Qi Li


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Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning
Kang Zhou | Yuepei Li | Qi Li
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we study the named entity recognition (NER) problem under distant supervision. Due to the incompleteness of the external dictionaries and/or knowledge bases, such distantly annotated training data usually suffer from a high false negative rate. To this end, we formulate the Distantly Supervised NER (DS-NER) problem via Multi-class Positive and Unlabeled (MPU) learning and propose a theoretically and practically novel CONFidence-based MPU (Conf-MPU) approach. To handle the incomplete annotations, Conf-MPU consists of two steps. First, a confidence score is estimated for each token of being an entity token. Then, the proposed Conf-MPU risk estimation is applied to train a multi-class classifier for the NER task. Thorough experiments on two benchmark datasets labeled by various external knowledge demonstrate the superiority of the proposed Conf-MPU over existing DS-NER methods. Our code is available at Github.


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Few-shot Intent Classification and Slot Filling with Retrieved Examples
Dian Yu | Luheng He | Yuan Zhang | Xinya Du | Panupong Pasupat | Qi Li
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain. In this paper, we explore retrieval-based methods for intent classification and slot filling tasks in few-shot settings. Retrieval-based methods make predictions based on labeled examples in the retrieval index that are similar to the input, and thus can adapt to new domains simply by changing the index without having to retrain the model. However, it is non-trivial to apply such methods on tasks with a complex label space like slot filling. To this end, we propose a span-level retrieval method that learns similar contextualized representations for spans with the same label via a novel batch-softmax objective. At inference time, we use the labels of the retrieved spans to construct the final structure with the highest aggregated score. Our method outperforms previous systems in various few-shot settings on the CLINC and SNIPS benchmarks.

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QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining
Xinya Du | Luheng He | Qi Li | Dian Yu | Panupong Pasupat | Yuan Zhang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Slot-filling is an essential component for building task-oriented dialog systems. In this work, we focus on the zero-shot slot-filling problem, where the model needs to predict slots and their values, given utterances from new domains without training on the target domain. Prior methods directly encode slot descriptions to generalize to unseen slot types. However, raw slot descriptions are often ambiguous and do not encode enough semantic information, limiting the models’ zero-shot capability. To address this problem, we introduce QA-driven slot filling (QASF), which extracts slot-filler spans from utterances with a span-based QA model. We use a linguistically motivated questioning strategy to turn descriptions into questions, allowing the model to generalize to unseen slot types. Moreover, our QASF model can benefit from weak supervision signals from QA pairs synthetically generated from unlabeled conversations. Our full system substantially outperforms baselines by over 5% on the SNIPS benchmark.


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OptSLA: an Optimization-Based Approach for Sequential Label Aggregation
Nasim Sabetpour | Adithya Kulkarni | Qi Li
Findings of the Association for Computational Linguistics: EMNLP 2020

The need for the annotated training dataset on which data-hungry machine learning algorithms feed has increased dramatically with advanced acclaim of machine learning applications. To annotate the data, people with domain expertise are needed, but they are seldom available and expensive to hire. This has lead to the thriving of crowdsourcing platforms such as Amazon Mechanical Turk (AMT). However, the annotations provided by one worker cannot be used directly to train the model due to the lack of expertise. Existing literature in annotation aggregation focuses on binary and multi-choice problems. In contrast, little work has been done on complex tasks such as sequence labeling with imbalanced classes, a ubiquitous task in Natural Language Processing (NLP), and Bio-Informatics. We propose OptSLA, an Optimization-based Sequential Label Aggregation method, that jointly considers the characteristics of sequential labeling tasks, workers reliabilities, and advanced deep learning techniques to conquer the challenge. We evaluate our model on crowdsourced data for named entity recognition task. Our results show that the proposed OptSLA outperforms the state-of-the-art aggregation methods, and the results are easier to interpret.

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EVIDENCEMINER: Textual Evidence Discovery for Life Sciences
Xuan Wang | Yingjun Guan | Weili Liu | Aabhas Chauhan | Enyi Jiang | Qi Li | David Liem | Dibakar Sigdel | John Caufield | Peipei Ping | Jiawei Han
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Traditional search engines for life sciences (e.g., PubMed) are designed for document retrieval and do not allow direct retrieval of specific statements. Some of these statements may serve as textual evidence that is key to tasks such as hypothesis generation and new finding validation. We present EVIDENCEMINER, a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. EVIDENCEMINER is constructed in a completely automated way without any human effort for training data annotation. It is supported by novel data-driven methods for distantly supervised named entity recognition and open information extraction. The entities and patterns are pre-computed and indexed offline to support fast online evidence retrieval. The annotation results are also highlighted in the original document for better visualization. EVIDENCEMINER also includes analytic functionalities such as the most frequent entity and relation summarization. EVIDENCEMINER can help scientists uncover important research issues, leading to more effective research and more in-depth quantitative analysis. The system of EVIDENCEMINER is available at https://evidenceminer.firebaseapp.com/.


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Improving Relation Extraction with Knowledge-attention
Pengfei Li | Kezhi Mao | Xuefeng Yang | Qi Li
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

While attention mechanisms have been proven to be effective in many NLP tasks, majority of them are data-driven. We propose a novel knowledge-attention encoder which incorporates prior knowledge from external lexical resources into deep neural networks for relation extraction task. Furthermore, we present three effective ways of integrating knowledge-attention with self-attention to maximize the utilization of both knowledge and data. The proposed relation extraction system is end-to-end and fully attention-based. Experiment results show that the proposed knowledge-attention mechanism has complementary strengths with self-attention, and our integrated models outperform existing CNN, RNN, and self-attention based models. State-of-the-art performance is achieved on TACRED, a complex and large-scale relation extraction dataset.


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Recognizing Salient Entities in Shopping Queries
Zornitsa Kozareva | Qi Li | Ke Zhai | Weiwei Guo
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Discourse Parsing with Attention-based Hierarchical Neural Networks
Qi Li | Tianshi Li | Baobao Chang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


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Seed-Based Event Trigger Labeling: How far can event descriptions get us?
Ofer Bronstein | Ido Dagan | Qi Li | Heng Ji | Anette Frank
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)


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Comparison of the Impact of Word Segmentation on Name Tagging for Chinese and Japanese
Haibo Li | Masato Hagiwara | Qi Li | Heng Ji
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Word Segmentation is usually considered an essential step for many Chinese and Japanese Natural Language Processing tasks, such as name tagging. This paper presents several new observations and analysis on the impact of word segmentation on name tagging; (1). Due to the limitation of current state-of-the-art Chinese word segmentation performance, a character-based name tagger can outperform its word-based counterparts for Chinese but not for Japanese; (2). It is crucial to keep segmentation settings (e.g. definitions, specifications, methods) consistent between training and testing for name tagging; (3). As long as (2) is ensured, the performance of word segmentation does not have appreciable impact on Chinese and Japanese name tagging.

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Constructing Information Networks Using One Single Model
Qi Li | Heng Ji | Yu Hong | Sujian Li
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Incremental Joint Extraction of Entity Mentions and Relations
Qi Li | Heng Ji
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Joint Event Extraction via Structured Prediction with Global Features
Qi Li | Heng Ji | Liang Huang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Name-aware Machine Translation
Haibo Li | Jing Zheng | Heng Ji | Qi Li | Wen Wang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Cross-lingual Slot Filling from Comparable Corpora
Matthew Snover | Xiang Li | Wen-Pin Lin | Zheng Chen | Suzanne Tamang | Mingmin Ge | Adam Lee | Qi Li | Hao Li | Sam Anzaroot | Heng Ji
Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web