Examining the Causal Effect of First Names on Language Models: The Case of Social Commonsense Reasoning

As language models continue to be integrated into applications of personal and societal relevance, ensuring these models' trustworthiness is crucial, particularly with respect to producing consistent outputs regardless of sensitive attributes. Given that first names may serve as proxies for (intersectional) socio-demographic representations, it is imperative to examine the impact of first names on commonsense reasoning capabilities. In this paper, we study whether a model's reasoning given a specific input differs based on the first names provided. Our underlying assumption is that the reasoning about Alice should not differ from the reasoning about James. We propose and implement a controlled experimental framework to measure the causal effect of first names on commonsense reasoning, enabling us to distinguish between model predictions due to chance and caused by actual factors of interest. Our results indicate that the frequency of first names has a direct effect on model prediction, with less frequent names yielding divergent predictions compared to more frequent names. To gain insights into the internal mechanisms of models that are contributing to these behaviors, we also conduct an in-depth explainable analysis. Overall, our findings suggest that to ensure model robustness, it is essential to augment datasets with more diverse first names during the configuration stage.

Complementary and Integrative Health Lexicon (CIHLex) and Entity Recognition in the Literature

Objective: Our study aimed to construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to better represent the often underrepresented physical and psychological CIH approaches in standard terminologies. We also intended to apply advanced Natural Language Processing (NLP) models such as Bidirectional Encoder Representations from Transformers (BERT) and GPT-3.5 Turbo for CIH named entity recognition, evaluating their performance against established models like MetaMap and CLAMP. Materials and Methods: We constructed the CIHLex by integrating various resources, compiling and integrating data from biomedical literature and relevant knowledge bases. The Lexicon encompasses 198 unique concepts with 1090 corresponding unique terms. We matched these concepts to the Unified Medical Language System (UMLS). Additionally, we developed and utilized BERT models and compared their efficiency in CIH named entity recognition to that of other models such as MetaMap, CLAMP, and GPT3.5-turbo. Results: From the 198 unique concepts in CIHLex, 62.1% could be matched to at least one term in the UMLS. Moreover, 75.7% of the mapped UMLS Concept Unique Identifiers (CUIs) were categorized as "Therapeutic or Preventive Procedure." Among the models applied to CIH named entity recognition, BLUEBERT delivered the highest macro average F1-score of 0.90, surpassing other models. Conclusion: Our CIHLex significantly augments representation of CIH approaches in biomedical literature. Demonstrating the utility of advanced NLP models, BERT notably excelled in CIH entity recognition. These results highlight promising strategies for enhancing standardization and recognition of CIH terminology in biomedical contexts.

Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation

Improving the emotional awareness of pre-trained language models is an emerging important problem for dialogue generation tasks. Although prior studies have introduced methods to improve empathetic dialogue generation, few have discussed how to incorporate commonsense knowledge into pre-trained language models for controllable dialogue generation. In this study, we propose a novel framework that improves empathetic dialogue generation using pre-trained language models by 1) incorporating commonsense knowledge through prompt verbalization, and 2) controlling dialogue generation using a strategy-driven future discriminator. We conducted experiments to reveal that both the incorporation of social commonsense knowledge and enforcement of control over generation help to improve generation performance. Finally, we discuss the implications of our study for future research.

Developing a Knowledge Graph Framework for Pharmacokinetic Natural Product-Drug Interactions

