Ndapandula Nakashole

Language-Aware Truth Assessment of Fact Candidates

This paper introduces FactChecker, language-aware approach to truth-finding. FactChecker differs from prior approaches in that it does not rely on iterative peer voting, instead it leverages language to infer believability of fact candidates. In particular, FactChecker makes use of linguistic features to detect if a given source objectively states facts or is speculative and opinionated. To ensure that fact candidates mentioned in similar sources have similar believability, FactChecker augments objectivity with a co-mention score to compute the overall believability score of a fact candidate. Our experiments on various datasets show that FactChecker yields higher accuracy than existing approaches.

A Knowledge-Intensive Model for Prepositional Phrase Attachment

Prepositional phrases (PPs) express crucial information that knowledge base construction methods need to extract. However, PPs are a major source of syntactic ambiguity and still pose problems in parsing. We present a method for resolving ambiguities arising from PPs, making extensive use of semantic knowledge from various resources. As training data, we use both labeled and unlabeled data, utilizing an expectation maximization algorithm for parameter estimation. Experiments show that our method yields improvements over existing methods including a state of the art dependency parser.

A Spousal Relation Begins with a Deletion of engage and Ends with an Addition of divorce: Learning State Changing Verbs from Wikipedia Revision History

Learning to determine when the timevarying facts of a Knowledge Base (KB) have to be updated is a challenging task. We propose to learn state changing verbs from Wikipedia edit history. When a state-changing event, such as a marriage or death, happens to an entity, the infobox on the entity’s Wikipedia page usually gets updated. At the same time, the article text may be updated with verbs either being added or deleted to reflect the changes made to the infobox. We use Wikipedia edit history to distantly supervise a method for automatically learning verbs and state changes. Additionally, our method uses constraints to effectively map verbs to infobox changes. We observe in our experiments that when state-changing verbs are added or deleted from an entity’s Wikipedia page text, we can predict the entity’s infobox updates with 88% precision and 76% recall. One compelling application of our verbs is to incorporate them as triggers in methods for updating existing KBs, which are currently mostly static.

Discovering sound concepts and acoustic relations in text

In this paper we describe approaches for discovering acoustic concepts and relations in text. The first major goal is to be able to identify text phrases which contain a notion of audibility and can be termed as a sound or an acoustic concept. We also propose a method to define an acoustic scene through a set of sound concepts. We use pattern matching and parts of speech tags to generate sound concepts from large scale text corpora. We use dependency parsing and LSTM recurrent neural network to predict a set of sound concepts for a given acoustic scene. These methods are not only helpful in creating an acoustic knowledge base but in the future can also directly help acoustic event and scene detection research.

CTPs: Contextual Temporal Profiles for Time Scoping Facts using State Change Detection

Temporal scope adds a time dimension to facts in Knowledge Bases (KBs). These time scopes specify the time periods when a given fact was valid in real life. Without temporal scope, many facts are underspecified, reducing the usefulness of the data for upper level applications such as Question Answering. Existing methods for temporal scope inference and extraction still suffer from low accuracy. In this paper, we present a new method that leverages temporal profiles augmented with context— Contextual Temporal Profiles (CTPs) of entities. Through change patterns in an entity’s CTP, we model the entity’s state change brought about by real world events that happen to the entity (e.g, hired, fired, divorced, etc.). This leads to a new formulation of the temporal scoping problem as a state change detection problem. Our experiments show that this formulation of the problem, and the resulting solution are highly effective for inferring temporal scope of facts.