pwc-archive/papers-with-abstracts huggingface dataset:
Modeling Context-sensitive Selectional Preference with Distributed Representations
Authors: Naoya Inoue, Yuichiroh Matsubayashi, Masayuki Ono, Naoaki Okazaki, Kentaro Inui
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Semantic Role Labeling

This paper proposes a novel problem setting of selectional preference (SP) between a predicate and its arguments, called as context-sensitive SP (CSP). CSP models the narrative consistency between the predicate and preceding contexts of its arguments, in addition to the conventional SP based on semantic types. Furthermore, we present a novel CSP model that extends the neural SP model (Van de Cruys, 2014) to incorporate contextual information into the distributed representations of arguments. Experimental results demonstrate that the proposed CSP model successfully learns CSP and outperforms the conventional SP model in coreference cluster ranking.
Exploring the value space of attributes: Unsupervised bidirectional clustering of adjectives in German
Authors: Wiebke Petersen, Oliver Hellwig
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Clustering

The paper presents an iterative bidirectional clustering of adjectives and nouns based on a co-occurrence matrix. The clustering method combines a Vector Space Models (VSM) and the results of a Latent Dirichlet Allocation (LDA), whose results are merged in each iterative step. The aim is to derive a clustering of German adjectives that reflects latent semantic classes of adjectives, and that can be used to induce frame-based representations of nouns in a later step. We are able to show that the method induces meaningful groups of adjectives, and that it outperforms a baseline k-means algorithm.
Parameter estimation of Japanese predicate argument structure analysis model using eye gaze information
Authors: Ryosuke Maki, Hitoshi Nishikawa, Takenobu Tokunaga
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: parameter estimation

In this paper, we propose utilising eye gaze information for estimating parameters of a Japanese predicate argument structure (PAS) analysis model. We employ not only linguistic information in the text, but also the information of annotator eye gaze during their annotation process. We hypothesise that annotator{'}s frequent looks at certain candidates imply their plausibility of being the argument of the predicate. Based on this hypothesis, we consider annotator eye gaze for estimating the model parameters of the PAS analysis. The evaluation experiment showed that introducing eye gaze information increased the accuracy of the PAS analysis by 0.05 compared with the conventional methods.
Reading and Thinking: Re-read LSTM Unit for Textual Entailment Recognition
Authors: Lei Sha, Baobao Chang, Zhifang Sui, Sujian Li
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Information Retrieval, Machine Translation, Natural Language Inference, Question Answering, RTE, Word Alignment

Recognizing Textual Entailment (RTE) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate deep neural network methods for the RTE task. Previous neural network based methods usually try to encode the two sentences (premise and hypothesis) and send them together into a multi-layer perceptron to get their entailment type, or use LSTM-RNN to link two sentences together while using attention mechanic to enhance the model{'}s ability. In this paper, we propose to use the re-read mechanic, which means to read the premise again and again while reading the hypothesis. After read the premise again, the model can get a better understanding of the premise, which can also affect the understanding of the hypothesis. On the contrary, a better understanding of the hypothesis can also affect the understanding of the premise. With the alternative re-read process, the model can {``}think{''} of a better decision of entailment type. We designed a new LSTM unit called re-read LSTM (rLSTM) to implement this {``}thinking{''} process. Experiments show that we achieve results better than current state-of-the-art equivalents.
A Paraphrase and Semantic Similarity Detection System for User Generated Short-Text Content on Microblogs
Authors: Kuntal Dey, Ritvik Shrivastava, Saroj Kaushik
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Semantic Similarity, Semantic Textual Similarity

Existing systems deliver high accuracy and F1-scores for detecting paraphrase and semantic similarity on traditional clean-text corpus. For instance, on the clean-text Microsoft Paraphrase benchmark database, the existing systems attain an accuracy as high as 0:8596. However, existing systems for detecting paraphrases and semantic similarity on user-generated short-text content on microblogs such as Twitter, comprising of noisy and ad hoc short-text, needs significant research attention. In this paper, we propose a machine learning based approach towards this. We propose a set of features that, although well-known in the NLP literature for solving other problems, have not been explored for detecting paraphrase or semantic similarity, on noisy user-generated short-text data such as Twitter. We apply support vector machine (SVM) based learning. We use the benchmark Twitter paraphrase data, released as a part of SemEval 2015, for experiments. Our system delivers a paraphrase detection F1-score of 0.717 and semantic similarity detection F1-score of 0.741, thereby significantly outperforming the existing systems, that deliver F1-scores of 0.696 and 0.724 for the two problems respectively. Our features also allow us to obtain a rank among the top-10, when trained on the Microsoft Paraphrase corpus and tested on the corresponding test data, thereby empirically establishing our approach as ubiquitous across the different paraphrase detection databases.
Modeling Extractive Sentence Intersection via Subtree Entailment
Authors: Omer Levy, Ido Dagan, Gabriel Stanovsky, Judith Eckle-Kohler, Iryna Gurevych
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Abstractive Text Summarization, Natural Language Inference, Sentence, text similarity

