pwc-archive/papers-with-abstracts huggingface dataset:
LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network
Authors: V{\'\i}ctor Su{\'a}rez-Paniagua, Isabel Segura-Bedmar, Paloma Mart{\'\i}nez
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Articles, General Classification, Lemmatization, Relation, Relation Classification, Relation Extraction, Sentence Classification

In this paper, we describe our participation at the subtask of extraction of relationships between two identified keyphrases. This task can be very helpful in improving search engines for scientific articles. Our approach is based on the use of a convolutional neural network (CNN) trained on the training dataset. This deep learning model has already achieved successful results for the extraction relationships between named entities. Thus, our hypothesis is that this model can be also applied to extract relations between keyphrases. The official results of the task show that our architecture obtained an F1-score of 0.38{\%} for Keyphrases Relation Classification. This performance is lower than the expected due to the generic preprocessing phase and the basic configuration of the CNN model, more complex architectures are proposed as future work to increase the classification rate.
WING-NUS at SemEval-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling
Authors: Animesh Prasad, Min-Yen Kan
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Clustering, General Classification, Keyphrase Extraction, Relation

We describe an end-to-end pipeline processing approach for SemEval 2017{'}s Task 10 to extract keyphrases and their relations from scientific publications. We jointly identify and classify keyphrases by modeling the subtasks as sequential labeling. Our system utilizes standard, surface-level features along with the adjacent word features, and performs conditional decoding on whole text to extract keyphrases. We focus only on the identification and typing of keyphrases (Subtasks A and B, together referred as extraction), but provide an end-to-end system inclusive of keyphrase relation identification (Subtask C) for completeness. Our top performing configuration achieves an $F_1$ of 0.27 for the end-to-end keyphrase extraction and relation identification scenario on the final test data, and compares on par to other top ranked systems for keyphrase extraction. Our system outperforms other techniques that do not employ global decoding and hence do not account for dependencies between keyphrases. We believe this is crucial for keyphrase classification in the given context of scientific document mining.
TTI-COIN at SemEval-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers
Authors: Tomoki Tsujimura, Makoto Miwa, Yutaka Sasaki
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Classification, General Classification, Relation, Relation Classification, Relation Extraction, Word Embeddings

This paper describes our TTI-COIN system that participated in SemEval-2017 Task 10. We investigated appropriate embeddings to adapt a neural end-to-end entity and relation extraction system LSTM-ER to this task. We participated in the full task setting of the entity segmentation, entity classification and relation classification (scenario 1) and the setting of relation classification only (scenario 3). The system was directly applied to the scenario 1 without modifying the codes thanks to its generality and flexibility. Our evaluation results show that the choice of appropriate pre-trained embeddings affected the performance significantly. With the best embeddings, our system was ranked third in the scenario 1 with the micro F1 score of 0.38. We also confirm that our system can produce the micro F1 score of 0.48 for the scenario 3 on the test data, and this score is close to the score of the 3rd ranked system in the task.
SZTE-NLP at SemEval-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation
Authors: G{\'a}bor Berend
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Clustering, Keyphrase Extraction, Named Entity Recognition (NER), Part-Of-Speech Tagging, Word Embeddings

In this paper we introduce our system participating at the 2017 SemEval shared task on keyphrase extraction from scientific documents. We aimed at the creation of a keyphrase extraction approach which relies on as little external resources as possible. Without applying any hand-crafted external resources, and only utilizing a transformed version of word embeddings trained at Wikipedia, our proposed system manages to perform among the best participating systems in terms of precision.
LIPN at SemEval-2017 Task 10: Filtering Candidate Keyphrases from Scientific Publications with Part-of-Speech Tag Sequences to Train a Sequence Labeling Model
Authors: Hern, Simon David ez, Davide Buscaldi, Thierry Charnois
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: TAG

This paper describes the system used by the team LIPN in SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications. The team participated in Scenario 1, that includes three subtasks, Identification of keyphrases (Subtask A), Classification of identified keyphrases (Subtask B) and Extraction of relationships between two identified keyphrases (Subtask C). The presented system was mainly focused on the use of part-of-speech tag sequences to filter candidate keyphrases for Subtask A. Subtasks A and B were addressed as a sequence labeling problem using Conditional Random Fields (CRFs) and even though Subtask C was out of the scope of this approach, one rule was included to identify synonyms.
EUDAMU at SemEval-2017 Task 11: Action Ranking and Type Matching for End-User Development
Authors: Marek Kubis, Pawe{\l} Sk{\'o}rzewski, Tomasz Zi{\k{e}}tkiewicz
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Action Detection

