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Siegfried Handschuh

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AAAI Conference 2019 Conference Paper

Exploring Knowledge Graphs in an Interpretable Composite Approach for Text Entailment

Vivian S. Silva
André Freitas
Siegfried Handschuh

Recognizing textual entailment is a key task for many semantic applications, such as Question Answering, Text Summarization, and Information Extraction, among others. Entailment scenarios can range from a simple syntactic variation to more complex semantic relationships between pieces of text, but most approaches try a one-size-fits-all solution that usually favors some scenario to the detriment of another. We propose a composite approach for recognizing text entailment which analyzes the entailment pair to decide whether it must be resolved syntactically or semantically. We also make the answer interpretable: whenever an entailment is solved semantically, we explore a knowledge base composed of structured lexical definitions to generate natural language humanlike justifications, explaining the semantic relationship holding between the pieces of text. Besides outperforming wellestablished entailment algorithms, our composite approach gives an important step towards Explainable AI, using world knowledge to make the semantic reasoning process explicit and understandable.

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AAAI Conference 2018 Conference Paper

Recognizing and Justifying Text Entailment Through Distributional Navigation on Definition Graphs

Vivian Silva
Siegfried Handschuh
André Freitas

Text entailment, the task of determining whether a piece of text logically follows from another piece of text, has become an important component for many natural language processing tasks, such as question answering and information retrieval. For entailments requiring world knowledge, most systems still work as a “black box”, providing a yes/no answer that doesn’t explain the reasoning behind it. We propose an interpretable text entailment approach that, given a structured deﬁnition graph, uses a navigation algorithm based on distributional semantic models to ﬁnd a path in the graph which links text and hypothesis. If such path is found, it is used to provide a human-readable justiﬁcation explaining why the entailment holds. Experiments show that the proposed approach present results comparable to some well-established entailment algorithms, while also meeting Explainable AI requirements, supplying clear explanations which allow the inference model interpretation.

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TIST Journal 2012 Journal Article

Visual Abstraction and Ordering in Faceted Browsing of Text Collections

Vinhtuan Thai
Pierre-Yves Rouille
Siegfried Handschuh

Faceted navigation is a technique for the exploration and discovery of a collection of resources, which can be of various types including text documents. While being information-rich resources, documents are usually not treated as content-bearing items in faceted browsing interfaces, and yet the required clean metadata is not always available or matches users’ interest. In addition, the existing linear listing paradigm for representing result items from the faceted filtering process makes it difficult for users to traverse or compare across facet values in different orders of importance to them. In this context, we report in this article a visual support toward faceted browsing of a collection of documents based on a set of entities of interest to users. Our proposed approach involves using a multi-dimensional visualization as an alternative to the linear listing of focus items. In this visualization, visual abstraction based on a combination of a conceptual structure and the structural equivalence of documents can be simultaneously used to deal with a large number of items. Furthermore, the approach also enables visual ordering based on the importance of facet values to support prioritized, cross-facet comparisons of focus items. A user study was conducted and the results suggest that interfaces using the proposed approach can support users better in exploratory tasks and were also well-liked by the participants of the study, with the hybrid interface combining the multi-dimensional visualization with the linear listing receiving the most favorable ratings.

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