acl acl2010 knowledge-graph by maker-knowledge-mining

acl 2010 knowledge graph

papers list:

1 acl-2010-"Ask Not What Textual Entailment Can Do for You..."

Author: Mark Sammons ; V.G.Vinod Vydiswaran ; Dan Roth

Abstract: We challenge the NLP community to participate in a large-scale, distributed effort to design and build resources for developing and evaluating solutions to new and existing NLP tasks in the context of Recognizing Textual Entailment. We argue that the single global label with which RTE examples are annotated is insufficient to effectively evaluate RTE system performance; to promote research on smaller, related NLP tasks, we believe more detailed annotation and evaluation are needed, and that this effort will benefit not just RTE researchers, but the NLP community as a whole. We use insights from successful RTE systems to propose a model for identifying and annotating textual infer- ence phenomena in textual entailment examples, and we present the results of a pilot annotation study that show this model is feasible and the results immediately useful.

2 acl-2010-"Was It Good? It Was Provocative." Learning the Meaning of Scalar Adjectives

Author: Marie-Catherine de Marneffe ; Christopher D. Manning ; Christopher Potts

Abstract: Texts and dialogues often express information indirectly. For instance, speakers’ answers to yes/no questions do not always straightforwardly convey a ‘yes’ or ‘no’ answer. The intended reply is clear in some cases (Was it good? It was great!) but uncertain in others (Was it acceptable? It was unprecedented.). In this paper, we present methods for interpreting the answers to questions like these which involve scalar modifiers. We show how to ground scalar modifier meaning based on data collected from the Web. We learn scales between modifiers and infer the extent to which a given answer conveys ‘yes’ or ‘no’ . To evaluate the methods, we collected examples of question–answer pairs involving scalar modifiers from CNN transcripts and the Dialog Act corpus and use response distributions from Mechanical Turk workers to assess the degree to which each answer conveys ‘yes’ or ‘no’ . Our experimental results closely match the Turkers’ response data, demonstrating that meanings can be learned from Web data and that such meanings can drive pragmatic inference.

3 acl-2010-A Bayesian Method for Robust Estimation of Distributional Similarities

Author: Jun'ichi Kazama ; Stijn De Saeger ; Kow Kuroda ; Masaki Murata ; Kentaro Torisawa

Abstract: Existing word similarity measures are not robust to data sparseness since they rely only on the point estimation of words’ context profiles obtained from a limited amount of data. This paper proposes a Bayesian method for robust distributional word similarities. The method uses a distribution of context profiles obtained by Bayesian estimation and takes the expectation of a base similarity measure under that distribution. When the context profiles are multinomial distributions, the priors are Dirichlet, and the base measure is . the Bhattacharyya coefficient, we can derive an analytical form that allows efficient calculation. For the task of word similarity estimation using a large amount of Web data in Japanese, we show that the proposed measure gives better accuracies than other well-known similarity measures.

4 acl-2010-A Cognitive Cost Model of Annotations Based on Eye-Tracking Data

Author: Katrin Tomanek ; Udo Hahn ; Steffen Lohmann ; Jurgen Ziegler

Abstract: We report on an experiment to track complex decision points in linguistic metadata annotation where the decision behavior of annotators is observed with an eyetracking device. As experimental conditions we investigate different forms of textual context and linguistic complexity classes relative to syntax and semantics. Our data renders evidence that annotation performance depends on the semantic and syntactic complexity of the decision points and, more interestingly, indicates that fullscale context is mostly negligible with – the exception of semantic high-complexity cases. We then induce from this observational data a cognitively grounded cost model of linguistic meta-data annotations and compare it with existing non-cognitive models. Our data reveals that the cognitively founded model explains annotation costs (expressed in annotation time) more adequately than non-cognitive ones.

5 acl-2010-A Framework for Figurative Language Detection Based on Sense Differentiation

Author: Daria Bogdanova

Abstract: Various text mining algorithms require the process offeature selection. High-level semantically rich features, such as figurative language uses, speech errors etc., are very promising for such problems as e.g. writing style detection, but automatic extraction of such features is a big challenge. In this paper, we propose a framework for figurative language use detection. This framework is based on the idea of sense differentiation. We describe two algorithms illustrating the mentioned idea. We show then how these algorithms work by applying them to Russian language data.

6 acl-2010-A Game-Theoretic Model of Metaphorical Bargaining

Author: Beata Beigman Klebanov ; Eyal Beigman

Abstract: We present a game-theoretic model of bargaining over a metaphor in the context of political communication, find its equilibrium, and use it to rationalize observed linguistic behavior. We argue that game theory is well suited for modeling discourse as a dynamic resulting from a number of conflicting pressures, and suggest applications of interest to computational linguists.