Sentence intersection captures the semantic overlap of two texts, generalizing over paradigms such as textual entailment and semantic text similarity. Despite its modeling power, it has received little attention because it is difficult for non-experts to annotate. We analyze 200 pairs of similar sentences and identify several underlying properties of sentence intersection. We leverage these insights to design an algorithm that decomposes the sentence intersection task into several simpler annotation tasks, facilitating the construction of a high quality dataset via crowdsourcing. We implement this approach and provide an annotated dataset of 1,764 sentence intersections.
Context-Sensitive Inference Rule Discovery: A Graph-Based Method
Authors: Xianpei Han, Le Sun
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Natural Language Inference, Question Answering

Inference rule discovery aims to identify entailment relations between predicates, e.g., {`}X acquire Y {--}{\textgreater} X purchase Y{'} and {`}X is author of Y {--}{\textgreater} X write Y{'}. Traditional methods dis-cover inference rules by computing distributional similarities between predicates, with each predicate is represented as one or more feature vectors of its instantiations. These methods, however, have two main drawbacks. Firstly, these methods are mostly context-insensitive, cannot accurately measure the similarity between two predicates in a specific context. Secondly, traditional methods usually model predicates independently, ignore the rich inter-dependencies between predicates. To address the above two issues, this pa-per proposes a graph-based method, which can discover inference rules by effectively modelling and exploiting both the context and the inter-dependencies between predicates. Specifically, we propose a graph-based representation{---}Predicate Graph, which can capture the semantic relevance between predicates using both the predicate-feature co-occurrence statistics and the inter-dependencies between predicates. Based on the predicate graph, we propose a context-sensitive random walk algorithm, which can learn con-text-specific predicate representations by distinguishing context-relevant information from context-irrelevant information. Experimental results show that our method significantly outperforms traditional inference rule discovery methods.
English-Chinese Knowledge Base Translation with Neural Network
Authors: Xiaocheng Feng, Duyu Tang, Bing Qin, Ting Liu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Information Retrieval, Machine Translation, Translation

Knowledge base (KB) such as Freebase plays an important role for many natural language processing tasks. English knowledge base is obviously larger and of higher quality than low resource language like Chinese. To expand Chinese KB by leveraging English KB resources, an effective way is to translate English KB (source) into Chinese (target). In this direction, two major challenges are to model triple semantics and to build a robust KB translator. We address these challenges by presenting a neural network approach, which learns continuous triple representation with a gated neural network. Accordingly, source triples and target triples are mapped in the same semantic vector space. We build a new dataset for English-Chinese KB translation from Freebase, and compare with several baselines on it. Experimental results show that the proposed method improves translation accuracy compared with baseline methods. We show that adaptive composition model improves standard solution such as neural tensor network in terms of translation accuracy.
What's in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams
Authors: Peter Jansen, Niranjan Balasubramanian, Mihai Surdeanu, Peter Clark
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Knowledge Base Construction, Question Answering, Retrieval

QA systems have been making steady advances in the challenging elementary science exam domain. In this work, we develop an explanation-based analysis of knowledge and inference requirements, which supports a fine-grained characterization of the challenges. In particular, we model the requirements based on appropriate sources of evidence to be used for the QA task. We create requirements by first identifying suitable sentences in a knowledge base that support the correct answer, then use these to build explanations, filling in any necessary missing information. These explanations are used to create a fine-grained categorization of the requirements. Using these requirements, we compare a retrieval and an inference solver on 212 questions. The analysis validates the gains of the inference solver, demonstrating that it answers more questions requiring complex inference, while also providing insights into the relative strengths of the solvers and knowledge sources. We release the annotated questions and explanations as a resource with broad utility for science exam QA, including determining knowledge base construction targets, as well as supporting information aggregation in automated inference.
``All I know about politics is what I read in Twitter'': Weakly Supervised Models for Extracting Politicians' Stances From Twitter
Authors: Kristen Johnson, Dan Goldwasser
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: All

