Man Lan

Also published as: Lan Man


2020

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A Span-based Linearization for Constituent Trees
Yang Wei | Yuanbin Wu | Man Lan
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose a novel linearization of a constituent tree, together with a new locally normalized model. For each split point in a sentence, our model computes the normalizer on all spans ending with that split point, and then predicts a tree span from them. Compared with global models, our model is fast and parallelizable. Different from previous local models, our linearization method is tied on the spans directly and considers more local features when performing span prediction, which is more interpretable and effective. Experiments on PTB (95.8 F1) and CTB (92.4 F1) show that our model significantly outperforms existing local models and efficiently achieves competitive results with global models.

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ECNU at SemEval-2020 Task 7: Assessing Humor in Edited News Headlines Using BiLSTM with Attention
Tiantian Zhang | Zhixuan Chen | Man Lan
Proceedings of the Fourteenth Workshop on Semantic Evaluation

In this paper we describe our system submitted to SemEval 2020 Task 7: “Assessing Humor in Edited News Headlines”. We participated in all subtasks, in which the main goal is to predict the mean funniness of the edited headline given the original and the edited headline. Our system involves two similar sub-networks, which generate vector representations for the original and edited headlines respectively. And then we do a subtract operation of the outputs from two sub-networks to predict the funniness of the edited headline.

2019

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Exploring Human Gender Stereotypes with Word Association Test
Yupei Du | Yuanbin Wu | Man Lan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Word embeddings have been widely used to study gender stereotypes in texts. One key problem regarding existing bias scores is to evaluate their validities: do they really reflect true bias levels? For a small set of words (e.g. occupations), we can rely on human annotations or external data. However, for most words, evaluating the correctness of them is still an open problem. In this work, we utilize word association test, which contains rich types of word connections annotated by human participants, to explore how gender stereotypes spread within our minds. Specifically, we use random walk on word association graph to derive bias scores for a large amount of words. Experiments show that these bias scores correlate well with bias in the real world. More importantly, comparing with word-embedding-based bias scores, it provides a different perspective on gender stereotypes in words.

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Joint Type Inference on Entities and Relations via Graph Convolutional Networks
Changzhi Sun | Yeyun Gong | Yuanbin Wu | Ming Gong | Daxin Jiang | Man Lan | Shiliang Sun | Nan Duan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We develop a new paradigm for the task of joint entity relation extraction. It first identifies entity spans, then performs a joint inference on entity types and relation types. To tackle the joint type inference task, we propose a novel graph convolutional network (GCN) running on an entity-relation bipartite graph. By introducing a binary relation classification task, we are able to utilize the structure of entity-relation bipartite graph in a more efficient and interpretable way. Experiments on ACE05 show that our model outperforms existing joint models in entity performance and is competitive with the state-of-the-art in relation performance.

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Graph-based Dependency Parsing with Graph Neural Networks
Tao Ji | Yuanbin Wu | Man Lan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing. Instead of explicitly extracting high-order features from intermediate parse trees, we develop a more powerful dependency tree node representation which captures high-order information concisely and efficiently. We use graph neural networks (GNNs) to learn the representations and discuss several new configurations of GNN’s updating and aggregation functions. Experiments on PTB show that our parser achieves the best UAS and LAS on PTB (96.0%, 94.3%) among systems without using any external resources.

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Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title
Huimin Xu | Wenting Wang | Xin Mao | Xinyu Jiang | Man Lan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Supplementing product information by extracting attribute values from title is a crucial task in e-Commerce domain. Previous studies treat each attribute only as an entity type and build one set of NER tags (e.g., BIO) for each of them, leading to a scalability issue which unfits to the large sized attribute system in real world e-Commerce. In this work, we propose a novel approach to support value extraction scaling up to thousands of attributes without losing performance: (1) We propose to regard attribute as a query and adopt only one global set of BIO tags for any attributes to reduce the burden of attribute tag or model explosion; (2) We explicitly model the semantic representations for attribute and title, and develop an attention mechanism to capture the interactive semantic relations in-between to enforce our framework to be attribute comprehensive. We conduct extensive experiments in real-life datasets. The results show that our model not only outperforms existing state-of-the-art NER tagging models, but also is robust and generates promising results for up to 8,906 attributes.