Pharmacokinetic natural product-drug interactions (NPDIs) occur when botanical natural products are co-consumed with pharmaceutical drugs. Understanding mechanisms of NPDIs is key to preventing adverse events. We constructed a knowledge graph framework, NP-KG, as a step toward computational discovery of pharmacokinetic NPDIs. NP-KG is a heterogeneous KG with biomedical ontologies, linked data, and full texts of the scientific literature, constructed with the Phenotype Knowledge Translator framework and the semantic relation extraction systems, SemRep and Integrated Network and Dynamic Reasoning Assembler. NP-KG was evaluated with case studies of pharmacokinetic green tea- and kratom-drug interactions through path searches and meta-path discovery to determine congruent and contradictory information compared to ground truth data. The fully integrated NP-KG consisted of 745,512 nodes and 7,249,576 edges. Evaluation of NP-KG resulted in congruent (38.98% for green tea, 50% for kratom), contradictory (15.25% for green tea, 21.43% for kratom), and both congruent and contradictory (15.25% for green tea, 21.43% for kratom) information. Potential pharmacokinetic mechanisms for several purported NPDIs, including the green tea-raloxifene, green tea-nadolol, kratom-midazolam, kratom-quetiapine, and kratom-venlafaxine interactions were congruent with the published literature. NP-KG is the first KG to integrate biomedical ontologies with full texts of the scientific literature focused on natural products. We demonstrate the application of NP-KG to identify pharmacokinetic interactions involving enzymes, transporters, and pharmaceutical drugs. We envision that NP-KG will facilitate improved human-machine collaboration to guide researchers in future studies of pharmacokinetic NPDIs. The NP-KG framework is publicly available at https://doi.org/10.5281/zenodo.6814507 and https://github.com/sanyabt/np-kg.

Discovering novel drug-supplement interactions using a dietary supplements knowledge graph generated from the biomedical literature

OBJECTIVE: Leverage existing biomedical NLP tools and DS domain terminology to produce a novel and comprehensive knowledge graph containing dietary supplement (DS) information for discovering interactions between DS and drugs, or Drug-Supplement Interactions (DSI). MATERIALS AND METHODS: We created SemRepDS (an extension of SemRep), capable of extracting semantic relations from abstracts by leveraging a DS-specific terminology (iDISK) containing 28,884 DS terms not found in the UMLS. PubMed abstracts were processed using SemRepDS to generate semantic relations, which were then filtered using a PubMedBERT-based model to remove incorrect relations before generating our knowledge graph (SuppKG). Two pathways are used to identify potential DS-Drug interactions which are then evaluated by medical professionals for mechanistic plausibility. RESULTS: Comparison analysis found that SemRepDS returned 206.9% more DS relations and 158.5% more DS entities than SemRep. The fine-tuned BERT model obtained an F1 score of 0.8605 and removed 43.86% of the relations, improving the precision of the relations by 26.4% compared to pre-filtering. SuppKG consists of 2,928 DS-specific nodes. Manual review of findings identified 44 (88%) proposed DS-Gene-Drug and 32 (64%) proposed DS-Gene1-Function-Gene2-Drug pathways to be mechanistically plausible. DISCUSSION: The additional relations extracted using SemRepDS generated SuppKG that was used to find plausible DSI not found in the current literature. By the nature of the SuppKG, these interactions are unlikely to have been found using SemRep without the expanded DS terminology. CONCLUSION: We successfully extend SemRep to include DS information and produce SuppKG which can be used to find potential DS-Drug interactions.

UIUC_BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP Contributions

We propose a cascade of neural models that performs sentence classification, phrase recognition, and triple extraction to automatically structure the scholarly contributions of NLP publications. To identify the most important contribution sentences in a paper, we used a BERT-based classifier with positional features (Subtask 1). A BERT-CRF model was used to recognize and characterize relevant phrases in contribution sentences (Subtask 2). We categorized the triples into several types based on whether and how their elements were expressed in text, and addressed each type using separate BERT-based classifiers as well as rules (Subtask 3). Our system was officially ranked second in Phase 1 evaluation and first in both parts of Phase 2 evaluation. After fixing a submission error in Pharse 1, our approach yields the best results overall. In this paper, in addition to a system description, we also provide further analysis of our results, highlighting its strengths and limitations. We make our code publicly available at https://github.com/Liu-Hy/nlp-contrib-graph.

