Xiang Li


2022

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Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)
Antoine Bosselut | Xiang Li | Bill Yuchen Lin | Vered Shwartz | Bodhisattwa Prasad Majumder | Yash Kumar Lal | Rachel Rudinger | Xiang Ren | Niket Tandon | Vilém Zouhar
Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)

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BIT-Xiaomi’s System for AutoSimTrans 2022
Mengge Liu | Xiang Li | Bao Chen | Yanzhi Tian | Tianwei Lan | Silin Li | Yuhang Guo | Jian Luan | Bin Wang
Proceedings of the Third Workshop on Automatic Simultaneous Translation

This system paper describes the BIT-Xiaomi simultaneous translation system for Autosimtrans 2022 simultaneous translation challenge. We participated in three tracks: the Zh-En text-to-text track, the Zh-En audio-to-text track and the En-Es test-to-text track. In our system, wait-k is employed to train prefix-to-prefix translation models. We integrate streaming chunking to detect boundaries as the source streaming read in. We further improve our system with data selection, data-augmentation and R-drop training methods. Results show that our wait-k implementation outperforms organizer’s baseline by 8 BLEU score at most, and our proposed streaming chunking method further improves about 2 BLEU in low latency regime.

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The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022
Bao Guo | Mengge Liu | Wen Zhang | Hexuan Chen | Chang Mu | Xiang Li | Jianwei Cui | Bin Wang | Yuhang Guo
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This system paper describes the Xiaomi Translation System for the IWSLT 2022 Simultaneous Speech Translation (noted as SST) shared task. We participate in the English-to-Mandarin Chinese Text-to-Text (noted as T2T) track. Our system is built based on the Transformer model with novel techniques borrowed from our recent research work. For the data filtering, language-model-based and rule-based methods are conducted to filter the data to obtain high-quality bilingual parallel corpora. We also strengthen our system with some dominating techniques related to data augmentation, such as knowledge distillation, tagged back-translation, and iterative back-translation. We also incorporate novel training techniques such as R-drop, deep model, and large batch training which have been shown to be beneficial to the naive Transformer model. In the SST scenario, several variations of extttwait-k strategies are explored. Furthermore, in terms of robustness, both data-based and model-based ways are used to reduce the sensitivity of our system to Automatic Speech Recognition (ASR) outputs. We finally design some inference algorithms and use the adaptive-ensemble method based on multiple model variants to further improve the performance of the system. Compared with strong baselines, fusing all techniques can improve our system by 2 extasciitilde3 BLEU scores under different latency regimes.

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JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection
Bin Liang | Qinglin Zhu | Xiang Li | Min Yang | Lin Gui | Yulan He | Ruifeng Xu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Zero-shot stance detection (ZSSD) aims to detect the stance for an unseen target during the inference stage. In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastive learning and target-aware prototypical graph contrastive learning. Specifically, a stance contrastive learning strategy is employed to better generalize stance features for unseen targets. Further, we build a prototypical graph for each instance to learn the target-based representation, in which the prototypes are deployed as a bridge to share the graph structures between the known targets and the unseen ones. Then a novel target-aware prototypical graph contrastive learning strategy is devised to generalize the reasoning ability of target-based stance representations to the unseen targets. Extensive experiments on three benchmark datasets show that the proposed approach achieves state-of-the-art performance in the ZSSD task.

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Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network
Bin Liang | Chenwei Lou | Xiang Li | Min Yang | Lin Gui | Yulan He | Wenjie Pei | Ruifeng Xu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

With the increasing popularity of posting multimodal messages online, many recent studies have been carried out utilizing both textual and visual information for multi-modal sarcasm detection. In this paper, we investigate multi-modal sarcasm detection from a novel perspective by constructing a cross-modal graph for each instance to explicitly draw the ironic relations between textual and visual modalities. Specifically, we first detect the objects paired with descriptions of the image modality, enabling the learning of important visual information. Then, the descriptions of the objects are served as a bridge to determine the importance of the association between the objects of image modality and the contextual words of text modality, so as to build a cross-modal graph for each multi-modal instance. Furthermore, we devise a cross-modal graph convolutional network to make sense of the incongruity relations between modalities for multi-modal sarcasm detection. Extensive experimental results and in-depth analysis show that our model achieves state-of-the-art performance in multi-modal sarcasm detection.