2018

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ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models
Huimin Xu | Man Lan | Yuanbin Wu
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes our submissions to SemEval 2018 task 1. The task is affect intensity prediction in tweets, including five subtasks. We participated in all subtasks of English tweets. We extracted several traditional NLP, sentiment lexicon, emotion lexicon and domain specific features from tweets, adopted supervised machine learning algorithms to perform emotion intensity prediction.

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ECNU at SemEval-2018 Task 2: Leverage Traditional NLP Features and Neural Networks Methods to Address Twitter Emoji Prediction Task
Xingwu Lu | Xin Mao | Man Lan | Yuanbin Wu
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes our submissions to Task 2 in SemEval 2018, i.e., Multilingual Emoji Prediction. We first investigate several traditional Natural Language Processing (NLP) features, and then design several deep learning models. For subtask 1: Emoji Prediction in English, we combine two different methods to represent tweet, i.e., supervised model using traditional features and deep learning model. For subtask 2: Emoji Prediction in Spanish, we only use deep learning model.

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ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods
Zhenghang Yin | Feixiang Wang | Man Lan | Wenting Wang
Proceedings of The 12th International Workshop on Semantic Evaluation

The paper describes our submissions to task 3 in SemEval-2018. There are two subtasks: Subtask A is a binary classification task to determine whether a tweet is ironic, and Subtask B is a fine-grained classification task including four classes. To address them, we explored supervised machine learning method alone and in combination with neural networks.

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ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection
Yunxiao Zhou | Man Lan | Yuanbin Wu
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes the system we submitted to Task 10 (Capturing Discriminative Attributes) in SemEval 2018. Given a triple (word1, word2, attribute), this task is to predict whether it exemplifies a semantic difference or not. We design and investigate several word embedding features, PMI features and WordNet features together with supervised machine learning methods to address this task. Officially released results show that our system ranks above average.

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ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task
Yixuan Sheng | Man Lan | Yuanbin Wu
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes the system we submitted to the Task 11 in SemEval 2018, i.e., Machine Comprehension using Commonsense Knowledge. Given a passage and some questions that each have two candidate answers, this task requires the participate system to select out one answer meet the meaning of original text or commonsense knowledge from the candidate answers. For this task, we use a deep learning method to obtain final predict answer by calculating relevance of choices representations and question-aware document representation.

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ECNU at SemEval-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension Task
Junfeng Tian | Man Lan | Yuanbin Wu
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper presents our submissions to SemEval 2018 Task 12: the Argument Reasoning Comprehension Task. We investigate an end-to-end attention-based neural network to represent the two lexically close candidate warrants. On the one hand, we extract their different parts as attention vectors to obtain distinguishable representations. On the other hand, we use their surrounds (i.e., claim, reason, debate context) as another attention vectors to get contextual representations, which work as final clues to select the correct warrant. Our model achieves 60.4% accuracy and ranks 3rd among 22 participating systems.

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AntNLP at CoNLL 2018 Shared Task: A Graph-Based Parser for Universal Dependency Parsing
Tao Ji | Yufang Liu | Yijun Wang | Yuanbin Wu | Man Lan
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

We describe the graph-based dependency parser in our system (AntNLP) submitted to the CoNLL 2018 UD Shared Task. We use bidirectional lstm to get the word representation, then a bi-affine pointer networks to compute scores of candidate dependency edges and the MST algorithm to get the final dependency tree. From the official testing results, our system gets 70.90 LAS F1 score (rank 9/26), 55.92 MLAS (10/26) and 60.91 BLEX (8/26).

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Extracting Entities and Relations with Joint Minimum Risk Training
Changzhi Sun | Yuanbin Wu | Man Lan | Shiliang Sun | Wenting Wang | Kuang-Chih Lee | Kewen Wu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We investigate the task of joint entity relation extraction. Unlike prior efforts, we propose a new lightweight joint learning paradigm based on minimum risk training (MRT). Specifically, our algorithm optimizes a global loss function which is flexible and effective to explore interactions between the entity model and the relation model. We implement a strong and simple neural network where the MRT is executed. Experiment results on the benchmark ACE05 and NYT datasets show that our model is able to achieve state-of-the-art joint extraction performances.