During the 2016 United States presidential election, politicians have increasingly used Twitter to express their beliefs, stances on current political issues, and reactions concerning national and international events. Given the limited length of tweets and the scrutiny politicians face for what they choose or neglect to say, they must craft and time their tweets carefully. The content and delivery of these tweets is therefore highly indicative of a politician{'}s stances. We present a weakly supervised method for extracting how issues are framed and temporal activity patterns on Twitter for popular politicians and issues of the 2016 election. These behavioral components are combined into a global model which collectively infers the most likely stance and agreement patterns among politicians, with respective accuracies of 86.44{\%} and 84.6{\%} on average.
Leveraging Multiple Domains for Sentiment Classification
Authors: Fan Yang, Arjun Mukherjee, Yifan Zhang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Classification, Domain Adaptation, General Classification, Opinion Mining, Sentiment Analysis, Sentiment Classification

Sentiment classification becomes more and more important with the rapid growth of user generated content. However, sentiment classification task usually comes with two challenges: first, sentiment classification is highly domain-dependent and training sentiment classifier for every domain is inefficient and often impractical; second, since the quantity of labeled data is important for assessing the quality of classifier, it is hard to evaluate classifiers when labeled data is limited for certain domains. To address the challenges mentioned above, we focus on learning high-level features that are able to generalize across domains, so a global classifier can benefit with a simple combination of documents from multiple domains. In this paper, the proposed model incorporates both sentiment polarity and unlabeled data from multiple domains and learns new feature representations. Our model doesn{'}t require labels from every domain, which means the learned feature representation can be generalized for sentiment domain adaptation. In addition, the learned feature representation can be used as classifier since our model defines the meaning of feature value and arranges high-level features in a prefixed order, so it is not necessary to train another classifier on top of the new features. Empirical evaluations demonstrate our model outperforms baselines and yields competitive results to other state-of-the-art works on benchmark datasets.
Political News Sentiment Analysis for Under-resourced Languages
Authors: Patrik F. Bakken, Terje A. Bratlie, Cristina Marco, Jon Atle Gulla
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Articles, General Classification, Negation, Sentiment Analysis

This paper presents classification results for the analysis of sentiment in political news articles. The domain of political news is particularly challenging, as journalists are presumably objective, whilst at the same time opinions can be subtly expressed. To deal with this challenge, in this work we conduct a two-step classification model, distinguishing first subjective and second positive and negative sentiment texts. More specifically, we propose a shallow machine learning approach where only minimal features are needed to train the classifier, including sentiment-bearing Co-Occurring Terms (COTs) and negation words. This approach yields close to state-of-the-art results. Contrary to results in other domains, the use of negations as features does not have a positive impact in the evaluation results. This method is particularly suited for languages that suffer from a lack of resources, such as sentiment lexicons or parsers, and for those systems that need to function in real-time.
Fast Inference for Interactive Models of Text
Authors: Jeffrey Lund, Paul Felt, Kevin Seppi, Eric Ringger
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Clustering, Topic Models

Probabilistic models are a useful means for analyzing large text corpora. Integrating such models with human interaction enables many new use cases. However, adding human interaction to probabilistic models requires inference algorithms which are both fast and accurate. We explore the use of Iterated Conditional Modes as a fast alternative to Gibbs sampling or variational EM. We demonstrate superior performance both in run time and model quality on three different models of text including a DP Mixture of Multinomials for web search result clustering, the Interactive Topic Model, and M OM R ESP , a multinomial crowdsourcing model.
Combining Heterogeneous User Generated Data to Sense Well-being
Authors: Adam Tsakalidis, Maria Liakata, Theo Damoulas, Brigitte Jellinek, Weisi Guo, Alex Cristea, ra
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Emotion Recognition

