pwc-archive/papers-with-abstracts huggingface dataset:
Determining the Multiword Expression Inventory of a Surprise Language
Authors: Bahar Salehi, Paul Cook, Timothy Baldwin
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation

Much previous research on multiword expressions (MWEs) has focused on the token- and type-level tasks of MWE identification and extraction, respectively. Such studies typically target known prevalent MWE types in a given language. This paper describes the first attempt to learn the MWE inventory of a {``}surprise{''} language for which we have no explicit prior knowledge of MWE patterns, certainly no annotated MWE data, and not even a parallel corpus. Our proposed model is trained on a treebank with MWE relations of a source language, and can be applied to the monolingual corpus of the surprise language to identify its MWE construction types.
A Hybrid Deep Learning Architecture for Sentiment Analysis
Authors: Md. Shad Akhtar, Ayush Kumar, Asif Ekbal, Pushpak Bhattacharyya
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Aspect-Based Sentiment Analysis (ABSA), Deep Learning, Sentence, Sentiment Analysis, Sentiment Classification

In this paper, we propose a novel hybrid deep learning archtecture which is highly efficient for sentiment analysis in resource-poor languages. We learn sentiment embedded vectors from the Convolutional Neural Network (CNN). These are augmented to a set of optimized features selected through a multi-objective optimization (MOO) framework. The sentiment augmented optimized vector obtained at the end is used for the training of SVM for sentiment classification. We evaluate our proposed approach for coarse-grained (i.e. sentence level) as well as fine-grained (i.e. aspect level) sentiment analysis on four Hindi datasets covering varying domains. In order to show that our proposed method is generic in nature we also evaluate it on two benchmark English datasets. Evaluation shows that the results of the proposed method are consistent across all the datasets and often outperforms the state-of-art systems. To the best of our knowledge, this is the very first attempt where such a deep learning model is used for less-resourced languages such as Hindi.
Word Segmentation in Sanskrit Using Path Constrained Random Walks
Authors: Amrith Krishna, Bishal Santra, Pavankumar Satuluri, B, Sasi Prasanth aru, Bhumi Faldu, Yajuvendra Singh, Pawan Goyal
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Segmentation, Sentence, valid

In Sanskrit, the phonemes at the word boundaries undergo changes to form new phonemes through a process called as sandhi. A fused sentence can be segmented into multiple possible segmentations. We propose a word segmentation approach that predicts the most semantically valid segmentation for a given sentence. We treat the problem as a query expansion problem and use the path-constrained random walks framework to predict the correct segments.
Mongolian Named Entity Recognition System with Rich Features
Authors: Weihua Wang, Feilong Bao, Guanglai Gao
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Answering

In this paper, we first build a manually annotated named entity corpus of Mongolian. Then, we propose three morphological processing methods and study comprehensive features, including syllable features, lexical features, context features, morphological features and semantic features in Mongolian named entity recognition. Moreover, we also evaluate the influence of word cluster features on the system and combine all features together eventually. The experimental result shows that segmenting each suffix into an individual token achieves better results than deleting suffixes or using the suffixes as feature. The system based on segmenting suffixes with all proposed features yields benchmark result of F-measure=84.65 on this corpus.
Appraising UMLS Coverage for Summarizing Medical Evidence
Authors: Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, Fang Chen
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Text Summarization

When making clinical decisions, practitioners need to rely on the most relevant evidence available. However, accessing a vast body of medical evidence and confronting with the issue of information overload can be challenging and time consuming. This paper proposes an effective summarizer for medical evidence by utilizing both UMLS and WordNet. Given a clinical query and a set of relevant abstracts, our aim is to generate a fluent, well-organized, and compact summary that answers the query. Analysis via ROUGE metrics shows that using WordNet as a general-purpose lexicon helps to capture the concepts not covered by the UMLS Metathesaurus, and hence significantly increases the performance. The effectiveness of our proposed approach is demonstrated by conducting a set of experiments over a specialized evidence-based medicine (EBM) corpus - which has been gathered and annotated for the purpose of biomedical text summarization.
Large-scale Multi-class and Hierarchical Product Categorization for an E-commerce Giant
Authors: Ali Cevahir, Koji Murakami
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: General Classification, Product Categorization, Text Classification

