pwc-archive/papers-with-abstracts huggingface dataset:
Joint Learning of Local and Global Features for Entity Linking via Neural Networks
Authors: Thien Huu Nguyen, Nicolas Fauceglia, Mariano Rodriguez Muro, Oktie Hassanzadeh, Alfio Massimiliano Gliozzo, Mohammad Sadoghi
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Domain Adaptation, Entity Linking

Previous studies have highlighted the necessity for entity linking systems to capture the local entity-mention similarities and the global topical coherence. We introduce a novel framework based on convolutional neural networks and recurrent neural networks to simultaneously model the local and global features for entity linking. The proposed model benefits from the capacity of convolutional neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. Our evaluation on multiple datasets demonstrates the effectiveness of the model and yields the state-of-the-art performance on such datasets. In addition, we examine the entity linking systems on the domain adaptation setting that further demonstrates the cross-domain robustness of the proposed model.
Structured Aspect Extraction
Authors: Omer Gunes, Tim Furche, Giorgio Orsi
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Aspect-Based Sentiment Analysis (ABSA), Aspect Extraction, Clustering, Computational Efficiency, Information Retrieval, Opinion Mining, Retrieval, Sentiment Analysis

Aspect extraction identifies relevant features from a textual description of an entity, e.g., a phone, and is typically targeted to product descriptions, reviews, and other short texts as an enabling task for, e.g., opinion mining and information retrieval. Current aspect extraction methods mostly focus on aspect terms and often neglect interesting modifiers of the term or embed them in the aspect term without proper distinction. Moreover, flat syntactic structures are often assumed, resulting in inaccurate extractions of complex aspects. This paper studies the problem of structured aspect extraction, a variant of traditional aspect extraction aiming at a fine-grained extraction of complex (i.e., hierarchical) aspects. We propose an unsupervised and scalable method for structured aspect extraction consisting of statistical noun phrase clustering, cPMI-based noun phrase segmentation, and hierarchical pattern induction. Our evaluation shows a substantial improvement over existing methods in terms of both quality and computational efficiency.
Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings
Authors: Simon Baker, Douwe Kiela, Anna Korhonen
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Classification, Feature Engineering, General Classification, Named Entity Recognition (NER), Sentence, text-classification, Text Classification

The conventional solution for handling sparsely labelled data is extensive feature engineering. This is time consuming and task and domain specific. We present a novel approach for learning embedded features that aims to alleviate this problem. Our approach jointly learns embeddings at different levels of granularity (word, sentence and document) along with the class labels. The intuition is that topic semantics represented by embeddings at multiple levels results in better classification. We evaluate this approach in unsupervised and semi-supervised settings on two sparsely labelled classification tasks, outperforming the handcrafted models and several embedding baselines.
Mathematical Information Retrieval based on Type Embeddings and Query Expansion
Authors: Yiannos Stathopoulos, Simone Teufel
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Information Retrieval, Language Modelling, Retrieval, Vocal Bursts Type Prediction

We present an approach to mathematical information retrieval (MIR) that exploits a special kind of technical terminology, referred to as a mathematical type. In this paper, we present and evaluate a type detection mechanism and show its positive effect on the retrieval of research-level mathematics. Our best model, which performs query expansion with a type-aware embedding space, strongly outperforms standard IR models with state-of-the-art query expansion (vector space-based and language modelling-based), on a relatively new corpus of research-level queries.
Text Retrieval by Term Co-occurrences in a Query-based Vector Space
Authors: Eriks Sneiders
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Retrieval, Sentence, Text Categorization, Text Matching, Text Retrieval

Term co-occurrence in a sentence or paragraph is a powerful and often overlooked feature for text matching in document retrieval. In our experiments with matching email-style query messages to webpages, such term co-occurrence helped greatly to filter and rank documents, compared to matching document-size bags-of-words. The paper presents the results of the experiments as well as a text-matching model where the query shapes the vector space, a document is modelled by two or three vectors in this vector space, and the query-document similarity score depends on the length of the vectors and the relationships between them.
Pairwise Relation Classification with Mirror Instances and a Combined Convolutional Neural Network
Authors: Jianfei Yu, Jing Jiang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Classification, General Classification, Knowledge Base Population, Opinion Mining, Question Answering, Relation, Relation Classification, Word Embeddings

