation-based finite state transduction, through which many linguistic filters can be implemented as stand-alone plugins. The framework and component plugins have been extensively deployed in both research and industrial environments, for a broad range of text analysis and mining tasks.

With this first issue of Volume 10 of Natural Language Engineering we would like to take this opportunity to reflect on the development of the journal, especially over the last year or two, and to look forward to a number of future developments.

This is the first issue of Volume 8, and we thought we would take this opportunity to bring readers of Natural Language Engineering up-to-date with various developments with the journal.

Americanae nace como un proyecto conjunto que surge dentro de la Red Europea de Información y Documentación sobre América Latina (REDIAL), y que ha afrontado la Biblioteca de la Agencia Española de Cooperación Internacional para el Desarrollo (AECID). Esta nueva biblioteca virtual hace más accesibles los libros digitales de tema americanista a los investigadores y usuarios interesados de cualquier parte del mundo.

The identification and extraction of technical terms is one of the better understood and most robust Natural Language Processing (NLP) technologies within the current state of the art of language engineering. In generic information management contexts, terms have been used primarily for procedures seeking to identify a set of phrases that is useful for tasks such as text indexing, computational lexicology, and machine-assisted translation: such tasks make important use of the assumption that terminology is representative of a given domain. This paper discusses an extension of basic terminology identification technology for the application to two higher level semantic tasks: domain description, the specification of the technical domain of a document, and content characterisation, the construction of a compact, coherent and useful representation of the topical content of a text. With these extensions, terminology identification becomes the foundation of an operational environment for document processing and content abstraction.

We identify two complementary processes in the conversion of machine-readable dictionaries into lexical databases: recovery of the dictionary stucture from the typographical markings which persist on the dictionary distribution tapes and embody the publishers' notational conventions; followed by making explicit all of the codified and ellided information packed into individual entries. We discuss notational conventions and tape formats, outline structural properties of dictionaries, observe a range of representational phenomena particularly relevant to dictionary parsing, and derive a set of minimal requirements for a dictionary grammar formalism. We present a general purpose dictionary entry parser which uses a formal notation designed to describe the structure of entries and performs a mapping from the flat character stream on the tape to a highly structured and fully instantiated representation of the dictionary. We demonstrate the power of the formalism by drawing examples from a range of dictionary sources which have been processed and converted into lexical databases.

We describe a methodology and associated software system for the construction of a large lexicon from an existing machine-readable (published) dictionary. The lexicon serves as a component of an English morphological and syntactic analyser and contains entries with grammatical definitions compatible with the word and sentence grammar employed by the analyser. We describe a software system with two integrated components. One of these is capable of extracting syntactically rich, theory-neutral lexical templates from a suitable machine-readable source. The second supports interactive and semi-automatic generation and testing of target lexical entries in order to derive a sizeable, accurate and consistent lexicon from the source dictionary which contains partial (and occasionally in-accurate) information. Finally, we evaluate the utility of the Longman Dictionary of Contemporary English as a suitable source dictionary for the target lexicon.

In this paper, we approach the problem of organisation and control in automatic speech understanding systems firstly, by presenting a theory of the non-serial interactions necessary between two processors in the system: namely, the morphosyntactic and the prosodic, and secondly, by showing how, when generalised, this theory allows one to specify a highly efficient architecture for a speech understanding system with a simple control structure and genuinely independent components. The theory of non-serial interactions we present predicts that speech is temporally organised in a very specific way; that is, the system would not function effectively if the temporal distribution of various types of information in speech were different. The architecture we propose is developed from a study of the task of speech understanding and, furthermore, is specific to this task. Consequently, the paper argues that general problem solving methods are unnecessary for speech understanding.