Information fusion for automated question answering

Until recently, research efforts in automated Question Answering (QA) have mainly focused on getting a good understanding of questions to retrieve correct answers. This includes deep parsing, lookups in ontologies, question typing and machine learning of answer patterns appropriate to question forms. In contrast, I have focused on the analysis of the relationships between answer candidates as provided in open domain QA on multiple documents. I argue that such candidates have intrinsic properties, partly regardless of the question, and those properties can be exploited to provide better quality and more user-oriented answers in QA.Information fusion refers to the technique of merging pieces of information from different sources. In QA over free text, it is motivated by the frequency with which different answer candidates are found in different locations, leading to a multiplicity of answers. The reason for such multiplicity is, in part, the massive amount of data used for answering, and also its unstructured and heterogeneous content: Besides am¬ biguities in user questions leading to heterogeneity in extractions, systems have to deal with redundancy, granularity and possible contradictory information. Hence the need for answer candidate comparison. While frequency has proved to be a significant char¬ acteristic of a correct answer, I evaluate the value of other relationships characterizing answer variability and redundancy.Partially inspired by recent developments in multi-document summarization, I re¬ define the concept of "answer" within an engineering approach to QA based on the Model-View-Controller (MVC) pattern of user interface design. An "answer model" is a directed graph in which nodes correspond to entities projected from extractions and edges convey relationships between such nodes. The graph represents the fusion of information contained in the set of extractions. Different views of the answer model can be produced, capturing the fact that the same answer can be expressed and pre¬ sented in various ways: picture, video, sound, written or spoken language, or a formal data structure. Within this framework, an answer is a structured object contained in the model and retrieved by a strategy to build a particular view depending on the end user (or taskj's requirements.I describe shallow techniques to compare entities and enrich the model by discovering four broad categories of relationships between entities in the model: equivalence, inclusion, aggregation and alternative. Quantitatively, answer candidate modeling im¬ proves answer extraction accuracy. It also proves to be more robust to incorrect answer candidates than traditional techniques. Qualitatively, models provide meta-information encoded by relationships that allow shallow reasoning to help organize and generate the final output

Cross-lingual Question Answering with QED

We present improvements and modifications of the QED open-domain question answering system developed for TREC-2003 to make it cross-lingual for participation in the CrossLinguistic Evaluation Forum (CLEF) Question Answering Track 2004 for the source languages French and German and the target language English. We use rule-based question translation extended with surface pattern-oriented pre- and post-processing rules for question reformulation to create and English query from its French or German original. Our system uses deep processing for the question and answers, which requires efficient and radical prior search space pruning. For answering factoid questions, we report an accuracy of 16% (German to English) and 20% (French to English), respectively

Conversational natural language interaction for place-related knowledge acquisition

We focus on the problems of using Natural Language inter- action to support pedestrians in their place-related knowledge acquisi- tion. Our case study for this discussion is a smartphone-based Natu- ral Language interface that allows users to acquire spatial and cultural knowledge of a city. The framework consists of a spoken dialogue-based information system and a smartphone client. The system is novel in com- bining geographic information system (GIS) modules such as a visibility engine with a question-answering (QA) system. Users can use the smart- phone client to engage in a variety of interleaved conversations such as navigating from A to B, using the QA functionality to learn more about points of interest (PoI) nearby, and searching for amenities and tourist attractions. This system explores a variety of research questions involving Natural Language interaction for acquisition of knowledge about space and place

電気自動車用スイッチトリラクタンスモータのトルクリップルと入力電流リップルの抑制技術

We present a city navigation and tourist information mobile dialogue app with integrated question-answering (QA) and geographic information system (GIS) modules that helps pedestrian users to navigate in and learn about urban environments. In contrast to existing mobile apps which treat these problems independently, our Android app addresses the problem of navigation and touristic question-answering in an integrated fashion using a shared dialogue context. We evaluated our system in comparison with Samsung S-Voice (which interfaces to Google navigation and Google search) with 17 users and found that users judged our system to be significantly more interesting to interact with and learn from. They also rated our system above Google search (with the Samsung S-Voice interface) for tourist information tasks