Relation classification is the task of classifying the semantic relations between entity pairs in text. Observing that existing work has not fully explored using different representations for relation instances, especially in order to better handle the asymmetry of relation types, in this paper, we propose a neural network based method for relation classification that combines the raw sequence and the shortest dependency path representations of relation instances and uses mirror instances to perform pairwise relation classification. We evaluate our proposed models on the SemEval-2010 Task 8 dataset. The empirical results show that with two additional features, our model achieves the state-of-the-art result of F1 score of 85.7.
FastHybrid: A Hybrid Model for Efficient Answer Selection
Authors: Lidan Wang, Ming Tan, Jiawei Han
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Answer Selection, Information Retrieval, Open-Domain Question Answering, Question Answering, Retrieval

Answer selection is a core component in any question-answering systems. It aims to select correct answer sentences for a given question from a pool of candidate sentences. In recent years, many deep learning methods have been proposed and shown excellent results for this task. However, these methods typically require extensive parameter (and hyper-parameter) tuning, which give rise to efficiency issues for large-scale datasets, and potentially make them less portable across new datasets and domains (as re-tuning is usually required). In this paper, we propose an extremely efficient hybrid model (FastHybrid) that tackles the problem from both an accuracy and scalability point of view. FastHybrid is a light-weight model that requires little tuning and adaptation across different domains. It combines a fast deep model (which will be introduced in the method section) with an initial information retrieval model to effectively and efficiently handle answer selection. We introduce a new efficient attention mechanism in the hybrid model and demonstrate its effectiveness on several QA datasets. Experimental results show that although the hybrid uses no training data, its accuracy is often on-par with supervised deep learning techniques, while significantly reducing training and tuning costs across different domains.
Extracting Spatial Entities and Relations in Korean Text
Authors: Bogyum Kim, Jae Sung Lee
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Entity Extraction using GAN, Named Entity Recognition (NER), Question Answering, Relation, Relation Extraction, Robot Navigation

A spatial information extraction system retrieves spatial entities and their relationships for geological searches and reasoning. Spatial information systems have been developed mainly for English text, e.g., through the SpaceEval competition. Some of the techniques are useful but not directly applicable to Korean text, because of linguistic differences and the lack of language resources. In this paper, we propose a Korean spatial entity extraction model and a spatial relation extraction model; the spatial entity extraction model uses word vectors to alleviate the over generation and the spatial relation extraction mod-el uses dependency parse labels to find the proper arguments in relations. Experiments with Korean text show that the two models are effective for spatial information extraction.
Hybrid Question Answering over Knowledge Base and Free Text
Authors: Kun Xu, Yansong Feng, Songfang Huang, Dongyan Zhao
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Information Retrieval, Question Answering, Retrieval, Semantic Parsing

Recent trend in question answering (QA) systems focuses on using structured knowledge bases (KBs) to find answers. While these systems are able to provide more precise answers than information retrieval (IR) based QA systems, the natural incompleteness of KB inevitably limits the question scope that the system can answer. In this paper, we present a hybrid question answering (hybrid-QA) system which exploits both structured knowledge base and free text to answer a question. The main challenge is to recognize the meaning of a question using these two resources, i.e., structured KB and free text. To address this, we map relational phrases to KB predicates and textual relations simultaneously, and further develop an integer linear program (ILP) model to infer on these candidates and provide a globally optimal solution. Experiments on benchmark datasets show that our system can benefit from both structured KB and free text, outperforming the state-of-the-art systems.
Improved Word Embeddings with Implicit Structure Information
Authors: Jie Shen, Cong Liu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Dependency Parsing, Language Modeling, Language Modelling, Learning Word Embeddings, Machine Translation, Part-Of-Speech Tagging, Sentiment Analysis, Word Embeddings, Word Similarity

Distributed word representation is an efficient method for capturing semantic and syntactic word relations. In this work, we introduce an extension to the continuous bag-of-words model for learning word representations efficiently by using implicit structure information. Instead of relying on a syntactic parser which might be noisy and slow to build, we compute weights representing probabilities of syntactic relations based on the Huffman softmax tree in an efficient heuristic. The constructed {``}implicit graphs{''} from these weights show that these weights contain useful implicit structure information. Extensive experiments performed on several word similarity and word analogy tasks show gains compared to the basic continuous bag-of-words model.
Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification
Authors: Abdelghani Dahou, Shengwu Xiong, Junwei Zhou, Mohamed Houcine Haddoud, Pengfei Duan
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: General Classification, Sentiment Analysis, Sentiment Classification, Word Embeddings