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Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings
Shib Dasgupta | Michael Boratko | Siddhartha Mishra | Shriya Atmakuri | Dhruvesh Patel | Xiang Li | Andrew McCallum
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Learning representations of words in a continuous space is perhaps the most fundamental task in NLP, however words interact in ways much richer than vector dot product similarity can provide. Many relationships between words can be expressed set-theoretically, for example, adjective-noun compounds (eg. “red cars”⊆“cars”) and homographs (eg. “tongue”∩“body” should be similar to “mouth”, while “tongue”∩“language” should be similar to “dialect”) have natural set-theoretic interpretations. Box embeddings are a novel region-based representation which provide the capability to perform these set-theoretic operations. In this work, we provide a fuzzy-set interpretation of box embeddings, and learn box representations of words using a set-theoretic training objective. We demonstrate improved performance on various word similarity tasks, particularly on less common words, and perform a quantitative and qualitative analysis exploring the additional unique expressivity provided by Word2Box.

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A Neural Network Architecture for Program Understanding Inspired by Human Behaviors
Renyu Zhu | Lei Yuan | Xiang Li | Ming Gao | Wenyuan Cai
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Program understanding is a fundamental task in program language processing. Despite the success, existing works fail to take human behaviors as reference in understanding programs. In this paper, we consider human behaviors and propose the PGNN-EK model that consists of two main components. On the one hand, inspired by the “divide-and-conquer” reading behaviors of humans, we present a partitioning-based graph neural network model PGNN on the upgraded AST of codes. On the other hand, to characterize human behaviors of resorting to other resources to help code comprehension, we transform raw codes with external knowledge and apply pre-training techniques for information extraction. Finally, we combine the two embeddings generated from the two components to output code embeddings. We conduct extensive experiments to show the superior performance of PGNN-EK on the code summarization and code clone detection tasks. In particular, to show the generalization ability of our model, we release a new dataset that is more challenging for code clone detection and could advance the development of the community. Our codes and data are publicly available at https://github.com/RecklessRonan/PGNN-EK.

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Lexical Knowledge Internalization for Neural Dialog Generation
Zhiyong Wu | Wei Bi | Xiang Li | Lingpeng Kong | Ben Kao
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We propose knowledge internalization (KI), which aims to complement the lexical knowledge into neural dialog models. Instead of further conditioning the knowledge-grounded dialog (KGD) models on externally retrieved knowledge, we seek to integrate knowledge about each input token internally into the model’s parameters. To tackle the challenge due to the large scale of lexical knowledge, we adopt the contrastive learning approach and create an effective token-level lexical knowledge retriever that requires only weak supervision mined from Wikipedia. We demonstrate the effectiveness and general applicability of our approach on various datasets and diversified model structures.

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Weakly Supervised Text Classification using Supervision Signals from a Language Model
Ziqian Zeng | Weimin Ni | Tianqing Fang | Xiang Li | Xinran Zhao | Yangqiu Song
Findings of the Association for Computational Linguistics: NAACL 2022

Solving text classification in a weakly supervised manner is important for real-world applications where human annotations are scarce. In this paper, we propose to query a masked language model with cloze style prompts to obtain supervision signals. We design a prompt which combines the document itself and “this article is talking about [MASK].” A masked language model can generate words for the [MASK] token. The generated words which summarize the content of a document can be utilized as supervision signals. We propose a latent variable model to learn a word distribution learner which associates generated words to pre-defined categories and a document classifier simultaneously without using any annotated data. Evaluation on three datasets, AGNews, 20Newsgroups, and UCINews, shows that our method can outperform baselines by 2%, 4%, and 3%.

