acl acl2013 knowledge-graph by maker-knowledge-mining

acl 2013 knowledge graph

papers list:

1 acl-2013-"Let Everything Turn Well in Your Wife": Generation of Adult Humor Using Lexical Constraints

Author: Alessandro Valitutti ; Hannu Toivonen ; Antoine Doucet ; Jukka M. Toivanen

Abstract: We propose a method for automated generation of adult humor by lexical replacement and present empirical evaluation results of the obtained humor. We propose three types of lexical constraints as building blocks of humorous word substitution: constraints concerning the similarity of sounds or spellings of the original word and the substitute, a constraint requiring the substitute to be a taboo word, and constraints concerning the position and context of the replacement. Empirical evidence from extensive user studies indicates that these constraints can increase the effectiveness of humor generation significantly.

2 acl-2013-A Bayesian Model for Joint Unsupervised Induction of Sentiment, Aspect and Discourse Representations

Author: Angeliki Lazaridou ; Ivan Titov ; Caroline Sporleder

Abstract: We propose a joint model for unsupervised induction of sentiment, aspect and discourse information and show that by incorporating a notion of latent discourse relations in the model, we improve the prediction accuracy for aspect and sentiment polarity on the sub-sentential level. We deviate from the traditional view of discourse, as we induce types of discourse relations and associated discourse cues relevant to the considered opinion analysis task; consequently, the induced discourse relations play the role of opinion and aspect shifters. The quantitative analysis that we conducted indicated that the integration of a discourse model increased the prediction accuracy results with respect to the discourse-agnostic approach and the qualitative analysis suggests that the induced representations encode a meaningful discourse structure.

3 acl-2013-A Comparison of Techniques to Automatically Identify Complex Words.

Author: Matthew Shardlow

Abstract: Identifying complex words (CWs) is an important, yet often overlooked, task within lexical simplification (The process of automatically replacing CWs with simpler alternatives). If too many words are identified then substitutions may be made erroneously, leading to a loss of meaning. If too few words are identified then those which impede a user’s understanding may be missed, resulting in a complex final text. This paper addresses the task of evaluating different methods for CW identification. A corpus of sentences with annotated CWs is mined from Simple Wikipedia edit histories, which is then used as the basis for several experiments. Firstly, the corpus design is explained and the results of the validation experiments using human judges are reported. Experiments are carried out into the CW identification techniques of: simplifying everything, frequency thresholding and training a support vector machine. These are based upon previous approaches to the task and show that thresholding does not perform significantly differently to the more na¨ ıve technique of simplifying everything. The support vector machine achieves a slight increase in precision over the other two methods, but at the cost of a dramatic trade off in recall.

4 acl-2013-A Context Free TAG Variant

Author: Ben Swanson ; Elif Yamangil ; Eugene Charniak ; Stuart Shieber

Abstract: We propose a new variant of TreeAdjoining Grammar that allows adjunction of full wrapping trees but still bears only context-free expressivity. We provide a transformation to context-free form, and a further reduction in probabilistic model size through factorization and pooling of parameters. This collapsed context-free form is used to implement efficient gram- mar estimation and parsing algorithms. We perform parsing experiments the Penn Treebank and draw comparisons to TreeSubstitution Grammars and between different variations in probabilistic model design. Examination of the most probable derivations reveals examples of the linguistically relevant structure that our variant makes possible.

5 acl-2013-A Decade of Automatic Content Evaluation of News Summaries: Reassessing the State of the Art

Author: Peter A. Rankel ; John M. Conroy ; Hoa Trang Dang ; Ani Nenkova

Abstract: How good are automatic content metrics for news summary evaluation? Here we provide a detailed answer to this question, with a particular focus on assessing the ability of automatic evaluations to identify statistically significant differences present in manual evaluation of content. Using four years of data from the Text Analysis Conference, we analyze the performance of eight ROUGE variants in terms of accuracy, precision and recall in finding significantly different systems. Our experiments show that some of the neglected variants of ROUGE, based on higher order n-grams and syntactic dependencies, are most accurate across the years; the commonly used ROUGE-1 scores find too many significant differences between systems which manual evaluation would deem comparable. We also test combinations ofROUGE variants and find that they considerably improve the accuracy of automatic prediction.

6 acl-2013-A Java Framework for Multilingual Definition and Hypernym Extraction

Author: Stefano Faralli ; Roberto Navigli

Abstract: In this paper we present a demonstration of a multilingual generalization of Word-Class Lattices (WCLs), a supervised lattice-based model used to identify textual definitions and extract hypernyms from them. Lattices are learned from a dataset of automatically-annotated definitions from Wikipedia. We release a Java API for the programmatic use of multilingual WCLs in three languages (English, French and Italian), as well as a Web application for definition and hypernym extraction from user-provided sentences.