With the development and the advancement of social networks, forums, blogs and online sales, a growing number of Arabs are expressing their opinions on the web. In this paper, a scheme of Arabic sentiment classification, which evaluates and detects the sentiment polarity from Arabic reviews and Arabic social media, is studied. We investigated in several architectures to build a quality neural word embeddings using a 3.4 billion words corpus from a collected 10 billion words web-crawled corpus. Moreover, a convolutional neural network trained on top of pre-trained Arabic word embeddings is used for sentiment classification to evaluate the quality of these word embeddings. The simulation results show that the proposed scheme outperforms the existed methods on 4 out of 5 balanced and unbalanced datasets.
Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts
Authors: Xingyou Wang, Weijie Jiang, Zhiyong Luo
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Information Retrieval, Sentiment Analysis, Speech Recognition, Word Embeddings

Sentiment analysis of short texts is challenging because of the limited contextual information they usually contain. In recent years, deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to text sentiment analysis with comparatively remarkable results. In this paper, we describe a jointed CNN and RNN architecture, taking advantage of the coarse-grained local features generated by CNN and long-distance dependencies learned via RNN for sentiment analysis of short texts. Experimental results show an obvious improvement upon the state-of-the-art on three benchmark corpora, MR, SST1 and SST2, with 82.28{\%}, 51.50{\%} and 89.95{\%} accuracy, respectively.
Tweet Sarcasm Detection Using Deep Neural Network
Authors: Meishan Zhang, Yue Zhang, Guohong Fu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Sarcasm Detection

Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets. Results show that neural features give improved accuracies for sarcasm detection, with different error distributions compared with discrete manual features.
Agreement and Disagreement: Comparison of Points of View in the Political Domain
Authors: Stefano Menini, Sara Tonelli
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Position, Sentiment Analysis

The automated comparison of points of view between two politicians is a very challenging task, due not only to the lack of annotated resources, but also to the different dimensions participating to the definition of agreement and disagreement. In order to shed light on this complex task, we first carry out a pilot study to manually annotate the components involved in detecting agreement and disagreement. Then, based on these findings, we implement different features to capture them automatically via supervised classification. We do not focus on debates in dialogical form, but we rather consider sets of documents, in which politicians may express their position with respect to different topics in an implicit or explicit way, like during an electoral campaign. We create and make available three different datasets.
Targeted Sentiment to Understand Student Comments
Authors: Charles Welch, Rada Mihalcea
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Decision Making, Entity Extraction using GAN, Sentiment Analysis

We address the task of targeted sentiment as a means of understanding the sentiment that students hold toward courses and instructors, as expressed by students in their comments. We introduce a new dataset consisting of student comments annotated for targeted sentiment and describe a system that can both identify the courses and instructors mentioned in student comments, as well as label the students{'} sentiment toward those entities. Through several comparative evaluations, we show that our system outperforms previous work on a similar task.
Constraint-Based Question Answering with Knowledge Graph
Authors: Junwei Bao, Nan Duan, Zhao Yan, Ming Zhou, Tiejun Zhao
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Question Answering

WebQuestions and SimpleQuestions are two benchmark data-sets commonly used in recent knowledge-based question answering (KBQA) work. Most questions in them are {`}simple{'} questions which can be answered based on a single relation in the knowledge base. Such data-sets lack the capability of evaluating KBQA systems on complicated questions. Motivated by this issue, we release a new data-set, namely ComplexQuestions, aiming to measure the quality of KBQA systems on {`}multi-constraint{'} questions which require multiple knowledge base relations to get the answer. Beside, we propose a novel systematic KBQA approach to solve multi-constraint questions. Compared to state-of-the-art methods, our approach not only obtains comparable results on the two existing benchmark data-sets, but also achieves significant improvements on the ComplexQuestions.
Selecting Sentences versus Selecting Tree Constituents for Automatic Question Ranking
Authors: Alberto Barr{\'o}n-Cede{\~n}o, Giovanni Da San Martino, Salvatore Romeo, Aless Moschitti, ro
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Community Question Answering, Machine Translation, Question Answering, Sentence

