pwc-archive/papers-with-abstracts huggingface dataset:
Video Event Detection by Exploiting Word Dependencies from Image Captions
Authors: Sang Phan, Yusuke Miyao, Duy-Dinh Le, Shin{'}ichi Satoh
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Action Detection, Event Detection, Image Captioning, Retrieval

Video event detection is a challenging problem in information and multimedia retrieval. Different from single action detection, event detection requires a richer level of semantic information from video. In order to overcome this challenge, existing solutions often represent videos using high level features such as concepts. However, concept-based representation can be confusing because it does not encode the relationship between concepts. This issue can be addressed by exploiting the co-occurrences of the concepts, however, it often leads to a very huge number of possible combinations. In this paper, we propose a new approach to obtain the relationship between concepts by exploiting the syntactic dependencies between words in the image captions. The main advantage of this approach is that it significantly reduces the number of informative combinations between concepts. We conduct extensive experiments to analyze the effectiveness of using the new dependency representation for event detection on two large-scale TRECVID Multimedia Event Detection 2013 and 2014 datasets. Experimental results show that i) Dependency features are more discriminative than concept-based features. ii) Dependency features can be combined with our current event detection system to further improve the performance. For instance, the relative improvement can be as far as 8.6{\%} on the MEDTEST14 10Ex setting.
Predicting Restaurant Consumption Level through Social Media Footprints
Authors: Yang Xiao, Yu-An Wang, Hangyu Mao, Zhen Xiao
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

Accurate prediction of user attributes from social media is valuable for both social science analysis and consumer targeting. In this paper, we propose a systematic method to leverage user online social media content for predicting offline restaurant consumption level. We utilize the social login as a bridge and construct a dataset of 8,844 users who have been linked across Dianping (similar to Yelp) and Sina Weibo. More specifically, we construct consumption level ground truth based on user self report spending. We build predictive models using both raw features and, especially, latent features, such as topic distributions and celebrities clusters. The employed methods demonstrate that online social media content has strong predictive power for offline spending. Finally, combined with qualitative feature analysis, we present the differences in words usage, topic interests and following behavior between different consumption level groups.
A Novel Fast Framework for Topic Labeling Based on Similarity-preserved Hashing
Authors: Xian-Ling Mao, Yi-Jing Hao, Qiang Zhou, Wen-Qing Yuan, Liner Yang, He-Yan Huang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Chunking, Topic Models

Recently, topic modeling has been widely applied in data mining due to its powerful ability. A common, major challenge in applying such topic models to other tasks is to accurately interpret the meaning of each topic. Topic labeling, as a major interpreting method, has attracted significant attention recently. However, most of previous works only focus on the effectiveness of topic labeling, and less attention has been paid to quickly creating good topic descriptors; meanwhile, it{'}s hard to assign labels for new emerging topics by using most of existing methods. To solve the problems above, in this paper, we propose a novel fast topic labeling framework that casts the labeling problem as a k-nearest neighbor (KNN) search problem in a probability vector set. Our experimental results show that the proposed sequential interleaving method based on locality sensitive hashing (LSH) technology is efficient in boosting the comparison speed among probability distributions, and the proposed framework can generate meaningful labels to interpret topics, including new emerging topics.
Disfluent but effective? A quantitative study of disfluencies and conversational moves in team discourse
Authors: Felix Gervits, Kathleen Eberhard, Matthias Scheutz
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Decision Making, Spoken Dialogue Systems

Situated dialogue systems that interact with humans as part of a team (e.g., robot teammates) need to be able to use information from communication channels to gauge the coordination level and effectiveness of the team. Currently, the feasibility of this end goal is limited by several gaps in both the empirical and computational literature. The purpose of this paper is to address those gaps in the following ways: (1) investigate which properties of task-oriented discourse correspond with effective performance in human teams, and (2) discuss how and to what extent these properties can be utilized in spoken dialogue systems. To this end, we analyzed natural language data from a unique corpus of spontaneous, task-oriented dialogue (CReST corpus), which was annotated for disfluencies and conversational moves. We found that effective teams made more self-repair disfluencies and used specific communication strategies to facilitate grounding and coordination. Our results indicate that truly robust and natural dialogue systems will need to interpret highly disfluent utterances and also utilize specific collaborative mechanisms to facilitate grounding. These data shed light on effective communication in performance scenarios and directly inform the development of robust dialogue systems for situated artificial agents.
A Neural Network Approach for Knowledge-Driven Response Generation
Authors: Pavlos Vougiouklis, Jonathon Hare, Elena Simperl
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Response Generation