2021

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Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation
Zhiyong Wu | Lingpeng Kong | Wei Bi | Xiang Li | Ben Kao
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

A neural multimodal machine translation (MMT) system is one that aims to perform better translation by extending conventional text-only translation models with multimodal information. Many recent studies report improvements when equipping their models with the multimodal module, despite the controversy of whether such improvements indeed come from the multimodal part. We revisit the contribution of multimodal information in MMT by devising two interpretable MMT models. To our surprise, although our models replicate similar gains as recently developed multimodal-integrated systems achieved, our models learn to ignore the multimodal information. Upon further investigation, we discover that the improvements achieved by the multimodal models over text-only counterparts are in fact results of the regularization effect. We report empirical findings that highlight the importance of MMT models’ interpretability, and discuss how our findings will benefit future research.

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HITSZ-HLT at SemEval-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection
Qinglin Zhu | Zijie Lin | Yice Zhang | Jingyi Sun | Xiang Li | Qihui Lin | Yixue Dang | Ruifeng Xu
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection. This task aims to locate those spans that attribute to the text’s toxicity within a text, which is crucial for semi-automated moderation in online discussions. We formalize this task as the Sequence Labeling (SL) problem and the Span Boundary Detection (SBD) problem separately and employ three state-of-the-art models. Next, we integrate predictions of these models to produce a more credible and complement result. Our system achieves a char-level score of 70.83%, ranking 1/91. In addition, we also explore the lexicon-based method, which is strongly interpretable and flexible in practice.

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融合情感分析的隐式反问句识别模型(Implicit Rhetorical Questions Recognition Model Combined with Sentiment Analysis)
Xiang Li (李翔) | Chengwei Liu (刘承伟) | Xiaoxu Zhu (朱晓旭)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

反问是现代汉语中一种常用的修辞手法,根据是否含有反问标记可分为显式反问句与隐式反问句。其中隐式反问句表达的情感更为丰富,表现形式也十分复杂,对隐式反问句的识别更具挑战性。本文首先扩充了汉语反问句语料库,语料库规模达到10000余句,接着针对隐式反问句的特点,提出了一种融合情感分析的隐式反问句识别模型。模型考虑了句子的语义信息,上下文信息,并借助情感分析任务辅助识别隐式反问句。实验结果表明,本文提出的模型在隐式反问句识别任务上取得了良好的性能。

2020

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Xiaomi’s Submissions for IWSLT 2020 Open Domain Translation Task
Yuhui Sun | Mengxue Guo | Xiang Li | Jianwei Cui | Bin Wang
Proceedings of the 17th International Conference on Spoken Language Translation

This paper describes the Xiaomi’s submissions to the IWSLT20 shared open domain translation task for Chinese<->Japanese language pair. We explore different model ensembling strategies based on recent Transformer variants. We also further strengthen our systems via some effective techniques, such as data filtering, data selection, tagged back translation, domain adaptation, knowledge distillation, and re-ranking. Our resulting Chinese->Japanese primary system ranked second in terms of character-level BLEU score among all submissions. Our resulting Japanese->Chinese primary system also achieved a competitive performance.

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ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
Michael Boratko | Xiang Li | Tim O’Gorman | Rajarshi Das | Dan Le | Andrew McCallum
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Given questions regarding some prototypical situation — such as Name something that people usually do before they leave the house for work? — a human can easily answer them via acquired experiences. There can be multiple right answers for such questions, with some more common for a situation than others. This paper introduces a new question answering dataset for training and evaluating common sense reasoning capabilities of artificial intelligence systems in such prototypical situations. The training set is gathered from an existing set of questions played in a long-running international trivia game show – Family Feud. The hidden evaluation set is created by gathering answers for each question from 100 crowd-workers. We also propose a generative evaluation task where a model has to output a ranked list of answers, ideally covering all prototypical answers for a question. After presenting multiple competitive baseline models, we find that human performance still exceeds model scores on all evaluation metrics with a meaningful gap, supporting the challenging nature of the task.