Community question answering (cQA) websites are focused on users who query questions onto an online forum, expecting for other users to provide them answers or suggestions. Unlike other social media, the length of the posted queries has no limits and queries tend to be multi-sentence elaborations combining context, actual questions, and irrelevant information. We approach the problem of question ranking: given a user{'}s new question, to retrieve those previously-posted questions which could be equivalent, or highly relevant. This could prevent the posting of nearly-duplicate questions and provide the user with instantaneous answers. For the first time in cQA, we address the selection of relevant text {---}both at sentence- and at constituent-level{---} for parse tree-based representations. Our supervised models for text selection boost the performance of a tree kernel-based machine learning model, allowing it to overtake the current state of the art on a recently released cQA evaluation framework.
Attention-Based Convolutional Neural Network for Semantic Relation Extraction
Authors: Yatian Shen, Xuanjing Huang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Feature Engineering, General Classification, Position, Relation, Relation Classification, Relation Extraction, Sentence, TAG

Nowadays, neural networks play an important role in the task of relation classification. In this paper, we propose a novel attention-based convolutional neural network architecture for this task. Our model makes full use of word embedding, part-of-speech tag embedding and position embedding information. Word level attention mechanism is able to better determine which parts of the sentence are most influential with respect to the two entities of interest. This architecture enables learning some important features from task-specific labeled data, forgoing the need for external knowledge such as explicit dependency structures. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model achieves better performances than several state-of-the-art neural network models and can achieve a competitive performance just with minimal feature engineering.
Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction
Authors: Pankaj Gupta, Hinrich Sch{\"u}tze, Bernt Andrassy
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Classification, Entity Extraction using GAN, General Classification, Joint Entity and Relation Extraction, Relation, Relation Classification, Relation Extraction, Semantic Composition, Sentence, Structured Prediction

This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies. The proposed neural network architecture is capable of modeling multiple relation instances without knowing the corresponding relation arguments in a sentence. The experimental results show that a simple approach of piggybacking candidate entities to model the label dependencies from relations to entities improves performance. We present state-of-the-art results with improvements of 2.0{\%} and 2.7{\%} for entity recognition and relation classification, respectively on CoNLL04 dataset.
Bilingual Autoencoders with Global Descriptors for Modeling Parallel Sentences
Authors: Biao Zhang, Deyi Xiong, Jinsong Su, Hong Duan, Min Zhang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Information Retrieval, Machine Translation, Sentence, Translation, Word Embeddings

Parallel sentence representations are important for bilingual and cross-lingual tasks in natural language processing. In this paper, we explore a bilingual autoencoder approach to model parallel sentences. We extract sentence-level global descriptors (e.g. min, max) from word embeddings, and construct two monolingual autoencoders over these descriptors on the source and target language. In order to tightly connect the two autoencoders with bilingual correspondences, we force them to share the same decoding parameters and minimize a corpus-level semantic distance between the two languages. Being optimized towards a joint objective function of reconstruction and semantic errors, our bilingual antoencoder is able to learn continuous-valued latent representations for parallel sentences. Experiments on both intrinsic and extrinsic evaluations on statistical machine translation tasks show that our autoencoder achieves substantial improvements over the baselines.
Multi-Engine and Multi-Alignment Based Automatic Post-Editing and its Impact on Translation Productivity
Authors: Santanu Pal, Sudip Kumar Naskar, Josef van Genabith
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Automatic Post-Editing, Machine Translation, Translation

In this paper we combine two strands of machine translation (MT) research: automatic post-editing (APE) and multi-engine (system combination) MT. APE systems learn a target-language-side second stage MT system from the data produced by human corrected output of a first stage MT system, to improve the output of the first stage MT in what is essentially a sequential MT system combination architecture. At the same time, there is a rich research literature on parallel MT system combination where the same input is fed to multiple engines and the best output is selected or smaller sections of the outputs are combined to obtain improved translation output. In the paper we show that parallel system combination in the APE stage of a sequential MT-APE combination yields substantial translation improvements both measured in terms of automatic evaluation metrics as well as in terms of productivity improvements measured in a post-editing experiment. We also show that system combination on the level of APE alignments yields further improvements. Overall our APE system yields statistically significant improvement of 5.9{\%} relative BLEU over a strong baseline (English{--}Italian Google MT) and 21.76{\%} productivity increase in a human post-editing experiment with professional translators.