7 acl-2013-A Lattice-based Framework for Joint Chinese Word Segmentation, POS Tagging and Parsing

Author: Zhiguo Wang ; Chengqing Zong ; Nianwen Xue

Abstract: For the cascaded task of Chinese word segmentation, POS tagging and parsing, the pipeline approach suffers from error propagation while the joint learning approach suffers from inefficient decoding due to the large combined search space. In this paper, we present a novel lattice-based framework in which a Chinese sentence is first segmented into a word lattice, and then a lattice-based POS tagger and a lattice-based parser are used to process the lattice from two different viewpoints: sequential POS tagging and hierarchical tree building. A strategy is designed to exploit the complementary strengths of the tagger and parser, and encourage them to predict agreed structures. Experimental results on Chinese Treebank show that our lattice-based framework significantly improves the accuracy of the three sub-tasks. 1

8 acl-2013-A Learner Corpus-based Approach to Verb Suggestion for ESL

Author: Yu Sawai ; Mamoru Komachi ; Yuji Matsumoto

Abstract: We propose a verb suggestion method which uses candidate sets and domain adaptation to incorporate error patterns produced by ESL learners. The candidate sets are constructed from a large scale learner corpus to cover various error patterns made by learners. Furthermore, the model is trained using both a native corpus and the learner corpus via a domain adaptation technique. Experiments on two learner corpora show that the candidate sets increase the coverage of error patterns and domain adaptation improves the performance for verb suggestion.

9 acl-2013-A Lightweight and High Performance Monolingual Word Aligner

Author: Xuchen Yao ; Benjamin Van Durme ; Chris Callison-Burch ; Peter Clark

Abstract: Fast alignment is essential for many natural language tasks. But in the setting of monolingual alignment, previous work has not been able to align more than one sentence pair per second. We describe a discriminatively trained monolingual word aligner that uses a Conditional Random Field to globally decode the best alignment with features drawn from source and target sentences. Using just part-of-speech tags and WordNet as external resources, our aligner gives state-of-the-art result, while being an order-of-magnitude faster than the previous best performing system.

10 acl-2013-A Markov Model of Machine Translation using Non-parametric Bayesian Inference

Author: Yang Feng ; Trevor Cohn

Abstract: Most modern machine translation systems use phrase pairs as translation units, allowing for accurate modelling of phraseinternal translation and reordering. However phrase-based approaches are much less able to model sentence level effects between different phrase-pairs. We propose a new model to address this imbalance, based on a word-based Markov model of translation which generates target translations left-to-right. Our model encodes word and phrase level phenomena by conditioning translation decisions on previous decisions and uses a hierarchical Pitman-Yor Process prior to provide dynamic adaptive smoothing. This mechanism implicitly supports not only traditional phrase pairs, but also gapping phrases which are non-consecutive in the source. Our experiments on Chinese to English and Arabic to English translation show consistent improvements over competitive baselines, of up to +3.4 BLEU.

11 acl-2013-A Multi-Domain Translation Model Framework for Statistical Machine Translation

Author: Rico Sennrich ; Holger Schwenk ; Walid Aransa

Abstract: While domain adaptation techniques for SMT have proven to be effective at improving translation quality, their practicality for a multi-domain environment is often limited because of the computational and human costs of developing and maintaining multiple systems adapted to different domains. We present an architecture that delays the computation of translation model features until decoding, allowing for the application of mixture-modeling techniques at decoding time. We also de- scribe a method for unsupervised adaptation with development and test data from multiple domains. Experimental results on two language pairs demonstrate the effectiveness of both our translation model architecture and automatic clustering, with gains of up to 1BLEU over unadapted systems and single-domain adaptation.

12 acl-2013-A New Set of Norms for Semantic Relatedness Measures

Author: Sean Szumlanski ; Fernando Gomez ; Valerie K. Sims

Abstract: We have elicited human quantitative judgments of semantic relatedness for 122 pairs of nouns and compiled them into a new set of relatedness norms that we call Rel-122. Judgments from individual subjects in our study exhibit high average correlation to the resulting relatedness means (r = 0.77, σ = 0.09, N = 73), although not as high as Resnik’s (1995) upper bound for expected average human correlation to similarity means (r = 0.90). This suggests that human perceptions of relatedness are less strictly constrained than perceptions of similarity and establishes a clearer expectation for what constitutes human-like performance by a computational measure of semantic relatedness. We compare the results of several WordNet-based similarity and relatedness measures to our Rel-122 norms and demonstrate the limitations of WordNet for discovering general indications of semantic relatedness. We also offer a critique of the field’s reliance upon similarity norms to evaluate relatedness measures.