The paper describes a system for end-user development using natural language. Our approach uses a ranking model to identify the actions to be executed followed by reference and parameter matching models to select parameter values that should be set for the given commands. We discuss the results of evaluation and possible improvements for future work.
Hitachi at SemEval-2017 Task 12: System for temporal information extraction from clinical notes
Authors: Sarath P R, Manik R, an, Yoshiki Niwa
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Domain Adaptation, Question Answering, Temporal Information Extraction, Text Classification, Unsupervised Domain Adaptation

This paper describes the system developed for the task of temporal information extraction from clinical narratives in the context of the 2017 Clinical TempEval challenge. Clinical TempEval 2017 addressed the problem of temporal reasoning in the clinical domain by providing annotated clinical notes, pathology and radiology reports in line with Clinical TempEval challenges 2015/16, across two different evaluation phases focusing on cross domain adaptation. Our team focused on subtasks involving extractions of temporal spans and relations for which the developed systems showed average F-score of 0.45 and 0.47 across the two phases of evaluations.
NTU-1 at SemEval-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation
Authors: Po-Yu Huang, Hen-Hsen Huang, Yu-Wun Wang, Ching Huang, Hsin-Hsi Chen
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Domain Adaptation, General Classification

This study proposes a system to participate in the Clinical TempEval 2017 shared task, a part of the SemEval 2017 Tasks. Domain adaptation was the main challenge this year. We took part in the supervised domain adaption where data of 591 records of colon cancer patients and 30 records of brain cancer patients from Mayo clinic were given and we are asked to analyze the records from brain cancer patients. Based on the THYME corpus released by the organizer of Clinical TempEval, we propose a framework that automatically analyzes clinical temporal events in a fine-grained level. Support vector machine (SVM) and conditional random field (CRF) were implemented in our system for different subtasks, including detecting clinical relevant events and time expression, determining their attributes, and identifying their relations with each other within the document. The results showed the capability of domain adaptation of our system.
ULISBOA at SemEval-2017 Task 12: Extraction and classification of temporal expressions and events
Authors: Andre Lamurias, Diana Sousa, Sofia Pereira, Luka Clarke, Francisco M. Couto
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Attribute, General Classification

This paper presents our approach to participate in the SemEval 2017 Task 12: Clinical TempEval challenge, specifically in the event and time expressions span and attribute identification subtasks (ES, EA, TS, TA). Our approach consisted in training Conditional Random Fields (CRF) classifiers using the provided annotations, and in creating manually curated rules to classify the attributes of each event and time expression. We used a set of common features for the event and time CRF classifiers, and a set of features specific to each type of entity, based on domain knowledge. Training only on the source domain data, our best F-scores were 0.683 and 0.485 for event and time span identification subtasks. When adding target domain annotations to the training data, the best F-scores obtained were 0.729 and 0.554, for the same subtasks. We obtained the second highest F-score of the challenge on the event polarity subtask (0.708). The source code of our system, Clinical Timeline Annotation (CiTA), is available at \url{https://github.com/lasigeBioTM/CiTA}.
GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction
Authors: Sean MacAvaney, Arman Cohan, Nazli Goharian
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Domain Adaptation, Information Retrieval, Relation Extraction, Temporal Information Extraction

Clinical TempEval 2017 (SemEval 2017 Task 12) addresses the task of cross-domain temporal extraction from clinical text. We present a system for this task that uses supervised learning for the extraction of temporal expression and event spans with corresponding attributes and narrative container relations. Approaches include conditional random fields and decision tree ensembles, using lexical, syntactic, semantic, distributional, and rule-based features. Our system received best or second best scores in TIMEX3 span, EVENT span, and CONTAINS relation extraction.
KULeuven-LIIR at SemEval-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical Records
Authors: Artuur Leeuwenberg, Marie-Francine Moens
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: All, Domain Adaptation, Relation Extraction, Temporal Information Extraction, Unsupervised Domain Adaptation

In this paper, we describe the system of the KULeuven-LIIR submission for Clinical TempEval 2017. We participated in all six subtasks, using a combination of Support Vector Machines (SVM) for event and temporal expression detection, and a structured perceptron for extracting temporal relations. Moreover, we present and analyze the results from our submissions, and verify the effectiveness of several system components. Our system performed above average for all subtasks in both phases.
Monolingual Phrase Alignment on Parse Forests
Authors: Yuki Arase, Junichi Tsujii
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Semantic Textual Similarity, Sentence Embeddings

