A Comparative analysis: QA evaluation questions versus real-world queries

This paper presents a comparative analysis of user queries to a web search engine, questions to a Q&A service (answers.com), and questions employed in question answering (QA) evaluations at TREC and CLEF. The analysis shows that user queries to search engines contain mostly content words (i.e. keywords) but lack structure words (i.e. stopwords) and capitalization. Thus, they resemble natural language input after case folding and stopword removal. In contrast, topics for QA evaluation and questions to answers.com mainly consist of fully capitalized and syntactically well-formed questions. Classification experiments using a na¨ıve Bayes classifier show that stopwords play an important role in determining the expected answer type. A classification based on stopwords is considerably more accurate (47.5% accuracy) than a classification based on all query words (40.1% accuracy) or on content words (33.9% accuracy). To simulate user input, questions are preprocessed by case folding and stopword removal. Additional classification experiments aim at reconstructing the syntactic wh-word frame of a question, i.e. the embedding of the interrogative word. Results indicate that this part of questions can be reconstructed with moderate accuracy (25.7%), but for a classification problem with a much larger number of classes compared to classifying queries by expected answer type (2096 classes vs. 130 classes). Furthermore, eliminating stopwords can lead to multiple reconstructed questions with a different or with the opposite meaning (e.g. if negations or temporal restrictions are included). In conclusion, question reconstruction from short user queries can be seen as a new realistic evaluation challenge for QA systems

Type Classes and Instance Chains: A Relational Approach

Type classes, first proposed during the design of the Haskell programming language, extend standard type systems to support overloaded functions. Since their introduction, type classes have been used to address a range of problems, from typing ordering and arithmetic operators to describing heterogeneous lists and limited subtyping. However, while type class programming is useful for a variety of practical problems, its wider use is limited by the inexpressiveness and hidden complexity of current mechanisms. We propose two improvements to existing class systems. First, we introduce several novel language features, instance chains and explicit failure, that increase the expressiveness of type classes while providing more direct expression of current idioms. To validate these features, we have built an implementation of these features, demonstrating their use in a practical setting and their integration with type reconstruction for a Hindley-Milner type system. Second, we define a set-based semantics for type classes that provides a sound basis for reasoning about type class systems, their implementations, and the meanings of programs that use them

Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the LHC. In this paper, a multihead attention message passing network is used to classify the relationship between detector hits by labelling graph edges, determining whether hits were produced by the same underlying particle, and if so, the particle type. The trained model is 84% accurate overall, and performs best on the EM shower and muon track classes. The model's strengths and weaknesses are discussed, and plans for developing this technique further are summarised.Comment: 7 pages, 3 figures, submitted to the 25th International Conference on Computing in High-Energy and Nuclear Physic