Drug Repurposing for COVID-19 via Knowledge Graph Completion

Objective: To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods. Methods: We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from both PubMed and COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant, and used this subset to construct a knowledge graph. Five SOTA, neural knowledge graph completion algorithms were used to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach. Results: Accuracy classifier based on PubMedBERT achieved the best performance (F1= 0.854) in classifying semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1=0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as some candidate drugs that have not yet been studied. Discovery patterns enabled generation of plausible hypotheses regarding the relationships between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (paclitaxel, SB 203580, alpha 2-antiplasmin, pyrrolidine dithiocarbamate, and butylated hydroxytoluene) with their mechanistic explanations were further discussed. Conclusion: We show that an LBD approach can be feasible for discovering drug candidates for COVID-19, and for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions.

Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels

Preventable adverse events as a result of medical errors present a growing concern in the healthcare system. As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a machine-processable form is an important step toward effective dissemination of drug safety information. In this study, we tackle the problem of jointly extracting drugs and their interactions, including interaction outcome, from drug labels. Our deep learning approach entails composing various intermediate representations including sequence and graph based context, where the latter is derived using graph convolutions (GC) with a novel attention-based gating mechanism (holistically called GCA). These representations are then composed in meaningful ways to handle all subtasks jointly. To overcome scarcity in training data, we additionally propose transfer learning by pre-training on related DDI data. Our model is trained and evaluated on the 2018 TAC DDI corpus. Our GCA model in conjunction with transfer learning performs at 39.20% F1 and 26.09% F1 on entity recognition (ER) and relation extraction (RE) respectively on the first official test set and at 45.30% F1 and 27.87% F1 on ER and RE respectively on the second official test set corresponding to an improvement over our prior best results by up to 6 absolute F1 points. After controlling for available training data, our model exhibits state-of-the-art performance by improving over the next comparable best outcome by roughly three F1 points in ER and 1.5 F1 points in RE evaluation across two official test sets.

Attention-Gated Graph Convolution for Extracting Drugs and Their Interactions from Drug Labels

Preventable adverse events as a result of medical errors present a growing concern in the healthcare system. As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a machine-processable form is an important step toward effective dissemination of drug safety information. In this study, we tackle the problem of jointly extracting drugs and their interactions, including interaction outcome, from drug labels. Our deep learning approach entails composing various intermediate representations including sequence and graph based context, where the latter is derived using graph convolutions (GC) with a novel attention-based gating mechanism (holistically called GCA). These representations are then composed in meaningful ways to handle all subtasks jointly. To overcome scarcity in training data, we additionally propose transfer learning by pre-training on related DDI data. Our model is trained and evaluated on the 2018 TAC DDI corpus. Our GCA model in conjunction with transfer learning performs at 39.20% F1 and 26.09% F1 on entity recognition (ER) and relation extraction (RE) respectively on the first official test set and at 45.30% F1 and 27.87% F1 on ER and RE respectively on the second official test set corresponding to an improvement over our prior best results by up to 6 absolute F1 points. After controlling for available training data, our model exhibits state-of-the-art performance by improving over the next comparable best outcome by roughly three F1 points in ER and 1.5 F1 points in RE evaluation across two official test sets.

A Multi-Task Learning Framework for Extracting Drugs and Their Interactions from Drug Labels

Preventable adverse drug reactions as a result of medical errors present a growing concern in modern medicine. As drug-drug interactions (DDIs) may cause adverse reactions, being able to extracting DDIs from drug labels into machine-readable form is an important effort in effectively deploying drug safety information. The DDI track of TAC 2018 introduces two large hand-annotated test sets for the task of extracting DDIs from structured product labels with linkage to standard terminologies. Herein, we describe our approach to tackling tasks one and two of the DDI track, which corresponds to named entity recognition (NER) and sentence-level relation extraction respectively. Namely, our approach resembles a multi-task learning framework designed to jointly model various sub-tasks including NER and interaction type and outcome prediction. On NER, our system ranked second (among eight teams) at 33.00% and 38.25% F1 on Test Sets 1 and 2 respectively. On relation extraction, our system ranked second (among four teams) at 21.59% and 23.55% on Test Sets 1 and 2 respectively.