We propose an efficient method to conduct phrase alignment on parse forests for paraphrase detection. Unlike previous studies, our method identifies syntactic paraphrases under linguistically motivated grammar. In addition, it allows phrases to non-compositionally align to handle paraphrases with non-homographic phrase correspondences. A dataset that provides gold parse trees and their phrase alignments is created. The experimental results confirm that the proposed method conducts highly accurate phrase alignment compared to human performance.
Quasi-Second-Order Parsing for 1-Endpoint-Crossing, Pagenumber-2 Graphs
Authors: Junjie Cao, Sheng Huang, Weiwei Sun, Xiaojun Wan
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: ARC, Dependency Parsing

We propose a new Maximum Subgraph algorithm for first-order parsing to 1-endpoint-crossing, pagenumber-2 graphs. Our algorithm has two characteristics: (1) it separates the construction for noncrossing edges and crossing edges; (2) in a single construction step, whether to create a new arc is deterministic. These two characteristics make our algorithm relatively easy to be extended to incorporiate crossing-sensitive second-order features. We then introduce a new algorithm for quasi-second-order parsing. Experiments demonstrate that second-order features are helpful for Maximum Subgraph parsing.
Position-aware Attention and Supervised Data Improve Slot Filling
Authors: Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, Christopher D. Manning
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Form, Knowledge Base Population, Knowledge Graphs, Position, Relation, Relation Extraction, slot-filling, Slot Filling

Organized relational knowledge in the form of {``}knowledge graphs{''} is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly slowly. This paper simultaneously addresses two issues that have held back prior work. We first propose an effective new model, which combines an LSTM sequence model with a form of entity position-aware attention that is better suited to relation extraction. Then we build TACRED, a large (119,474 examples) supervised relation extraction dataset obtained via crowdsourcing and targeted towards TAC KBP relations. The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance. When the model trained on this new dataset replaces the previous relation extraction component of the best TAC KBP 2015 slot filling system, its F1 score increases markedly from 22.2{\%} to 26.7{\%}.
Integrating Order Information and Event Relation for Script Event Prediction
Authors: Zhongqing Wang, Yue Zhang, Ching-Yun Chang
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Language Modeling, Language Modelling, Relation

There has been a recent line of work automatically learning scripts from unstructured texts, by modeling narrative event chains. While the dominant approach group events using event pair relations, LSTMs have been used to encode full chains of narrative events. The latter has the advantage of learning long-range temporal orders, yet the former is more adaptive to partial orders. We propose a neural model that leverages the advantages of both methods, by using LSTM hidden states as features for event pair modelling. A dynamic memory network is utilized to automatically induce weights on existing events for inferring a subsequent event. Standard evaluation shows that our method significantly outperforms both methods above, giving the best results reported so far.
Train-O-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data
Authors: Tommaso Pasini, Roberto Navigli
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Word Sense Disambiguation

Annotating large numbers of sentences with senses is the heaviest requirement of current Word Sense Disambiguation. We present Train-O-Matic, a language-independent method for generating millions of sense-annotated training instances for virtually all meanings of words in a language{'}s vocabulary. The approach is fully automatic: no human intervention is required and the only type of human knowledge used is a WordNet-like resource. Train-O-Matic achieves consistently state-of-the-art performance across gold standard datasets and languages, while at the same time removing the burden of manual annotation. All the training data is available for research purposes at \url{http://trainomatic.org}.
Towards Decoding as Continuous Optimisation in Neural Machine Translation
Authors: Cong Duy Vu Hoang, Gholamreza Haffari, Trevor Cohn
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, NMT, Reranking, Translation

We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. We reformulate decoding, a discrete optimization problem, into a continuous problem, such that optimization can make use of efficient gradient-based techniques. Our powerful decoding framework allows for more accurate decoding for standard neural machine translation models, as well as enabling decoding in intractable models such as intersection of several different NMT models. Our empirical results show that our decoding framework is effective, and can leads to substantial improvements in translations, especially in situations where greedy search and beam search are not feasible. Finally, we show how the technique is highly competitive with, and complementary to, reranking.
Where is Misty? Interpreting Spatial Descriptors by Modeling Regions in Space
Authors: Nikita Kitaev, Dan Klein
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Minecraft, Semantic Segmentation, Sentence