7 acl-2010-A Generalized-Zero-Preserving Method for Compact Encoding of Concept Lattices

Author: Matthew Skala ; Victoria Krakovna ; Janos Kramar ; Gerald Penn

Abstract: Constructing an encoding of a concept lattice using short bit vectors allows for efficient computation of join operations on the lattice. Join is the central operation any unification-based parser must support. We extend the traditional bit vector encoding, which represents join failure using the zero vector, to count any vector with less than a fixed number of one bits as failure. This allows non-joinable elements to share bits, resulting in a smaller vector size. A constraint solver is used to construct the encoding, and a variety of techniques are employed to find near-optimal solutions and handle timeouts. An evaluation is provided comparing the extended representation of failure with traditional bit vector techniques.

8 acl-2010-A Hybrid Hierarchical Model for Multi-Document Summarization

Author: Asli Celikyilmaz ; Dilek Hakkani-Tur

Abstract: Scoring sentences in documents given abstract summaries created by humans is important in extractive multi-document summarization. In this paper, we formulate extractive summarization as a two step learning problem building a generative model for pattern discovery and a regression model for inference. We calculate scores for sentences in document clusters based on their latent characteristics using a hierarchical topic model. Then, using these scores, we train a regression model based on the lexical and structural characteristics of the sentences, and use the model to score sentences of new documents to form a summary. Our system advances current state-of-the-art improving ROUGE scores by ∼7%. Generated summaries are less rbeydu ∼n7d%an.t a Gnedn more dc sohuemremnatr bieasse adre upon manual quality evaluations.

9 acl-2010-A Joint Rule Selection Model for Hierarchical Phrase-Based Translation

Author: Lei Cui ; Dongdong Zhang ; Mu Li ; Ming Zhou ; Tiejun Zhao

Abstract: In hierarchical phrase-based SMT systems, statistical models are integrated to guide the hierarchical rule selection for better translation performance. Previous work mainly focused on the selection of either the source side of a hierarchical rule or the target side of a hierarchical rule rather than considering both of them simultaneously. This paper presents a joint model to predict the selection of hierarchical rules. The proposed model is estimated based on four sub-models where the rich context knowledge from both source and target sides is leveraged. Our method can be easily incorporated into the practical SMT systems with the log-linear model framework. The experimental results show that our method can yield significant improvements in performance.

10 acl-2010-A Latent Dirichlet Allocation Method for Selectional Preferences

Author: Alan Ritter ; Mausam Mausam ; Oren Etzioni

Abstract: The computation of selectional preferences, the admissible argument values for a relation, is a well-known NLP task with broad applicability. We present LDA-SP, which utilizes LinkLDA (Erosheva et al., 2004) to model selectional preferences. By simultaneously inferring latent topics and topic distributions over relations, LDA-SP combines the benefits of previous approaches: like traditional classbased approaches, it produces humaninterpretable classes describing each relation’s preferences, but it is competitive with non-class-based methods in predictive power. We compare LDA-SP to several state-ofthe-art methods achieving an 85% increase in recall at 0.9 precision over mutual information (Erk, 2007). We also evaluate LDA-SP’s effectiveness at filtering improper applications of inference rules, where we show substantial improvement over Pantel et al. ’s system (Pantel et al., 2007).

11 acl-2010-A New Approach to Improving Multilingual Summarization Using a Genetic Algorithm

Author: Marina Litvak ; Mark Last ; Menahem Friedman

Abstract: Automated summarization methods can be defined as “language-independent,” if they are not based on any languagespecific knowledge. Such methods can be used for multilingual summarization defined by Mani (2001) as “processing several languages, with summary in the same language as input.” In this paper, we introduce MUSE, a languageindependent approach for extractive summarization based on the linear optimization of several sentence ranking measures using a genetic algorithm. We tested our methodology on two languages—English and Hebrew—and evaluated its performance with ROUGE-1 Recall vs. state- of-the-art extractive summarization approaches. Our results show that MUSE performs better than the best known multilingual approach (TextRank1) in both languages. Moreover, our experimental results on a bilingual (English and Hebrew) document collection suggest that MUSE does not need to be retrained on each language and the same model can be used across at least two different languages.

12 acl-2010-A Probabilistic Generative Model for an Intermediate Constituency-Dependency Representation

Author: Federico Sangati

Abstract: We present a probabilistic model extension to the Tesni `ere Dependency Structure (TDS) framework formulated in (Sangati and Mazza, 2009). This representation incorporates aspects from both constituency and dependency theory. In addition, it makes use of junction structures to handle coordination constructions. We test our model on parsing the English Penn WSJ treebank using a re-ranking framework. This technique allows us to efficiently test our model without needing a specialized parser, and to use the standard evaluation metric on the original Phrase Structure version of the treebank. We obtain encouraging results: we achieve a small improvement over state-of-the-art results when re-ranking a small number of candidate structures, on all the evaluation metrics except for chunking.

