Automatic Extraction of Narrative Structure from Long Form Text

Abstract

Automatic understanding of stories is a long-held goal of artificial intelligence and natural language processing research communities. Stories explain the human experience. Understanding stories promotes the understanding of both individuals and groups of people; cultures, societies, families, organizations, governments, and corporations, to name a few. People use stories to share information. Stories are told-by narrators-in linguistic bundles of words called narratives. My work has given computers understanding of some aspects of narrative structure. Specifically, where are the boundaries of a narrative in a text. This is the task of determining where a narrative begins and ends, a non-trivial task, because people rarely tell one story at a time. People don't specifically announce when we are starting or stopping our stories: We interrupt each other. We tell stories within stories. Before my work, computers had no awareness of narrative boundaries, essentially where stories begin and end. My programs can extract narrative boundaries from novels and short stories with an F 1 of 0.65. viii Before this I worked on teaching computers to identify which paragraphs of text have story content, with an F 1 of 0.75 (which is state of the art). Additionally, I have taught computers to identify the narrative point of view (POV; how the narrator refers to theirself) and diegesis (how is the narrator involved in the story's action) with F 1 of over 0.90 for both narrative characteristics. For the narrative POV, diegesis, and narrative level extractors I ran annotation studies, with high agreement, that allowed me to teach computational models to identify structural elements of narrative through supervised machine learning. My work has given computers the ability to find where stories begin and end in raw text. This will allow for further, automatic analysis, like extraction of plot, intent, event causality, and event coreference. These are difficult when there are multiple narratives in one text. There are two key contributions in my work: 1) my identification of features that accurately extract information about narrators, and narrative levels and 2) the gold-standard data generated from running annotation studies on identifying these same elements of narrative structure.

Phosphonium polyelectrolytes: influence of diphosphine spacer on layer‐by‐layer assembly with anionic conjugated polymers

“…Scheme 1) such as polyphosphazenes 1,2 have drawn particular notoriety in flame-retardant materials 3 -6 such as derivatives of R1, although they are also commercially employed in optical applications (R2), 7 for glass fiber reinforcement (R3) 8,9 and as finishes for textiles (R4). 10,11 Examples of phosphorus-containing polymers with promise for more recently emerging technologies, such as semiconducting -conjugated polymers incorporating low-coordinate phosphorus centers, have also been reported. 12 -14 Phosphines are among the best-studied ligands for coordination chemistry and catalysis, and have thus naturally drawn attention as elements of coordination polymers as well.…”