Sankofa Project

Our class, the Sankofa Project, traveled to the Cleveland School of The Arts for a week long residency. This is our final project, a book that is full of interviews and artwork from our trip. We did all of this work remotely for the culminating group project remotely.https://digital.kenyon.edu/covid19words/1051/thumbnail.jp

A Network of SLC and ABC Transporter and DME Genes Involved in Remote Sensing and Signaling in the Gut-Liver-Kidney Axis.

Genes central to drug absorption, distribution, metabolism and elimination (ADME) also regulate numerous endogenous molecules. The Remote Sensing and Signaling Hypothesis argues that an ADME gene-centered network-including SLC and ABC "drug" transporters, "drug" metabolizing enzymes (DMEs), and regulatory genes-is essential for inter-organ communication via metabolites, signaling molecules, antioxidants, gut microbiome products, uremic solutes, and uremic toxins. By cross-tissue co-expression network analysis, the gut, liver, and kidney (GLK) formed highly connected tissue-specific clusters of SLC transporters, ABC transporters, and DMEs. SLC22, SLC25 and SLC35 families were network hubs, having more inter-organ and intra-organ connections than other families. Analysis of the GLK network revealed key physiological pathways (e.g., involving bile acids and uric acid). A search for additional genes interacting with the network identified HNF4α, HNF1α, and PXR. Knockout gene expression data confirmed ~60-70% of predictions of ADME gene regulation by these transcription factors. Using the GLK network and known ADME genes, we built a tentative gut-liver-kidney "remote sensing and signaling network" consisting of SLC and ABC transporters, as well as DMEs and regulatory proteins. Together with protein-protein interactions to prioritize likely functional connections, this network suggests how multi-specificity combines with oligo-specificity and mono-specificity to regulate homeostasis of numerous endogenous small molecules

Detecting Opinionated Claims in Online Discussions

This paper explores the automatic detection of sentences that are opinionated claims, in which the author expresses a belief. We use a machine learning based approach, investigating the impact of features such as sentiment and the output of a system that determines committed belief. We train and test our approach on social media, where people often try to convince others of the validity of their opinions. We experiment with two different types of data, drawn from LiveJournal weblogs and Wikipedia discussion forums. Our experiments show that sentiment analysis is more important in LiveJournal, while committed belief is more helpful for Wikipedia. In both corpora,n-grams and part-of-speech features also account for significantly better accuracy. We discuss the ramifications behind these differences

Large-Scale Goodness Polarity Lexicons for Community Question Answering

We transfer a key idea from the field of sentiment analysis to a new domain: community question answering (cQA). The cQA task we are interested in is the following: given a question and a thread of comments, we want to re-rank the comments so that the ones that are good answers to the question would be ranked higher than the bad ones. We notice that good vs. bad comments use specific vocabulary and that one can often predict the goodness/badness of a comment even ignoring the question, based on the comment contents only. This leads us to the idea to build a good/bad polarity lexicon as an analogy to the positive/negative sentiment polarity lexicons, commonly used in sentiment analysis. In particular, we use pointwise mutual information in order to build large-scale goodness polarity lexicons in a semi-supervised manner starting with a small number of initial seeds. The evaluation results show an improvement of 0.7 MAP points absolute over a very strong baseline and state-of-the art performance on SemEval-2016 Task 3.Comment: SIGIR '17, August 07-11, 2017, Shinjuku, Tokyo, Japan; Community Question Answering; Goodness polarity lexicons; Sentiment Analysi

Detecting Influencers in Social Media Discussions

In the past decade we have been privileged to witness the creation and revolution of social media on the World Wide Web. The abundance of content available on the web allows us to analyze the way people interact and the roles they play in a conversation on a large scale. One such role is influencer in the conversation. Detecting influence can be useful for successful advertisement strategies, detecting terrorist leaders and political campaigning. We explore influence in discussion forums, weblogs, and micro-blogs using several components that have been found to be indicators of influence. Our components are author traits, agreement, claims, argumentation, persuasion, credibility, and certain dialog patterns. In the first portion of this thesis we describe each of our system components. Each of these components is motivated by social science through Robert Cialdini’s “Weapons of Influence” [Cialdini, 2007]. The weapons of influence are Reciprocation, Commitment and Consistency, Social Proof, Liking, Authority, and Scarcity. We then show the method and experiments for classifying each component. In the second part of this thesis we classify influencers across five online genres and analyze which features are most indicative of influencers in each genre. The online genres we explore are Wikipedia Talk Pages, LiveJournal weblogs, Political Forum discussions, Create Debate debate discussions, and Twitter microblog conversations. First, we describe a rich suite of features that were generated using each of the system components. Then, we describe our experiments and results including using domain adaptation to exploit the data from multiple online genres. Finally, we also provide a detailed analysis of a single weapon of influence, social proof, and its impact in detecting influence in Wikipedia Talk Pages. This provides a single example of the usefulness of providing comprehensive components in the detection of influence. The contributions of this thesis include a system for predicting who the influencers are in online discussion forums. We provide an evaluation of a rich set of features inspired by social science. In our system, each feature set used to detect influence is complex and computed by a system component. This allows us to provide a detailed analysis as to why the person was chosen as an influencer. We also provide a comparison of differences across several online discussion datasets and exploit the differences across the different genres to provide further improvements in influence detection

Time-Efficient Creation of an Accurate Sentence Fusion Corpus

Sentence fusion enables summarization and question-answering systems to produce output by combining fully formed phrases from different sentences. Yet there is little data that can be used to develop and evaluate fusion techniques. In this paper, we present a methodology for collecting fusions of similar sentence pairs using Amazon’s Mechanical Turk, selecting the input pairs in a semiautomated fashion. We evaluate the results using a novel technique for automatically selecting a representative sentence from multiple responses. Our approach allows for rapid construction of a high accuracy fusion corpus

CLAPNQ: Cohesive Long-form Answers from Passages in Natural Questions for RAG systems

Retrieval Augmented Generation (RAG) has become a popular application for large language models. It is preferable that successful RAG systems provide accurate answers that are supported by being grounded in a passage without any hallucinations. While considerable work is required for building a full RAG pipeline, being able to benchmark performance is also necessary. We present ClapNQ, a benchmark Long-form Question Answering dataset for the full RAG pipeline. ClapNQ includes long answers with grounded gold passages from Natural Questions (NQ) and a corpus to perform either retrieval, generation, or the full RAG pipeline. The ClapNQ answers are concise, 3x smaller than the full passage, and cohesive, with multiple pieces of the passage that are not contiguous. RAG models must adapt to these properties to be successful at ClapNQ. We present baseline experiments and analysis for ClapNQ that highlight areas where there is still significant room for improvement in grounded RAG. CLAPNQ is publicly available at https://github.com/primeqa/clapnqComment: 25 page