We present a novel response generation system. The system assumes the hypothesis that participants in a conversation base their response not only on previous dialog utterances but also on their background knowledge. Our model is based on a Recurrent Neural Network (RNN) that is trained over concatenated sequences of comments, a Convolution Neural Network that is trained over Wikipedia sentences and a formulation that couples the two trained embeddings in a multimodal space. We create a dataset of aligned Wikipedia sentences and sequences of Reddit utterances, which we we use to train our model. Given a sequence of past utterances and a set of sentences that represent the background knowledge, our end-to-end learnable model is able to generate context-sensitive and knowledge-driven responses by leveraging the alignment of two different data sources. Our approach achieves up to 55{\%} improvement in perplexity compared to purely sequential models based on RNNs that are trained only on sequences of utterances.
PersoNER: Persian Named-Entity Recognition
Authors: Hanieh Poostchi, Ehsan Zare Borzeshi, Mohammad Abdous, Massimo Piccardi
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Question Answering

Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network.
Efficient Data Selection for Bilingual Terminology Extraction from Comparable Corpora
Authors: Amir Hazem, Emmanuel Morin
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, Topic Models, Translation, Word Embeddings

Comparable corpora are the main alternative to the use of parallel corpora to extract bilingual lexicons. Although it is easier to build comparable corpora, specialized comparable corpora are often of modest size in comparison with corpora issued from the general domain. Consequently, the observations of word co-occurrences which are the basis of context-based methods are unreliable. We propose in this article to improve word co-occurrences of specialized comparable corpora and thus context representation by using general-domain data. This idea, which has been already used in machine translation task for more than a decade, is not straightforward for the task of bilingual lexicon extraction from specific-domain comparable corpora. We go against the mainstream of this task where many studies support the idea that adding out-of-domain documents decreases the quality of lexicons. Our empirical evaluation shows the advantages of this approach which induces a significant gain in the accuracy of extracted lexicons.
TweetGeo - A Tool for Collecting, Processing and Analysing Geo-encoded Linguistic Data
Authors: Nikola Ljube{\v{s}}i{\'c}, Tanja Samard{\v{z}}i{\'c}, Curdin Derungs
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

In this paper we present a newly developed tool that enables researchers interested in spatial variation of language to define a geographic perimeter of interest, collect data from the Twitter streaming API published in that perimeter, filter the obtained data by language and country, define and extract variables of interest and analyse the extracted variables by one spatial statistic and two spatial visualisations. We showcase the tool on the area and a selection of languages spoken in former Yugoslavia. By defining the perimeter, languages and a series of linguistic variables of interest we demonstrate the data collection, processing and analysis capabilities of the tool.
Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning
Authors: Luis Espinosa-Anke, Jose Camacho-Collados, Sara Rodr{\'\i}guez-Fern{\'a}ndez, Horacio Saggion, Leo Wanner
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, Semantic Textual Similarity, Sentiment Analysis, Text Generation, Translation, Word Embeddings, Word Sense Disambiguation

WordNet is probably the best known lexical resource in Natural Language Processing. While it is widely regarded as a high quality repository of concepts and semantic relations, updating and extending it manually is costly. One important type of relation which could potentially add enormous value to WordNet is the inclusion of collocational information, which is paramount in tasks such as Machine Translation, Natural Language Generation and Second Language Learning. In this paper, we present ColWordNet (CWN), an extended WordNet version with fine-grained collocational information, automatically introduced thanks to a method exploiting linear relations between analogous sense-level embeddings spaces. We perform both intrinsic and extrinsic evaluations, and release CWN for the use and scrutiny of the community.
A News Editorial Corpus for Mining Argumentation Strategies
Authors: Khalid Al-Khatib, Henning Wachsmuth, Johannes Kiesel, Matthias Hagen, Benno Stein
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Argument Mining

Many argumentative texts, and news editorials in particular, follow a specific strategy to persuade their readers of some opinion or attitude. This includes decisions such as when to tell an anecdote or where to support an assumption with statistics, which is reflected by the composition of different types of argumentative discourse units in a text. While several argument mining corpora have recently been published, they do not allow the study of argumentation strategies due to incomplete or coarse-grained unit annotations. This paper presents a novel corpus with 300 editorials from three diverse news portals that provides the basis for mining argumentation strategies. Each unit in all editorials has been assigned one of six types by three annotators with a high Fleiss{'} Kappa agreement of 0.56. We investigate various challenges of the annotation process and we conduct a first corpus analysis. Our results reveal different strategies across the news portals, exemplifying the benefit of studying editorials{---}a so far underresourced text genre in argument mining.
Universal Dependencies for Turkish
Authors: Umut Sulubacak, Memduh Gokirmak, Francis Tyers, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Joakim Nivre, G{\"u}l{\c{s}}en Eryi{\u{g}}it
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

The Universal Dependencies (UD) project was conceived after the substantial recent interest in unifying annotation schemes across languages. With its own annotation principles and abstract inventory for parts of speech, morphosyntactic features and dependency relations, UD aims to facilitate multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. This paper presents the Turkish IMST-UD Treebank, the first Turkish treebank to be in a UD release. The IMST-UD Treebank was automatically converted from the IMST Treebank, which was also recently released. We describe this conversion procedure in detail, complete with mapping tables. We also present our evaluation of the parsing performances of both versions of the IMST Treebank. Our findings suggest that the UD framework is at least as viable for Turkish as the original annotation framework of the IMST Treebank.
Creating Resources for Dialectal Arabic from a Single Annotation: A Case Study on Egyptian and Levantine
Authors: Esk, Ramy er, Nizar Habash, Owen Rambow, Arfath Pasha
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Morphological Analysis

Arabic dialects present a special problem for natural language processing because there are few resources, they have no standard orthography, and have not been studied much. However, as more and more written dialectal Arabic is found in social media, NLP for Arabic dialects becomes an important goal. We present a methodology for creating a morphological analyzer and a morphological tagger for dialectal Arabic, and we illustrate it on Egyptian and Levantine Arabic. To our knowledge, these are the first analyzer and tagger for Levantine.
Multilingual Aliasing for Auto-Generating Proposition Banks
Authors: Alan Akbik, Xinyu Guan, Yunyao Li
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, Question Answering, Semantic Role Labeling

Semantic Role Labeling (SRL) is the task of identifying the predicate-argument structure in sentences with semantic frame and role labels. For the English language, the Proposition Bank provides both a lexicon of all possible semantic frames and large amounts of labeled training data. In order to expand SRL beyond English, previous work investigated automatic approaches based on parallel corpora to automatically generate Proposition Banks for new target languages (TLs). However, this approach heuristically produces the frame lexicon from word alignments, leading to a range of lexicon-level errors and inconsistencies. To address these issues, we propose to manually alias TL verbs to existing English frames. For instance, the German verb drehen may evoke several meanings, including {``}turn something{''} and {``}film something{''}. Accordingly, we alias the former to the frame TURN.01 and the latter to a group of frames that includes FILM.01 and SHOOT.03. We execute a large-scale manual aliasing effort for three target languages and apply the new lexicons to automatically generate large Proposition Banks for Chinese, French and German with manually curated frames. We present a detailed evaluation in which we find that our proposed approach significantly increases the quality and consistency of the generated Proposition Banks. We release these resources to the research community.
PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors
Authors: David R. Mortensen, Patrick Littell, Akash Bharadwaj, Kartik Goyal, Chris Dyer, Lori Levin
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: NER

This paper contributes to a growing body of evidence that{---}when coupled with appropriate machine-learning techniques{--}linguistically motivated, information-rich representations can outperform one-hot encodings of linguistic data. In particular, we show that phonological features outperform character-based models. PanPhon is a database relating over 5,000 IPA segments to 21 subsegmental articulatory features. We show that this database boosts performance in various NER-related tasks. Phonologically aware, neural CRF models built on PanPhon features are able to perform better on monolingual Spanish and Turkish NER tasks that character-based models. They have also been shown to work well in transfer models (as between Uzbek and Turkish). PanPhon features also contribute measurably to Orthography-to-IPA conversion tasks.
More is not always better: balancing sense distributions for all-words Word Sense Disambiguation
Authors: Marten Postma, Ruben Izquierdo Bevia, Piek Vossen
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: All, Word Sense Disambiguation

Current Word Sense Disambiguation systems show an extremely poor performance on low frequent senses, which is mainly caused by the difference in sense distributions between training and test data. The main focus in tackling this problem has been on acquiring more data or selecting a single predominant sense and not necessarily on the meta properties of the data itself. We demonstrate that these properties, such as the volume, provenance, and balancing, play an important role with respect to system performance. In this paper, we describe a set of experiments to analyze these meta properties in the framework of a state-of-the-art WSD system when evaluated on the SemEval-2013 English all-words dataset. We show that volume and provenance are indeed important, but that approximating the perfect balancing of the selected training data leads to an improvement of 21 points and exceeds state-of-the-art systems by 14 points while using only simple features. We therefore conclude that unsupervised acquisition of training data should be guided by strategies aimed at matching meta properties.
Word Embeddings, Analogies, and Machine Learning: Beyond king - man + woman = queen
Authors: Aleks Drozd, R, Anna Gladkova, Satoshi Matsuoka
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: BIG-bench Machine Learning, Morphological Analysis, Retrieval, Word Embeddings, Word Sense Disambiguation

Solving word analogies became one of the most popular benchmarks for word embeddings on the assumption that linear relations between word pairs (such as \textit{king}:\textit{man} :: \textit{woman}:\textit{queen}) are indicative of the quality of the embedding. We question this assumption by showing that the information not detected by linear offset may still be recoverable by a more sophisticated search method, and thus is actually encoded in the embedding. The general problem with linear offset is its sensitivity to the idiosyncrasies of individual words. We show that simple averaging over multiple word pairs improves over the state-of-the-art. A further improvement in accuracy (up to 30{\%} for some embeddings and relations) is achieved by combining cosine similarity with an estimation of the extent to which a candidate answer belongs to the correct word class. In addition to this practical contribution, this work highlights the problem of the interaction between word embeddings and analogy retrieval algorithms, and its implications for the evaluation of word embeddings and the use of analogies in extrinsic tasks.
A Supervised Approach for Enriching the Relational Structure of Frame Semantics in FrameNet
Authors: Shafqat Mumtaz Virk, Philippe Muller, Juliette Conrath
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Coreference Resolution, Question Answering

Frame semantics is a theory of linguistic meanings, and is considered to be a useful framework for shallow semantic analysis of natural language. FrameNet, which is based on frame semantics, is a popular lexical semantic resource. In addition to providing a set of core semantic frames and their frame elements, FrameNet also provides relations between those frames (hence providing a network of frames i.e. FrameNet). We address here the limited coverage of the network of conceptual relations between frames in FrameNet, which has previously been pointed out by others. We present a supervised model using rich features from three different sources: structural features from the existing FrameNet network, information from the WordNet relations between synsets projected into semantic frames, and corpus-collected lexical associations. We show large improvements over baselines consisting of each of the three groups of features in isolation. We then use this model to select frame pairs as candidate relations, and perform evaluation on a sample with good precision.
Reddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis
Authors: Anh Dang, Abidalrahman Moh{'}d, Aminul Islam, Rosane Minghim, Michael Smit, Evangelos Milios
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Attribute, Information Retrieval, Language Modeling, Language Modelling, Machine Translation, Named Entity Recognition (NER), Paraphrase Identification, Rumour Detection, Semantic Similarity, Semantic Textual Similarity, Speech Recognition

This paper introduces a new large-scale n-gram corpus that is created specifically from social media text. Two distinguishing characteristics of this corpus are its monthly temporal attribute and that it is created from 1.65 billion comments of user-generated text in Reddit. The usefulness of this corpus is exemplified and evaluated by a novel Topic-based Latent Semantic Analysis (TLSA) algorithm. The experimental results show that unsupervised TLSA outperforms all the state-of-the-art unsupervised and semi-supervised methods in SEMEVAL 2015: paraphrase and semantic similarity in Twitter tasks.
Dictionaries as Networks: Identifying the graph structure of Ogden's Basic English
Authors: Camilo Garrido, Claudio Gutierrez
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

We study the network structure underlying dictionaries. We systematize the properties of such networks and show their relevance for linguistics. As case of study, we apply this technique to identify the graph structure of Ogden{'}s Basic English. We show that it constitutes a strong core of the English language network and that classic centrality measures fail to capture this set of words.
Structured Generative Models of Continuous Features for Word Sense Induction
Authors: Alex Komninos, ros, Man, Suresh har
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Clustering, Word Embeddings, Word Sense Disambiguation, Word Sense Induction

We propose a structured generative latent variable model that integrates information from multiple contextual representations for Word Sense Induction. Our approach jointly models global lexical, local lexical and dependency syntactic context. Each context type is associated with a latent variable and the three types of variables share a hierarchical structure. We use skip-gram based word and dependency context embeddings to construct all three types of representations, reducing the total number of parameters to be estimated and enabling better generalization. We describe an EM algorithm to efficiently estimate model parameters and use the Integrated Complete Likelihood criterion to automatically estimate the number of senses. Our model achieves state-of-the-art results on the SemEval-2010 and SemEval-2013 Word Sense Induction datasets.