2017

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A Fast and Lightweight System for Multilingual Dependency Parsing
Tao Ji | Yuanbin Wu | Man Lan
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

We present a multilingual dependency parser with a bidirectional-LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier. We trained our transition-based projective parser in UD version 2.0 datasets without any additional data. The parser is fast, lightweight and effective on big treebanks. In the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, the official results show that the macro-averaged LAS F1 score of our system Mengest is 61.33%.

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Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network
Changzhi Sun | Yuanbin Wu | Man Lan | Shiliang Sun | Qi Zhang
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We investigate the task of open domain opinion relation extraction. Different from works on manually labeled corpus, we propose an efficient distantly supervised framework based on pattern matching and neural network classifiers. The patterns are designed to automatically generate training data, and the deep learning model is design to capture various lexical and syntactic features. The result algorithm is fast and scalable on large-scale corpus. We test the system on the Amazon online review dataset. The result shows that our model is able to achieve promising performances without any human annotations.

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ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity
Junfeng Tian | Zhiheng Zhou | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features. Our system is further supported by combining with deep learning semantic similarity and our best run achieves the mean Pearson correlation 73.16% in primary track.

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ECNU at SemEval-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task
Guoshun Wu | Yixuan Sheng | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the systems we submitted to the task 3 (Community Question Answering) in SemEval 2017 which contains three subtasks on English corpora, i.e., subtask A: Question-Comment Similarity, subtask B: Question-Question Similarity, and subtask C: Question-External Comment Similarity. For subtask A, we combined two different methods to represent question-comment pair, i.e., supervised model using traditional features and Convolutional Neural Network. For subtask B, we utilized the information of snippets returned from Search Engine with question subject as query. For subtask C, we ranked the comments by multiplying the probability of the pair related question comment being Good by the reciprocal rank of the related question.

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ECNU at SemEval-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate English Puns
Yuhuan Xiu | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our submissions to task 7 in SemEval 2017, i.e., Detection and Interpretation of English Puns. We participated in the first two subtasks, which are to detect and locate English puns respectively. For subtask 1, we presented a supervised system to determine whether or not a sentence contains a pun using similarity features calculated on sense vectors or cluster center vectors. For subtask 2, we established an unsupervised system to locate the pun by scoring each word in the sentence and we assumed that the word with the smallest score is the pun.

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ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models
Feixiang Wang | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our submissions to task 8 in SemEval 2017, i.e., Determining rumour veracity and support for rumours. Given a rumoured tweet and a lot of reply tweets, the subtask A is to label whether these tweets are support, deny, query or comment, and the subtask B aims to predict the veracity (i.e., true, false, and unverified) with a confidence (in range of 0-1) of the given rumoured tweet. For both subtasks, we adopted supervised machine learning methods, incorporating rich features. Since training data is imbalanced, we specifically designed a two-step classifier to address subtask A .

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ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification
Yunxiao Zhou | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine learning methods to address this task. Officially released results showed that our system ranked above average.

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ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain
Mengxiao Jiang | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our systems submitted to the Fine-Grained Sentiment Analysis on Financial Microblogs and News task (i.e., Task 5) in SemEval-2017. This task includes two subtasks in microblogs and news headline domain respectively. To settle this problem, we extract four types of effective features, including linguistic features, sentiment lexicon features, domain-specific features and word embedding features. Then we employ these features to construct models by using ensemble regression algorithms. Our submissions rank 1st and rank 5th in subtask 1 and subtask 2 respectively.

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Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification
Man Lan | Jianxiang Wang | Yuanbin Wu | Zheng-Yu Niu | Haifeng Wang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present a novel multi-task attention based neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attention based neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora. The extensive experiments have been performed on two benchmark corpora (i.e., PDTB and CoNLL-2016 datasets). Experimental results show that our proposed model outperforms the state-of-the-art systems on benchmark corpora.