In this paper we address a new problem of predicting affect and well-being scales in a real-world setting of heterogeneous, longitudinal and non-synchronous textual as well as non-linguistic data that can be harvested from on-line media and mobile phones. We describe the method for collecting the heterogeneous longitudinal data, how features are extracted to address missing information and differences in temporal alignment, and how the latter are combined to yield promising predictions of affect and well-being on the basis of widely used psychological scales. We achieve a coefficient of determination ($R^2$) of 0.71-0.76 and a correlation coefficient of 0.68-0.87 which is higher than the state-of-the art in equivalent multi-modal tasks for affect.
Hashtag Recommendation with Topical Attention-Based LSTM
Authors: Yang Li, Ting Liu, Jing Jiang, Liang Zhang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Collaborative Filtering, Feature Engineering, General Classification, Information Retrieval, Text Classification, Topic Models

Microblogging services allow users to create hashtags to categorize their posts. In recent years, the task of recommending hashtags for microblogs has been given increasing attention. However, most of existing methods depend on hand-crafted features. Motivated by the successful use of long short-term memory (LSTM) for many natural language processing tasks, in this paper, we adopt LSTM to learn the representation of a microblog post. Observing that hashtags indicate the primary topics of microblog posts, we propose a novel attention-based LSTM model which incorporates topic modeling into the LSTM architecture through an attention mechanism. We evaluate our model using a large real-world dataset. Experimental results show that our model significantly outperforms various competitive baseline methods. Furthermore, the incorporation of topical attention mechanism gives more than 7.4{\%} improvement in F1 score compared with standard LSTM method.
Better call Saul: Flexible Programming for Learning and Inference in NLP
Authors: Parisa Kordjamshidi, Daniel Khashabi, Christos Christodoulopoulos, Bhargav Mangipudi, Sameer Singh, Dan Roth
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Part-Of-Speech Tagging, Probabilistic Programming, Semantic Role Labeling

We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP). We enrich Saul with components that are necessary for a broad range of learning based Natural Language Processing tasks at various levels of granularity. We illustrate these advances using three different, well-known NLP problems, and show how these generic learning and inference modules can directly exploit Saul{'}s graph-based data representation. These properties allow the programmer to easily switch between different model formulations and configurations, and consider various kinds of dependencies and correlations among variables of interest with minimal programming effort. We argue that Saul provides an extremely useful paradigm both for the design of advanced NLP systems and for supporting advanced research in NLP.
Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax
Authors: Bruno Guillaume, Kar{\"e}n Fort, Nicolas Lefebvre
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Active Learning, Natural Language Inference

This article presents the results we obtained on a complex annotation task (that of dependency syntax) using a specifically designed Game with a Purpose, ZombiLingo. We show that with suitable mechanisms (decomposition of the task, training of the players and regular control of the annotation quality during the game), it is possible to obtain annotations whose quality is significantly higher than that obtainable with a parser, provided that enough players participate. The source code of the game and the resulting annotated corpora (for French) are freely available.
Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of English Words for Multilingual Sentiment Classification
Authors: Prerana Singhal, Pushpak Bhattacharyya
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Feature Engineering, General Classification, Machine Translation, Sentence Classification, Sentiment Analysis, Sentiment Classification, Speech Recognition, Translation, Word Embeddings, Word Translation

In this paper, we provide a solution to multilingual sentiment classification using deep learning. Given input text in a language, we use word translation into English and then the embeddings of these English words to train a classifier. This projection into the English space plus word embeddings gives a simple and uniform framework for multilingual sentiment analysis. A novel idea is augmentation of the training data with polar words, appearing in these sentences, along with their polarities. This approach leads to a performance gain of 7-10{\%} over traditional classifiers on many languages, irrespective of text genre, despite the scarcity of resources in most languages.
A Character-Aware Encoder for Neural Machine Translation
Authors: Zhen Yang, Wei Chen, Feng Wang, Bo Xu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, NMT, Translation

This article proposes a novel character-aware neural machine translation (NMT) model that views the input sequences as sequences of characters rather than words. On the use of row convolution (Amodei et al., 2015), the encoder of the proposed model composes word-level information from the input sequences of characters automatically. Since our model doesn{'}t rely on the boundaries between each word (as the whitespace boundaries in English), it is also applied to languages without explicit word segmentations (like Chinese). Experimental results on Chinese-English translation tasks show that the proposed character-aware NMT model can achieve comparable translation performance with the traditional word based NMT models. Despite the target side is still word based, the proposed model is able to generate much less unknown words.