We present a model for locating regions in space based on natural language descriptions. Starting with a 3D scene and a sentence, our model is able to associate words in the sentence with regions in the scene, interpret relations such as {`}on top of{'} or {`}next to,{'} and finally locate the region described in the sentence. All components form a single neural network that is trained end-to-end without prior knowledge of object segmentation. To evaluate our model, we construct and release a new dataset consisting of Minecraft scenes with crowdsourced natural language descriptions. We achieve a 32{\%} relative error reduction compared to a strong neural baseline.
Neural Net Models of Open-domain Discourse Coherence
Authors: Jiwei Li, Dan Jurafsky
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Abstractive Text Summarization, Question Answering, Sentence Embeddings, Text Generation

Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. Yet existing models of coherence focus on measuring individual aspects of coherence (lexical overlap, rhetorical structure, entity centering) in narrow domains. In this paper, we describe domain-independent neural models of discourse coherence that are capable of measuring multiple aspects of coherence in existing sentences and can maintain coherence while generating new sentences. We study both discriminative models that learn to distinguish coherent from incoherent discourse, and generative models that produce coherent text, including a novel neural latent-variable Markovian generative model that captures the latent discourse dependencies between sentences in a text. Our work achieves state-of-the-art performance on multiple coherence evaluations, and marks an initial step in generating coherent texts given discourse contexts.
Affinity-Preserving Random Walk for Multi-Document Summarization
Authors: Kexiang Wang, Tianyu Liu, Zhifang Sui, Baobao Chang
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Diversity, Document Summarization, Multi-Document Summarization, Text Summarization

Multi-document summarization provides users with a short text that summarizes the information in a set of related documents. This paper introduces affinity-preserving random walk to the summarization task, which preserves the affinity relations of sentences by an absorbing random walk model. Meanwhile, we put forward adjustable affinity-preserving random walk to enforce the diversity constraint of summarization in the random walk process. The ROUGE evaluations on DUC 2003 topic-focused summarization task and DUC 2004 generic summarization task show the good performance of our method, which has the best ROUGE-2 recall among the graph-based ranking methods.
Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics
Authors: Zhe Zhao, Tao Liu, Shen Li, Bofang Li, Xiaoyong Du
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Language Modeling, Language Modelling, Word Embeddings

The existing word representation methods mostly limit their information source to word co-occurrence statistics. In this paper, we introduce ngrams into four representation methods: SGNS, GloVe, PPMI matrix, and its SVD factorization. Comprehensive experiments are conducted on word analogy and similarity tasks. The results show that improved word representations are learned from ngram co-occurrence statistics. We also demonstrate that the trained ngram representations are useful in many aspects such as finding antonyms and collocations. Besides, a novel approach of building co-occurrence matrix is proposed to alleviate the hardware burdens brought by ngrams.
Dict2vec : Learning Word Embeddings using Lexical Dictionaries
Authors: Julien Tissier, Christophe Gravier, Amaury Habrard
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: General Classification, Knowledge Graphs, Learning Word Embeddings, Machine Translation, Reading Comprehension, Semantic Role Labeling, text-classification, Text Classification, Word Embeddings, Word Sense Disambiguation, Word Similarity

Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks. The most efficient and popular approaches learn or retrofit such representations using additional external data. Resulting embeddings are generally better than their corpus-only counterparts, although such resources cover a fraction of words in the vocabulary. In this paper, we propose a new approach, Dict2vec, based on one of the largest yet refined datasource for describing words {--} natural language dictionaries. Dict2vec builds new word pairs from dictionary entries so that semantically-related words are moved closer, and negative sampling filters out pairs whose words are unrelated in dictionaries. We evaluate the word representations obtained using Dict2vec on eleven datasets for the word similarity task and on four datasets for a text classification task.
Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components
Authors: Jinxing Yu, Xun Jian, Hao Xin, Yangqiu Song
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Named Entity Recognition (NER), Question Answering, Sentiment Analysis, Text Classification, Word Embeddings, Word Similarity

Word embeddings have attracted much attention recently. Different from alphabetic writing systems, Chinese characters are often composed of subcharacter components which are also semantically informative. In this work, we propose an approach to jointly embed Chinese words as well as their characters and fine-grained subcharacter components. We use three likelihoods to evaluate whether the context words, characters, and components can predict the current target word, and collected 13,253 subcharacter components to demonstrate the existing approaches of decomposing Chinese characters are not enough. Evaluation on both word similarity and word analogy tasks demonstrates the superior performance of our model.