13 acl-2013-A New Syntactic Metric for Evaluation of Machine Translation

Author: Melania Duma ; Cristina Vertan ; Wolfgang Menzel

Abstract: Machine translation (MT) evaluation aims at measuring the quality of a candidate translation by comparing it with a reference translation. This comparison can be performed on multiple levels: lexical, syntactic or semantic. In this paper, we propose a new syntactic metric for MT evaluation based on the comparison of the dependency structures of the reference and the candidate translations. The dependency structures are obtained by means of a Weighted Constraints Dependency Grammar parser. Based on experiments performed on English to German translations, we show that the new metric correlates well with human judgments at the system level. 1

14 acl-2013-A Novel Classifier Based on Quantum Computation

Author: Ding Liu ; Xiaofang Yang ; Minghu Jiang

Abstract: In this article, we propose a novel classifier based on quantum computation theory. Different from existing methods, we consider the classification as an evolutionary process of a physical system and build the classifier by using the basic quantum mechanics equation. The performance of the experiments on two datasets indicates feasibility and potentiality of the quantum classifier.

15 acl-2013-A Novel Graph-based Compact Representation of Word Alignment

Author: Qun Liu ; Zhaopeng Tu ; Shouxun Lin

Abstract: In this paper, we propose a novel compact representation called weighted bipartite hypergraph to exploit the fertility model, which plays a critical role in word alignment. However, estimating the probabilities of rules extracted from hypergraphs is an NP-complete problem, which is computationally infeasible. Therefore, we propose a divide-and-conquer strategy by decomposing a hypergraph into a set of independent subhypergraphs. The experiments show that our approach outperforms both 1-best and n-best alignments.

16 acl-2013-A Novel Translation Framework Based on Rhetorical Structure Theory

Author: Mei Tu ; Yu Zhou ; Chengqing Zong

Abstract: Rhetorical structure theory (RST) is widely used for discourse understanding, which represents a discourse as a hierarchically semantic structure. In this paper, we propose a novel translation framework with the help of RST. In our framework, the translation process mainly includes three steps: 1) Source RST-tree acquisition: a source sentence is parsed into an RST tree; 2) Rule extraction: translation rules are extracted from the source tree and the target string via bilingual word alignment; 3) RST-based translation: the source RST-tree is translated with translation rules. Experiments on Chinese-to-English show that our RST-based approach achieves improvements of 2.3/0.77/1.43 BLEU points on NIST04/NIST05/CWMT2008 respectively. 1

17 acl-2013-A Random Walk Approach to Selectional Preferences Based on Preference Ranking and Propagation

Author: Zhenhua Tian ; Hengheng Xiang ; Ziqi Liu ; Qinghua Zheng

Abstract: This paper presents an unsupervised random walk approach to alleviate data sparsity for selectional preferences. Based on the measure of preferences between predicates and arguments, the model aggregates all the transitions from a given predicate to its nearby predicates, and propagates their argument preferences as the given predicate’s smoothed preferences. Experimental results show that this approach outperforms several state-of-the-art methods on the pseudo-disambiguation task, and it better correlates with human plausibility judgements.

18 acl-2013-A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization

Author: Lu Wang ; Hema Raghavan ; Vittorio Castelli ; Radu Florian ; Claire Cardie

Abstract: We consider the problem of using sentence compression techniques to facilitate queryfocused multi-document summarization. We present a sentence-compression-based framework for the task, and design a series of learning-based compression models built on parse trees. An innovative beam search decoder is proposed to efficiently find highly probable compressions. Under this framework, we show how to integrate various indicative metrics such as linguistic motivation and query relevance into the compression process by deriving a novel formulation of a compression scoring function. Our best model achieves statistically significant improvement over the state-of-the-art systems on several metrics (e.g. 8.0% and 5.4% improvements in ROUGE-2 respectively) for the DUC 2006 and 2007 summarization task. ,

19 acl-2013-A Shift-Reduce Parsing Algorithm for Phrase-based String-to-Dependency Translation

Author: Yang Liu

Abstract: We introduce a shift-reduce parsing algorithm for phrase-based string-todependency translation. As the algorithm generates dependency trees for partial translations left-to-right in decoding, it allows for efficient integration of both n-gram and dependency language models. To resolve conflicts in shift-reduce parsing, we propose a maximum entropy model trained on the derivation graph of training data. As our approach combines the merits of phrase-based and string-todependency models, it achieves significant improvements over the two baselines on the NIST Chinese-English datasets.

20 acl-2013-A Stacking-based Approach to Twitter User Geolocation Prediction

Author: Bo Han ; Paul Cook ; Timothy Baldwin

Abstract: We implement a city-level geolocation prediction system for Twitter users. The system infers a user’s location based on both tweet text and user-declared metadata using a stacking approach. We demonstrate that the stacking method substantially outperforms benchmark methods, achieving 49% accuracy on a benchmark dataset. We further evaluate our method on a recent crawl of Twitter data to investigate the impact of temporal factors on model generalisation. Our results suggest that user-declared location metadata is more sensitive to temporal change than the text of Twitter messages. We also describe two ways of accessing/demoing our system.

acl 2013 knowledge graph

similar papers computed by tfidf model

similar papers computed by lsi model

similar papers computed by lda model

papers list: