pwc-archive/papers-with-abstracts huggingface dataset:
Modeling Diachronic Change in Scientific Writing with Information Density
Authors: Raphael Rubino, Stefania Degaetano-Ortlieb, Elke Teich, Josef van Genabith
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: General Classification, Informativeness

Previous linguistic research on scientific writing has shown that language use in the scientific domain varies considerably in register and style over time. In this paper we investigate the introduction of information theory inspired features to study long term diachronic change on three levels: lexis, part-of-speech and syntax. Our approach is based on distinguishing between sentences from 19th and 20th century scientific abstracts using supervised classification models. To the best of our knowledge, the introduction of information theoretic features to this task is novel. We show that these features outperform more traditional features, such as token or character n-grams, while leading to more compact models. We present a detailed analysis of feature informativeness in order to gain a better understanding of diachronic change on different linguistic levels.
Different Contexts Lead to Different Word Embeddings
Authors: Wenpeng Hu, Jiajun Zhang, Nan Zheng
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Clustering, Information Retrieval, Learning Word Embeddings, Question Answering, Sentiment Analysis, Word Embeddings

Recent work for learning word representations has applied successfully to many NLP applications, such as sentiment analysis and question answering. However, most of these models assume a single vector per word type without considering polysemy and homonymy. In this paper, we present an extension to the CBOW model which not only improves the quality of embeddings but also makes embeddings suitable for polysemy. It differs from most of the related work in that it learns one semantic center embedding and one context bias instead of training multiple embeddings per word type. Different context leads to different bias which is defined as the weighted average embeddings of local context. Experimental results on similarity task and analogy task show that the word representations learned by the proposed method outperform the competitive baselines.
Machine Learning for Metrical Analysis of English Poetry
Authors: Manex Agirrezabal, I{\~n}aki Alegria, Mans Hulden
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: BIG-bench Machine Learning, Structured Prediction

In this work we tackle the challenge of identifying rhythmic patterns in poetry written in English. Although poetry is a literary form that makes use standard meters usually repeated among different authors, we will see in this paper how performing such analyses is a difficult task in machine learning due to the unexpected deviations from such standard patterns. After breaking down some examples of classical poetry, we apply a number of NLP techniques for the scansion of poetry, training and testing our systems against a human-annotated corpus. With these experiments, our purpose is establish a baseline of automatic scansion of poetry using NLP tools in a straightforward manner and to raise awareness of the difficulties of this task.
Automated speech-unit delimitation in spoken learner English
Authors: Russell Moore, Andrew Caines, Calbert Graham, Paula Buttery
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Sentence, speech-recognition, Speech Recognition

In order to apply computational linguistic analyses and pass information to downstream applications, transcriptions of speech obtained via automatic speech recognition (ASR) need to be divided into smaller meaningful units, in a task we refer to as {`}speech-unit (SU) delimitation{'}. We closely recreate the automatic delimitation system described by Lee and Glass (2012), {`}Sentence detection using multiple annotations{'}, Proceedings of INTERSPEECH, which combines a prosodic model, language model and speech-unit length model in log-linear fashion. Since state-of-the-art natural language processing (NLP) tools have been developed to deal with written text and its characteristic sentence-like units, SU delimitation helps bridge the gap between ASR and NLP, by normalising spoken data into a more canonical format. Previous work has focused on native speaker recordings; we test the system of Lee and Glass (2012) on non-native speaker (or {`}learner{'}) data, achieving performance above the state-of-the-art. We also consider alternative evaluation metrics which move away from the idea of a single {`}truth{'} in SU delimitation, and frame this work in the context of downstream NLP applications.
Learning to Identify Sentence Parallelism in Student Essays
Authors: Wei Song, Tong Liu, Ruiji Fu, Lizhen Liu, Hanshi Wang, Ting Liu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Sentence, Word Alignment

Parallelism is an important rhetorical device. We propose a machine learning approach for automated sentence parallelism identification in student essays. We build an essay dataset with sentence level parallelism annotated. We derive features by combining generalized word alignment strategies and the alignment measures between word sequences. The experimental results show that sentence parallelism can be effectively identified with a F1 score of 82{\%} at pair-wise level and 72{\%} at parallelism chunk level.Based on this approach, we automatically identify sentence parallelism in more than 2000 student essays and study the correlation between the use of sentence parallelism and the types and quality of essays.
Evaluating anaphora and coreference resolution to improve automatic keyphrase extraction
Authors: Marco Basaldella, Giorgia Chiaradia, Carlo Tasso
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Clustering, coreference-resolution, Coreference Resolution, Keyphrase Extraction, Language Modeling, Language Modelling