Generating Annotated Corpora for Reading Comprehension and Question Answering Evaluation

Recently, reading comprehension tests for students and adult language learners have received increased attention within the NLP community as a means to develop and evaluate robust question answering (NLQA) methods. We present our ongoing work on automatically creating richly annotated corpus resources for NLQA and on comparing automatic methods for answering questions against this data set. Starting with the CBC4Kids corpus, we have added XML annotation layers for tokenization, lemmatization, stemming, semantic classes, POS tags and bestranking syntactic parses to support future experiments with semantic answer retrieval and inference. Using this resource, we have calculated a baseline for word-overlap based answer retrieval (Hirschman et al., 1999) on the CBC4Kids data and found the method performs slightly better than on the REMEDIA corpus. We hope that our richly annotated version of the CBC4Kids corpus will become a standard resource, especially as a controlled environment for evaluating inference-based techniques

QED: The Edinburgh TREC-2003 Question Answering System

This report describes a new open-domain answer retrieval system developed at the University of Edinburgh and gives results for the TREC-12 question answering track. Phrasal answers are identified by increasingly narrowing down the search space from a large text collection to a single phrase. The system uses document retrieval, query-based passage segmentation and ranking, semantic analysis from a wide-coverage parser, and a unification-like matching procedure to extract potential answers. A simple Web-based answer validation stage is also applied. The system is based on the Open Agent Architecture and has a parallel design so that multiple questions can be answered simultaneously on a Beowulf cluster

A dialogue based mobile virtual assistant for tourists: The SpaceBook Project

Ubiquitous mobile computing offers innovative approaches in the delivery of information that can facilitate free roaming of the city, informing and guiding the tourist as the city unfolds before them. However making frequent visual reference to mobile devices can be distracting, the user having to interact via a small screen thus disrupting the explorative experience. This research reports on an EU funded project, SpaceBook, that explored the utility of a hands-free, eyes-free virtual tour guide, that could answer questions through a spoken dialogue user interface and notify the user of interesting features in view while guiding the tourist to various destinations. Visibility modelling was carried out in real-time based on a LiDAR sourced digital surface model, fused with a variety of map and crowd sourced datasets (e.g. Ordnance Survey, OpenStreetMap, Flickr, Foursquare) to establish the most interesting landmarks visible from the user's location at any given moment. A number of variations of the SpaceBook system were trialled in Edinburgh (Scotland). The research highlighted the pleasure derived from this novel form of interaction and revealed the complexity of prioritising route guidance instruction alongside identification, description and embellishment of landmark information – there being a delicate balance between the level of information ‘pushed’ to the user, and the user's requests for further information. Among a number of challenges, were issues regarding the fidelity of spatial data and positioning information required for pedestrian based systems – the pedestrian having much greater freedom of movement than vehicles

Answer comparison in automated question answering

AbstractIn the context of Question Answering (QA) on free text, we assess the value of answer comparison and information fusion in handling multiple answers. We report improvements in answer re-ranking using fusion on a set of location questions and show the advantages of considering candidates as allies rather than competitors. We conclude with some observations about answer modeling and evaluation methodology, arising from a more recent experiment with a larger set of questions and a greater diversity of question types and candidates

Information Fusion for Answering Factoid Questions

We describe an experiment involving reranking a set of answer extractions for where- questions based on computing equivalence and inclusion relations among a question and its set of answer extractions

Answer Comparison: Analysis of Relationships between Answers to `Where'-Questions

For many reasons, Question Answering must deal with questions that have multiple answers. To this end, we have built a framework for answer comparison based on a technique using information fusion that has successfully been applied in automated summarization. The architecture of this system is a direct application of the Model-View-Controller design pattern which allows us to define an answer in terms of content, structure and rendering. We present an experiment using this system on TREC `where`-questions and analyse relationships discovered between potential answers. We show that this approach is robust and a first step towards complex /structred answer generation