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Modeling Discourse Structure for Document-level Neural Machine Translation
Junxuan Chen | Xiang Li | Jiarui Zhang | Chulun Zhou | Jianwei Cui | Bin Wang | Jinsong Su
Proceedings of the First Workshop on Automatic Simultaneous Translation

Recently, document-level neural machine translation (NMT) has become a hot topic in the community of machine translation. Despite its success, most of existing studies ignored the discourse structure information of the input document to be translated, which has shown effective in other tasks. In this paper, we propose to improve document-level NMT with the aid of discourse structure information. Our encoder is based on a hierarchical attention network (HAN) (Miculicich et al., 2018). Specifically, we first parse the input document to obtain its discourse structure. Then, we introduce a Transformer-based path encoder to embed the discourse structure information of each word. Finally, we combine the discourse structure information with the word embedding before it is fed into the encoder. Experimental results on the English-to-German dataset show that our model can significantly outperform both Transformer and Transformer+HAN.

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ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems
Qi Zhu | Zheng Zhang | Yan Fang | Xiang Li | Ryuichi Takanobu | Jinchao Li | Baolin Peng | Jianfeng Gao | Xiaoyan Zhu | Minlie Huang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems. As the successor of ConvLab, ConvLab-2 inherits ConvLab’s framework but integrates more powerful dialogue models and supports more datasets. Besides, we have developed an analysis tool and an interactive tool to assist researchers in diagnosing dialogue systems. The analysis tool presents rich statistics and summarizes common mistakes from simulated dialogues, which facilitates error analysis and system improvement. The interactive tool provides an user interface that allows developers to diagnose an assembled dialogue system by interacting with the system and modifying the output of each system component.

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Reading Comprehension as Natural Language Inference:A Semantic Analysis
Anshuman Mishra | Dhruvesh Patel | Aparna Vijayakumar | Xiang Li | Pavan Kapanipathi | Kartik Talamadupula
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited and has not been well studied. Therefore, in this paper, we explore the utility of NLI for one of the most prominent downstream tasks, viz. Question Answering (QA). We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms. We propose new characterizations of questions, and evaluate the performance of QA and NLI models on these categories. We highlight clear categories for which the model is able to perform better when the data is presented in a coherent entailment form, and a structured question-answer concatenation form, respectively.

2019

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ConvLab: Multi-Domain End-to-End Dialog System Platform
Sungjin Lee | Qi Zhu | Ryuichi Takanobu | Zheng Zhang | Yaoqin Zhang | Xiang Li | Jinchao Li | Baolin Peng | Xiujun Li | Minlie Huang | Jianfeng Gao
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments. ConvLab offers a set of fully annotated datasets and associated pre-trained reference models. As a showcase, we extend the MultiWOZ dataset with user dialog act annotations to train all component models and demonstrate how ConvLab makes it easy and effortless to conduct complicated experiments in multi-domain end-to-end dialog settings.

2018

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Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures
Luke Vilnis | Xiang Li | Shikhar Murty | Andrew McCallum
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Embedding methods which enforce a partial order or lattice structure over the concept space, such as Order Embeddings (OE), are a natural way to model transitive relational data (e.g. entailment graphs). However, OE learns a deterministic knowledge base, limiting expressiveness of queries and the ability to use uncertainty for both prediction and learning (e.g. learning from expectations). Probabilistic extensions of OE have provided the ability to somewhat calibrate these denotational probabilities while retaining the consistency and inductive bias of ordered models, but lack the ability to model the negative correlations found in real-world knowledge. In this work we show that a broad class of models that assign probability measures to OE can never capture negative correlation, which motivates our construction of a novel box lattice and accompanying probability measure to capture anti-correlation and even disjoint concepts, while still providing the benefits of probabilistic modeling, such as the ability to perform rich joint and conditional queries over arbitrary sets of concepts, and both learning from and predicting calibrated uncertainty. We show improvements over previous approaches in modeling the Flickr and WordNet entailment graphs, and investigate the power of the model.