In this paper we analyze the effectiveness of using linguistic knowledge from coreference and anaphora resolution for improving the performance for supervised keyphrase extraction. In order to verify the impact of these features, we define a baseline keyphrase extraction system and evaluate its performance on a standard dataset using different machine learning algorithms. Then, we consider new sets of features by adding combinations of the linguistic features we propose and we evaluate the new performance of the system. We also use anaphora and coreference resolution to transform the documents, trying to simulate the cohesion process performed by the human mind. We found that our approach has a slightly positive impact on the performance of automatic keyphrase extraction, in particular when considering the ranking of the results.
Retrieving Occurrences of Grammatical Constructions
Authors: Anna Ehrlemark, Richard Johansson, Benjamin Lyngfelt
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Information Retrieval, Retrieval

Finding authentic examples of grammatical constructions is central in constructionist approaches to linguistics, language processing, and second language learning. In this paper, we address this problem as an information retrieval (IR) task. To facilitate research in this area, we built a benchmark collection by annotating the occurrences of six constructions in a Swedish corpus. Furthermore, we implemented a simple and flexible retrieval system for finding construction occurrences, in which the user specifies a ranking function using lexical-semantic similarities (lexicon-based or distributional). The system was evaluated using standard IR metrics on the new benchmark, and we saw that lexical-semantical rerankers improve significantly over a purely surface-oriented system, but must be carefully tailored for each individual construction.
Automatic Extraction of Learner Errors in ESL Sentences Using Linguistically Enhanced Alignments
Authors: Mariano Felice, Christopher Bryant, Ted Briscoe
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Grammatical Error Correction, Machine Translation, Sentence

We propose a new method of automatically extracting learner errors from parallel English as a Second Language (ESL) sentences in an effort to regularise annotation formats and reduce inconsistencies. Specifically, given an original and corrected sentence, our method first uses a linguistically enhanced alignment algorithm to determine the most likely mappings between tokens, and secondly employs a rule-based function to decide which alignments should be merged. Our method beats all previous approaches on the tested datasets, achieving state-of-the-art results for automatic error extraction.
Contrasting Vertical and Horizontal Transmission of Typological Features
Authors: Kenji Yamauchi, Yugo Murawaki
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

Linguistic typology provides features that have a potential of uncovering deep phylogenetic relations among the world{'}s languages. One of the key challenges in using typological features for phylogenetic inference is that horizontal (spatial) transmission obscures vertical (phylogenetic) signals. In this paper, we characterize typological features with respect to the relative strength of vertical and horizontal transmission. To do this, we first construct (1) a spatial neighbor graph of languages and (2) a phylogenetic neighbor graph by collapsing known language families. We then develop an autologistic model that predicts a feature{'}s distribution from these two graphs. In the experiments, we managed to separate vertically and/or horizontally stable features from unstable ones, and the results are largely consistent with previous findings.
How Regular is Japanese Loanword Adaptation? A Computational Study
Authors: Lingshuang Mao, Mans Hulden
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

The modifications that foreign loanwords undergo when adapted into Japanese have been the subject of much study in linguistics. The scholarly interest of the topic can be attributed to the fact that Japanese loanwords undergo a complex series of phonological adaptations, something which has been puzzling scholars for decades. While previous studies of Japanese loanword accommodation have focused on specific phonological phenomena of limited scope, the current study leverages computational methods to provide a more complete description of all the sound changes that occur when adopting English words into Japanese. To investigate this, we have developed a parallel corpus of 250 English transcriptions and their respective Japanese equivalents. These words were then used to develop a wide-coverage finite state transducer based phonological grammar that mimics the behavior of the Japanese adaption process. By developing rules with the goal of accounting completely for a large number of borrowing and analyzing forms mistakenly generated by the system, we discovered an internal inconsistency inside the loanword phonology of the Japanese language, something arguably underestimated by previous studies. The result of the investigation suggests that there are multiple {`}dimensions{'} that shape the output form of the current Japanese loanwords. These dimensions include orthography, phonetics, and historical changes.
Using Linguistic Data for English and Spanish Verb-Noun Combination Identification
Authors: Uxoa I{\~n}urrieta, Arantza D{\'\i}az de Ilarraza, Gorka Labaka, Kepa Sarasola, Itziar Aduriz, John Carroll
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Chunking, Machine Translation

