A pinboard by
Snigdha Chaturvedi

Postdoctoral researcher, University of Illinois, Urbana-Champaign, USA


Natural Language Understanding

Despite recent advancements in Artificial Intelligence (AI), computers today cannot understand text in the ways that humans can. E.g. interactive agents like Siri can follow predefined instructions (such as to report current weather), but cannot engage in meaningful conversations like a human assistant. This is because existing methods are designed to process text but don't necessarily ‘understand’ it. My research aims at creating computational models/algorithms that not only ‘read’ but also interpret and reason about text. These models are ‘structured’ in the sense that, unlike previous methods, they view each sentence in light of other sentences in the text. These structured models are also aware of social dynamics of humans, linguistic structure and semantic knowledge. Such systems can find use in many domains such as social networks like Facebook and Twitter, discussion forums like Quora, intelligent virtual assistants like Siri and Alexa, artificial tutors, etc.

Over the past few years, I have developed models for understanding relationships between various people in text (such as novels, news articles, discussion forum etc.). Understanding these relationships is essential to text understanding because they help in explaining people's desires, goals, actions and expected behaviors. E.g. consider ‘The duke asked the king to surrender, and he refused' and ‘Tom asked his mother for another cookie, and she refused'. Despite syntactic similarities, the relationship between people is one of mutual hostility in one case, and asymmetric authority in the other. Automatically inferring such relationships is important for allowing a computer infer what is not explicitly stated and what is expected in the story. E.g., ‘The duke then imprisoned the king' is something that a reader might expect, but ‘Tom then imprisoned mother' would be surprising. A computer can have this capability only if it understands dynamics of social relationships.

My research also explores models for general understanding of story-like texts such as news articles. This can enable computers to understand social norms, human behavior and commonsense. I develop models that attempt to understand a story on three semantic axes: (i) the sequence of events described in the text, (ii) its emotional trajectory, and (iii) its plot consistency. We judge the model’s understanding by inquiring if, like humans, it can develop an expectation of what will happen next in a given story.


Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks

Abstract: Many real world systems need to operate on heterogeneous information networks that consist of numerous interacting components of different types. Examples include systems that perform data analysis on biological information networks; social networks; and information extraction systems processing unstructured data to convert raw text to knowledge graphs. Many previous works describe specialized approaches to perform specific types of analysis, mining and learning on such networks. In this work, we propose a unified framework consisting of a data model -a graph with a first order schema along with a declarative language for constructing, querying and manipulating such networks in ways that facilitate relational and structured machine learning. In particular, we provide an initial prototype for a relational and graph traversal query language where queries are directly used as relational features for structured machine learning models. Feature extraction is performed by making declarative graph traversal queries. Learning and inference models can directly operate on this relational representation and augment it with new data and knowledge that, in turn, is integrated seamlessly into the relational structure to support new predictions. We demonstrate this system's capabilities by showcasing tasks in natural language processing and computational biology domains.

Pub.: 24 Jul '17, Pinned: 04 Aug '17