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Sound Signal Processing with Seq2Tree Network
Weicheng Ma | Kai Cao | Zhaoheng Ni | Peter Chin | Xiang Li
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Few-Shot Charge Prediction with Discriminative Legal Attributes
Zikun Hu | Xiang Li | Cunchao Tu | Zhiyuan Liu | Maosong Sun
Proceedings of the 27th International Conference on Computational Linguistics

Automatic charge prediction aims to predict the final charges according to the fact descriptions in criminal cases and plays a crucial role in legal assistant systems. Existing works on charge prediction perform adequately on those high-frequency charges but are not yet capable of predicting few-shot charges with limited cases. Moreover, these exist many confusing charge pairs, whose fact descriptions are fairly similar to each other. To address these issues, we introduce several discriminative attributes of charges as the internal mapping between fact descriptions and charges. These attributes provide additional information for few-shot charges, as well as effective signals for distinguishing confusing charges. More specifically, we propose an attribute-attentive charge prediction model to infer the attributes and charges simultaneously. Experimental results on real-work datasets demonstrate that our proposed model achieves significant and consistent improvements than other state-of-the-art baselines. Specifically, our model outperforms other baselines by more than 50% in the few-shot scenario. Our codes and datasets can be obtained from https://github.com/thunlp/attribute_charge.

2016

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Commonsense Knowledge Base Completion
Xiang Li | Aynaz Taheri | Lifu Tu | Kevin Gimpel
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Improving Event Detection with Abstract Meaning Representation
Xiang Li | Thien Huu Nguyen | Kai Cao | Ralph Grishman
Proceedings of the First Workshop on Computing News Storylines

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Improving Event Detection with Active Learning
Kai Cao | Xiang Li | Miao Fan | Ralph Grishman
Proceedings of the International Conference Recent Advances in Natural Language Processing

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Improving Event Detection with Dependency Regularization
Kai Cao | Xiang Li | Ralph Grishman
Proceedings of the International Conference Recent Advances in Natural Language Processing

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Tackling Sparsity, the Achilles Heel of Social Networks: Language Model Smoothing via Social Regularization
Rui Yan | Xiang Li | Mengwen Liu | Xiaohua Hu
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)

2013

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Confidence Estimation for Knowledge Base Population
Xiang Li | Ralph Grishman
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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Towards Fine-grained Citation Function Classification
Xiang Li | Yifan He | Adam Meyers | Ralph Grishman
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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Iterative Transformation of Annotation Guidelines for Constituency Parsing
Xiang Li | Wenbin Jiang | Yajuan Lü | Qun Liu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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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

2010

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Annotating Event Chains for Carbon Sequestration Literature
Heng Ji | Xiang Li | Angelo Lucia | Jianting Zhang
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In this paper we present a project of annotating event chains for an important scientific domain ― carbon sequestration. This domain aims to reduce carbon emissions and has been identified by the U.S. National Academy of Engineering (NAE) as a grand challenge problem for the 21st century. Given a collection of scientific literature, we identify a set of centroid experiments; and then link and order the observations and events centered around these experiments on temporal or causal chains. We describe the fundamental challenges on annotations and our general solutions to address them. We expect that our annotation efforts will produce significant advances in inter-operability through new information extraction techniques and permit scientists to build knowledge that will provide better understanding of important scientific challenges in this domain, share and re-use of diverse data sets and experimental results in a more efficient manner. In addition, the annotations of metadata and ontology for these literature will provide important support for data lifecycle activities.

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Domain-Independent Novel Event Discovery and Semi-Automatic Event Annotation
Hao Li | Xiang Li | Heng Ji | Yuval Marton
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation

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