We present a linguistic analysis of a set of English and Spanish verb+noun combinations (VNCs), and a method to use this information to improve VNC identification. Firstly, a sample of frequent VNCs are analysed in-depth and tagged along lexico-semantic and morphosyntactic dimensions, obtaining satisfactory inter-annotator agreement scores. Then, a VNC identification experiment is undertaken, where the analysed linguistic data is combined with chunking information and syntactic dependencies. A comparison between the results of the experiment and the results obtained by a basic detection method shows that VNC identification can be greatly improved by using linguistic information, as a large number of additional occurrences are detected with high precision.
Analyzing Gender Bias in Student Evaluations
Authors: Andamlak Terkik, Emily Prud{'}hommeaux, Cecilia Ovesdotter Alm, Christopher Homan, Scott Franklin
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Sentiment Analysis

University students in the United States are routinely asked to provide feedback on the quality of the instruction they have received. Such feedback is widely used by university administrators to evaluate teaching ability, despite growing evidence that students assign lower numerical scores to women and people of color, regardless of the actual quality of instruction. In this paper, we analyze students{'} written comments on faculty evaluation forms spanning eight years and five STEM disciplines in order to determine whether open-ended comments reflect these same biases. First, we apply sentiment analysis techniques to the corpus of comments to determine the overall affect of each comment. We then use this information, in combination with other features, to explore whether there is bias in how students describe their instructors. We show that while the gender of the evaluated instructor does not seem to affect students{'} expressed level of overall satisfaction with their instruction, it does strongly influence the language that they use to describe their instructors and their experience in class.
Adverse Drug Reaction Classification With Deep Neural Networks
Authors: Trung Huynh, Yulan He, Alistair Willis, Stefan Rueger
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Binary Classification, Classification, General Classification

We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs.
Chinese Preposition Selection for Grammatical Error Diagnosis
Authors: Hen-Hsen Huang, Yen-Chi Shao, Hsin-Hsi Chen
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Language Modeling, Language Modelling, Sentence, TAG

Misuse of Chinese prepositions is one of common word usage errors in grammatical error diagnosis. In this paper, we adopt the Chinese Gigaword corpus and HSK corpus as L1 and L2 corpora, respectively. We explore gated recurrent neural network model (GRU), and an ensemble of GRU model and maximum entropy language model (GRU-ME) to select the best preposition from 43 candidates for each test sentence. The experimental results show the advantage of the GRU models over simple RNN and n-gram models. We further analyze the effectiveness of linguistic information such as word boundary and part-of-speech tag in this task.
Extending the Use of Adaptor Grammars for Unsupervised Morphological Segmentation of Unseen Languages
Authors: Esk, Ramy er, Owen Rambow, Tianchun Yang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

We investigate using Adaptor Grammars for unsupervised morphological segmentation. Using six development languages, we investigate in detail different grammars, the use of morphological knowledge from outside sources, and the use of a cascaded architecture. Using cross-validation on our development languages, we propose a system which is language-independent. We show that it outperforms two state-of-the-art systems on 5 out of 6 languages.
CharNER: Character-Level Named Entity Recognition
Authors: Onur Kuru, Ozan Arkan Can, Deniz Yuret
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Decoder, Feature Engineering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence, TAG, Word Embeddings

We describe and evaluate a character-level tagger for language-independent Named Entity Recognition (NER). Instead of words, a sentence is represented as a sequence of characters. The model consists of stacked bidirectional LSTMs which inputs characters and outputs tag probabilities for each character. These probabilities are then converted to consistent word level named entity tags using a Viterbi decoder. We are able to achieve close to state-of-the-art NER performance in seven languages with the same basic model using only labeled NER data and no hand-engineered features or other external resources like syntactic taggers or Gazetteers.
A Neural Model for Part-of-Speech Tagging in Historical Texts
Authors: Christian Hardmeier
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Part-Of-Speech Tagging, POS, POS Tagging

Historical texts are challenging for natural language processing because they differ linguistically from modern texts and because of their lack of orthographical and grammatical standardisation. We use a character-level neural network to build a part-of-speech (POS) tagger that can process historical data directly without requiring a separate spelling normalisation stage. Its performance in a Swedish verb identification and a German POS tagging task is similar to that of a two-stage model. We analyse the performance of this tagger and a more traditional baseline system, discuss some of the remaining problems for tagging historical data and suggest how the flexibility of our neural tagger could be exploited to address diachronic divergences in morphology and syntax in early modern Swedish with the help of data from closely related languages.
Extracting Discriminative Keyphrases with Learned Semantic Hierarchies
Authors: Yunli Wang, Yong Jin, Xiaodan Zhu, Cyril Goutte
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Keyphrase Extraction, Specificity

The goal of keyphrase extraction is to automatically identify the most salient phrases from documents. The technique has a wide range of applications such as rendering a quick glimpse of a document, or extracting key content for further use. While previous work often assumes keyphrases are a static property of a given documents, in many applications, the appropriate set of keyphrases that should be extracted depends on the set of documents that are being considered together. In particular, good keyphrases should not only accurately describe the content of a document, but also reveal what discriminates it from the other documents. In this paper, we study this problem of extracting discriminative keyphrases. In particularly, we propose to use the hierarchical semantic structure between candidate keyphrases to promote keyphrases that have the right level of specificity to clearly distinguish the target document from others. We show that such knowledge can be used to construct better discriminative keyphrase extraction systems that do not assume a static, fixed set of keyphrases for a document. We show how this helps identify key expertise of authors from their papers, as well as competencies covered by online courses within different domains.
Hashtag Recommendation Using End-To-End Memory Networks with Hierarchical Attention
Authors: Haoran Huang, Qi Zhang, Yeyun Gong, Xuanjing Huang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Collaborative Filtering, General Classification, Machine Translation, Multi-class Classification, Sentiment Analysis

On microblogging services, people usually use hashtags to mark microblogs, which have a specific theme or content, making them easier for users to find. Hence, how to automatically recommend hashtags for microblogs has received much attention in recent years. Previous deep neural network-based hashtag recommendation approaches converted the task into a multi-class classification problem. However, most of these methods only took the microblog itself into consideration. Motivated by the intuition that the history of users should impact the recommendation procedure, in this work, we extend end-to-end memory networks to perform this task. We incorporate the histories of users into the external memory and introduce a hierarchical attention mechanism to select more appropriate histories. To train and evaluate the proposed method, we also construct a dataset based on microblogs collected from Twitter. Experimental results demonstrate that the proposed methods can significantly outperform state-of-the-art methods. By incorporating the hierarchical attention mechanism, the relative improvement in the proposed method over the state-of-the-art method is around 67.9{\%} in the F1-score.
Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input
Authors: Cory Shain, William Bryce, Lifeng Jin, Victoria Krakovna, Finale Doshi-Velez, Timothy Miller, William Schuler, Lane Schwartz
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Language Acquisition, Sentence

This paper presents a new memory-bounded left-corner parsing model for unsupervised raw-text syntax induction, using unsupervised hierarchical hidden Markov models (UHHMM). We deploy this algorithm to shed light on the extent to which human language learners can discover hierarchical syntax through distributional statistics alone, by modeling two widely-accepted features of human language acquisition and sentence processing that have not been simultaneously modeled by any existing grammar induction algorithm: (1) a left-corner parsing strategy and (2) limited working memory capacity. To model realistic input to human language learners, we evaluate our system on a corpus of child-directed speech rather than typical newswire corpora. Results beat or closely match those of three competing systems.
`Calling on the classical phone': a distributional model of adjective-noun errors in learners' English
Authors: Aur{\'e}lie Herbelot, Ekaterina Kochmar
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

In this paper we discuss three key points related to error detection (ED) in learners{'} English. We focus on content word ED as one of the most challenging tasks in this area, illustrating our claims on adjective{--}noun (AN) combinations. In particular, we (1) investigate the role of context in accurately capturing semantic anomalies and implement a system based on distributional topic coherence, which achieves state-of-the-art accuracy on a standard test set; (2) thoroughly investigate our system{'}s performance across individual adjective classes, concluding that a class-dependent approach is beneficial to the task; (3) discuss the data size bottleneck in this area, and highlight the challenges of automatic error generation for content words.