2016

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Two End-to-end Shallow Discourse Parsers for English and Chinese in CoNLL-2016 Shared Task
Jianxiang Wang | Man Lan
Proceedings of the CoNLL-16 shared task

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ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter
Yunxiao Zhou | Zhihua Zhang | Man Lan
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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ECNU at SemEval-2016 Task 5: Extracting Effective Features from Relevant Fragments in Sentence for Aspect-Based Sentiment Analysis in Reviews
Mengxiao Jiang | Zhihua Zhang | Man Lan
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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ECNU at SemEval 2016 Task 6: Relevant or Not? Supportive or Not? A Two-step Learning System for Automatic Detecting Stance in Tweets
Zhihua Zhang | Man Lan
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking
Feixiang Wang | Zhihua Zhang | Man Lan
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity
Junfeng Tian | Man Lan
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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ECNU at SemEval-2016 Task 3: Exploring Traditional Method and Deep Learning Method for Question Retrieval and Answer Ranking in Community Question Answering
Guoshun Wu | Man Lan
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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A Refined End-to-End Discourse Parser
Jianxiang Wang | Man Lan
Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

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ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in Twitter
Jiang Zhao | Man Lan
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation
Jiang Zhao | Man Lan | Jun Feng Tian
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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ECNU: Using Multiple Sources of CQA-based Information for Answers Selection and YES/NO Response Inference
Liang Yi | JianXiang Wang | Man Lan
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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ECNU: Multi-level Sentiment Analysis on Twitter Using Traditional Linguistic Features and Word Embedding Features
Zhihua Zhang | Guoshun Wu | Man Lan
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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ECNU: Extracting Effective Features from Multiple Sequential Sentences for Target-dependent Sentiment Analysis in Reviews
Zhihua Zhang | Man Lan
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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ECNU: A Combination Method and Multiple Features for Aspect Extraction and Sentiment Polarity Classification
Fangxi Zhang | Zhihua Zhang | Man Lan
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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ECNU: Expression- and Message-level Sentiment Orientation Classification in Twitter Using Multiple Effective Features
Jiang Zhao | Man Lan | Tiantian Zhu
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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ECNU: Leveraging on Ensemble of Heterogeneous Features and Information Enrichment for Cross Level Semantic Similarity Estimation
Tiantian Zhu | Man Lan
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment
Jiang Zhao | Tiantian Zhu | Man Lan
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

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Leveraging Synthetic Discourse Data via Multi-task Learning for Implicit Discourse Relation Recognition
Man Lan | Yu Xu | Zhengyu Niu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Probabilistic Sense Sentiment Similarity through Hidden Emotions
Mitra Mohtarami | Man Lan | Chew Lim Tan
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements
Tiantian Zhu | Lan Man
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

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ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures
Jiang Zhao | Man Lan | Zheng-Yu Niu
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

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ECNUCS: A Surface Information Based System Description of Sentiment Analysis in Twitter in the SemEval-2013 (Task 2)
Tiantian Zhu | Fangxi Zhang | Lan Man
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

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Tiantianzhu7:System Description of Semantic Textual Similarity (STS) in the SemEval-2012 (Task 6)
Tiantian Zhu | Man Lan
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

2010

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The Effects of Discourse Connectives Prediction on Implicit Discourse Relation Recognition
Zhi Min Zhou | Man Lan | Zheng Yu Niu | Yu Xu | Jian Su
Proceedings of the SIGDIAL 2010 Conference

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Predicting Discourse Connectives for Implicit Discourse Relation Recognition
Zhi-Min Zhou | Yu Xu | Zheng-Yu Niu | Man Lan | Jian Su | Chew Lim Tan
Coling 2010: Posters

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ECNU: Effective Semantic Relations Classification without Complicated Features or Multiple External Corpora
Yuan Chen | Man Lan | Jian Su | Zhi Min Zhou | Yu Xu
Proceedings of the 5th International Workshop on Semantic Evaluation