In order to organize the large number of products listed in e-commerce sites, each product is usually assigned to one of the multi-level categories in the taxonomy tree. It is a time-consuming and difficult task for merchants to select proper categories within thousands of options for the products they sell. In this work, we propose an automatic classification tool to predict the matching category for a given product title and description. We used a combination of two different neural models, i.e., deep belief nets and deep autoencoders, for both titles and descriptions. We implemented a selective reconstruction approach for the input layer during the training of the deep neural networks, in order to scale-out for large-sized sparse feature vectors. GPUs are utilized in order to train neural networks in a reasonable time. We have trained our models for around 150 million products with a taxonomy tree with at most 5 levels that contains 28,338 leaf categories. Tests with millions of products show that our first predictions matches 81{\%} of merchants{'} assignments, when {``}others{''} categories are excluded.
Using Relevant Public Posts to Enhance News Article Summarization
Authors: Chen Li, Zhongyu Wei, Yang Liu, Yang Jin, Fei Huang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Articles, Sentence, Sentence Compression

A news article summary usually consists of 2-3 key sentences that reflect the gist of that news article. In this paper we explore using public posts following a new article to improve automatic summary generation for the news article. We propose different approaches to incorporate information from public posts, including using frequency information from the posts to re-estimate bigram weights in the ILP-based summarization model and to re-weight a dependency tree edge{'}s importance for sentence compression, directly selecting sentences from posts as the final summary, and finally a strategy to combine the summarization results generated from news articles and posts. Our experiments on data collected from Facebook show that relevant public posts provide useful information and can be effectively leveraged to improve news article summarization results.
A Proposition-Based Abstractive Summariser
Authors: Yimai Fang, Haoyue Zhu, Ewa Muszy{\'n}ska, Alex Kuhnle, er, Simone Teufel
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Language Modeling, Language Modelling, Sentence, Sentence Compression

Abstractive summarisation is not yet common amongst today{'}s deployed and research systems. Most existing systems either extract sentences or compress individual sentences. In this paper, we present a summariser that works by a different paradigm. It is a further development of an existing summariser that has an incremental, proposition-based content selection process but lacks a natural language (NL) generator for the final output. Using an NL generator, we can now produce the summary text to directly reflect the selected propositions. Our evaluation compares textual quality of our system to the earlier preliminary output method, and also uses ROUGE to compare to various summarisers that use the traditional method of sentence extraction, followed by compression. Our results suggest that cutting out the middle-man of sentence extraction can lead to better abstractive summaries.
Cross-lingual Learning of an Open-domain Semantic Parser
Authors: Kilian Evang, Johan Bos
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Semantic Parsing

We propose a method for learning semantic CCG parsers by projecting annotations via a parallel corpus. The method opens an avenue towards cheaply creating multilingual semantic parsers mapping open-domain text to formal meaning representations. A first cross-lingually learned Dutch (from English) semantic parser obtains f-scores ranging from 42.99{\%} to 69.22{\%} depending on the level of label informativity taken into account, compared to 58.40{\%} to 78.88{\%} for the underlying source-language system. These are promising numbers compared to state-of-the-art semantic parsing in open domains.
A subtree-based factorization of dependency parsing
Authors: Qiuye Zhao, Qun Liu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Dependency Parsing

We propose a dependency parsing pipeline, in which the parsing of long-distance projections and localized dependencies are explicitly decomposed at the input level. A chosen baseline dependency parsing model performs only on {`}carved{'} sequences at the second stage, which are transformed from coarse constituent parsing outputs at the first stage. When k-best constituent parsing outputs are kept, a third-stage is required to search for an optimal combination of the overlapped dependency subtrees. In this sense, our dependency model is subtree-factored. We explore alternative approaches for scoring subtrees, including feature-based models as well as continuous representations. The search for optimal subset to combine is formulated as an ILP problem. This framework especially benefits the models poor on long sentences, generally improving baselines by 0.75-1.28 (UAS) on English, achieving comparable performance with high-order models but faster. For Chinese, the most notable increase is as high as 3.63 (UAS) when the proposed framework is applied to first-order parsing models.
K-SRL: Instance-based Learning for Semantic Role Labeling
Authors: Alan Akbik, 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 and labeling predicate-argument structures in sentences with semantic frame and role labels. A known challenge in SRL is the large number of low-frequency exceptions in training data, which are highly context-specific and difficult to generalize. To overcome this challenge, we propose the use of instance-based learning that performs no explicit generalization, but rather extrapolates predictions from the most similar instances in the training data. We present a variant of k-nearest neighbors (kNN) classification with composite features to identify nearest neighbors for SRL. We show that high-quality predictions can be derived from a very small number of similar instances. In a comparative evaluation we experimentally demonstrate that our instance-based learning approach significantly outperforms current state-of-the-art systems on both in-domain and out-of-domain data, reaching F1-scores of 89,28{\%} and 79.91{\%} respectively.
A Bayesian model for joint word alignment and part-of-speech transfer
Authors: Robert {\"O}stling
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, Word Alignment, Word Sense Disambiguation

Current methods for word alignment require considerable amounts of parallel text to deliver accurate results, a requirement which is met only for a small minority of the world{'}s approximately 7,000 languages. We show that by jointly performing word alignment and annotation transfer in a novel Bayesian model, alignment accuracy can be improved for language pairs where annotations are available for only one of the languages{---}a finding which could facilitate the study and processing of a vast number of low-resource languages. We also present an evaluation where our method is used to perform single-source and multi-source part-of-speech transfer with 22 translations of the same text in four different languages. This allows us to quantify the considerable variation in accuracy depending on the specific source text(s) used, even with different translations into the same language.
Splitting compounds with ngrams
Authors: Naomi Tachikawa Shapiro
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Language Modeling, Language Modelling, Machine Translation, Morphological Analysis, Segmentation, Semantic Parsing, Translation

Compound words with unmarked word boundaries are problematic for many tasks in NLP and computational linguistics, including information extraction, machine translation, and syllabification. This paper introduces a simple, proof-of-concept language modeling approach to automatic compound segmentation, as applied to Finnish. This approach utilizes an off-the-shelf morphological analyzer to split training words into their constituent morphemes. A language model is subsequently trained on ngrams composed of morphemes, morpheme boundaries, and word boundaries. Linguistic constraints are then used to weed out phonotactically ill-formed segmentations, thereby allowing the language model to select the best grammatical segmentation. This approach achieves an accuracy of {\textasciitilde}97{\%}.
GAKE: Graph Aware Knowledge Embedding
Authors: Jun Feng, Minlie Huang, Yang Yang, Xiaoyan Zhu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

Knowledge embedding, which projects triples in a given knowledge base to d-dimensional vectors, has attracted considerable research efforts recently. Most existing approaches treat the given knowledge base as a set of triplets, each of whose representation is then learned separately. However, as a fact, triples are connected and depend on each other. In this paper, we propose a graph aware knowledge embedding method (GAKE), which formulates knowledge base as a directed graph, and learns representations for any vertices or edges by leveraging the graph{'}s structural information. We introduce three types of graph context for embedding: neighbor context, path context, and edge context, each reflects properties of knowledge from different perspectives. We also design an attention mechanism to learn representative power of different vertices or edges. To validate our method, we conduct several experiments on two tasks. Experimental results suggest that our method outperforms several state-of-art knowledge embedding models.
Ranking Responses Oriented to Conversational Relevance in Chat-bots
Authors: Bowen Wu, Baoxun Wang, Hui Xue
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Sentence

For automatic chatting systems, it is indeed a great challenge to reply the given query considering the conversation history, rather than based on the query only. This paper proposes a deep neural network to address the context-aware response ranking problem by end-to-end learning, so as to help to select conversationally relevant candidate. By combining the multi-column convolutional layer and the recurrent layer, our model is able to model the semantics of the utterance sequence by grasping the semantic clue within the conversation, on the basis of the effective representation for each sentence. Especially, the network utilizes attention pooling to further emphasis the importance of essential words in conversations, thus the representations of contexts tend to be more meaningful and the performance of candidate ranking is notably improved. Meanwhile, due to the adoption of attention pooling, it is possible to visualize the semantic clues. The experimental results on the large amount of conversation data from social media have shown that our approach is promising for quantifying the conversational relevance of responses, and indicated its good potential for building practical IR based chat-bots.
Probabilistic Prototype Model for Serendipitous Property Mining
Authors: Taesung Lee, Seung-won Hwang, Zhongyuan Wang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: model, Question Generation

Besides providing the relevant information, amusing users has been an important role of the web. Many web sites provide serendipitous (unexpected but relevant) information to draw user traffic. In this paper, we study the representative scenario of mining an amusing quiz. An existing approach leverages a knowledge base to mine an unexpected property then find quiz questions on such property, based on prototype theory in cognitive science. However, existing deterministic model is vulnerable to noise in the knowledge base. Therefore, we instead propose to leverage probabilistic approach to build a prototype that can overcome noise. Our extensive empirical study shows that our approach not only significantly outperforms baselines by 0.06 in accuracy, and 0.11 in serendipity but also shows higher relevance than the traditional relevance-pursuing baseline using TF-IDF.
Identifying Cross-Cultural Differences in Word Usage
Authors: Aparna Garimella, Rada Mihalcea, James Pennebaker
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Cultural Vocal Bursts Intensity Prediction

Personal writings have inspired researchers in the fields of linguistics and psychology to study the relationship between language and culture to better understand the psychology of people across different cultures. In this paper, we explore this relation by developing cross-cultural word models to identify words with cultural bias {--} i.e., words that are used in significantly different ways by speakers from different cultures. Focusing specifically on two cultures: United States and Australia, we identify a set of words with significant usage differences, and further investigate these words through feature analysis and topic modeling, shedding light on the attributes of language that contribute to these differences.
Reading-Time Annotations for ``Balanced Corpus of Contemporary Written Japanese''
Authors: Masayuki Asahara, Hajime Ono, Edson T. Miyamoto
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Articles

The Dundee Eyetracking Corpus contains eyetracking data collected while native speakers of English and French read newspaper editorial articles. Similar resources for other languages are still rare, especially for languages in which words are not overtly delimited with spaces. This is a report on a project to build an eyetracking corpus for Japanese. Measurements were collected while 24 native speakers of Japanese read excerpts from the Balanced Corpus of Contemporary Written Japanese Texts were presented with or without segmentation (i.e. with or without space at the boundaries between bunsetsu segmentations) and with two types of methodologies (eyetracking and self-paced reading presentation). Readers{'} background information including vocabulary-size estimation and Japanese reading-span score were also collected. As an example of the possible uses for the corpus, we also report analyses investigating the phenomena of anti-locality.
Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition
Authors: Mehmet Ali Yatbaz, Volkan Cirik, Aylin K{\"u}ntay, Deniz Yuret
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Language Acquisition

Learning syntactic categories is a fundamental task in language acquisition. Previous studies show that co-occurrence patterns of preceding and following words are essential to group words into categories. However, the neighboring words, or frames, are rarely repeated exactly in the data. This creates data sparsity and hampers learning for frame based models. In this work, we propose a paradigmatic representation of word context which uses probable substitutes instead of frames. Our experiments on child-directed speech show that models based on probable substitutes learn more accurate categories with fewer examples compared to models based on frames.
Understanding the Lexical Simplification Needs of Non-Native Speakers of English
Authors: Gustavo Paetzold, Lucia Specia
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Complex Word Identification, Lexical Simplification, Misconceptions, Text Simplification

We report three user studies in which the Lexical Simplification needs of non-native English speakers are investigated. Our analyses feature valuable new insight on the relationship between the non-natives{'} notion of complexity and various morphological, semantic and lexical word properties. Some of our findings contradict long-standing misconceptions about word simplicity. The data produced in our studies consists of 211,564 annotations made by 1,100 volunteers, which we hope will guide forthcoming research on Text Simplification for non-native speakers of English.
How Interlocutors Coordinate with each other within Emotional Segments?
Authors: Firoj Alam, Shammur Absar Chowdhury, Morena Danieli, Giuseppe Riccardi
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

In this paper, we aim to investigate the coordination of interlocutors behavior in different emotional segments. Conversational coordination between the interlocutors is the tendency of speakers to predict and adjust each other accordingly on an ongoing conversation. In order to find such a coordination, we investigated 1) lexical similarities between the speakers in each emotional segments, 2) correlation between the interlocutors using psycholinguistic features, such as linguistic styles, psychological process, personal concerns among others, and 3) relation of interlocutors turn-taking behaviors such as competitiveness. To study the degree of coordination in different emotional segments, we conducted our experiments using real dyadic conversations collected from call centers in which agent{'}s emotional state include empathy and customer{'}s emotional states include anger and frustration. Our findings suggest that the most coordination occurs between the interlocutors inside anger segments, where as, a little coordination was observed when the agent was empathic, even though an increase in the amount of non-competitive overlaps was observed. We found no significant difference between anger and frustration segment in terms of turn-taking behaviors. However, the length of pause significantly decreases in the preceding segment of anger where as it increases in the preceding segment of frustration.
Advancing Linguistic Features and Insights by Label-informed Feature Grouping: An Exploration in the Context of Native Language Identification
Authors: Serhiy Bykh, Detmar Meurers
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Clustering, Language Acquisition, Language Identification, Native Language Identification, Text Classification

We propose a hierarchical clustering approach designed to group linguistic features for supervised machine learning that is inspired by variationist linguistics. The method makes it possible to abstract away from the individual feature occurrences by grouping features together that behave alike with respect to the target class, thus providing a new, more general perspective on the data. On the one hand, it reduces data sparsity, leading to quantitative performance gains. On the other, it supports the formation and evaluation of hypotheses about individual choices of linguistic structures. We explore the method using features based on verb subcategorization information and evaluate the approach in the context of the Native Language Identification (NLI) task.