• Key research areas: Information extraction, temporal anchoring, temporal ordering, multimodal event extraction, possessions
  • My research focuses on extracting events and their attributes. In particular, my Ph.D. research concentrates on a single event---possession. Possession is an asymmetric semantic relation, where an entity (possessor) possesses another entity (possessee). Throughout my Ph.D., I work on automatically extracting possession and its attributes. In this section, I briefly explain my research published so far. For more details on my research read my publications.

    Temporally-oriented possession: A corpus for tracking possession over time

    In this work, we present a new corpus for tracking concrete objects as they change hands over time. We then generate a timeline using the corpus which includes possessors, the certainty of possession, and the duration of possession. In addition to possessors that are named entities, we extract possessors that are not identified by conventional NERs. We also present a strategy to place possessors who do not have an explicit time mention on the timeline.

    Possessors Change Over Time: A Case Study with Artworks

    In this research, we work on Wikipedia articles on historical artifacts, which mostly includes paintings like the Guernica and the Mona Lisa. The goal is to identify all possessors of a chosen artifact and the years in which they were possessed. We present a mechanism to pair the possessors and years, along with a LSTM ensemble to predict possession relations.

    Mining possesions: Existence, Type and Temporal Anchor

    In this work, we extract the presence of possession in sentence level. We then identify the type of possession and temporally anchor the possession relation with respect to the verb in the sentence. We automatically extract these relations using SVM and deep learning models.