13 acl-2010-A Rational Model of Eye Movement Control in Reading

Author: Klinton Bicknell ; Roger Levy

Abstract: A number of results in the study of realtime sentence comprehension have been explained by computational models as resulting from the rational use of probabilistic linguistic information. Many times, these hypotheses have been tested in reading by linking predictions about relative word difficulty to word-aggregated eye tracking measures such as go-past time. In this paper, we extend these results by asking to what extent reading is well-modeled as rational behavior at a finer level of analysis, predicting not aggregate measures, but the duration and location of each fixation. We present a new rational model of eye movement control in reading, the central assumption of which is that eye move- ment decisions are made to obtain noisy visual information as the reader performs Bayesian inference on the identities of the words in the sentence. As a case study, we present two simulations demonstrating that the model gives a rational explanation for between-word regressions.

14 acl-2010-A Risk Minimization Framework for Extractive Speech Summarization

Author: Shih-Hsiang Lin ; Berlin Chen

Abstract: In this paper, we formulate extractive summarization as a risk minimization problem and propose a unified probabilistic framework that naturally combines supervised and unsupervised summarization models to inherit their individual merits as well as to overcome their inherent limitations. In addition, the introduction of various loss functions also provides the summarization framework with a flexible but systematic way to render the redundancy and coherence relationships among sentences and between sentences and the whole document, respectively. Experiments on speech summarization show that the methods deduced from our framework are very competitive with existing summarization approaches. 1

15 acl-2010-A Semi-Supervised Key Phrase Extraction Approach: Learning from Title Phrases through a Document Semantic Network

Author: Decong Li ; Sujian Li ; Wenjie Li ; Wei Wang ; Weiguang Qu

Abstract: It is a fundamental and important task to extract key phrases from documents. Generally, phrases in a document are not independent in delivering the content of the document. In order to capture and make better use of their relationships in key phrase extraction, we suggest exploring the Wikipedia knowledge to model a document as a semantic network, where both n-ary and binary relationships among phrases are formulated. Based on a commonly accepted assumption that the title of a document is always elaborated to reflect the content of a document and consequently key phrases tend to have close semantics to the title, we propose a novel semi-supervised key phrase extraction approach in this paper by computing the phrase importance in the semantic network, through which the influence of title phrases is propagated to the other phrases iteratively. Experimental results demonstrate the remarkable performance of this approach. 1

16 acl-2010-A Statistical Model for Lost Language Decipherment

Author: Benjamin Snyder ; Regina Barzilay ; Kevin Knight

Abstract: In this paper we propose a method for the automatic decipherment of lost languages. Given a non-parallel corpus in a known related language, our model produces both alphabetic mappings and translations of words into their corresponding cognates. We employ a non-parametric Bayesian framework to simultaneously capture both low-level character mappings and highlevel morphemic correspondences. This formulation enables us to encode some of the linguistic intuitions that have guided human decipherers. When applied to the ancient Semitic language Ugaritic, the model correctly maps 29 of 30 letters to their Hebrew counterparts, and deduces the correct Hebrew cognate for 60% of the Ugaritic words which have cognates in Hebrew.

17 acl-2010-A Structured Model for Joint Learning of Argument Roles and Predicate Senses

Author: Yotaro Watanabe ; Masayuki Asahara ; Yuji Matsumoto

Abstract: In predicate-argument structure analysis, it is important to capture non-local dependencies among arguments and interdependencies between the sense of a predicate and the semantic roles of its arguments. However, no existing approach explicitly handles both non-local dependencies and semantic dependencies between predicates and arguments. In this paper we propose a structured model that overcomes the limitation of existing approaches; the model captures both types of dependencies simultaneously by introducing four types of factors including a global factor type capturing non-local dependencies among arguments and a pairwise factor type capturing local dependencies between a predicate and an argument. In experiments the proposed model achieved competitive results compared to the stateof-the-art systems without applying any feature selection procedure.

18 acl-2010-A Study of Information Retrieval Weighting Schemes for Sentiment Analysis

Author: Georgios Paltoglou ; Mike Thelwall

Abstract: Most sentiment analysis approaches use as baseline a support vector machines (SVM) classifier with binary unigram weights. In this paper, we explore whether more sophisticated feature weighting schemes from Information Retrieval can enhance classification accuracy. We show that variants of the classic tf.idf scheme adapted to sentiment analysis provide significant increases in accuracy, especially when using a sublinear function for term frequency weights and document frequency smoothing. The techniques are tested on a wide selection of data sets and produce the best accuracy to our knowledge.

19 acl-2010-A Taxonomy, Dataset, and Classifier for Automatic Noun Compound Interpretation

Author: Stephen Tratz ; Eduard Hovy

Abstract: The automatic interpretation of noun-noun compounds is an important subproblem within many natural language processing applications and is an area of increasing interest. The problem is difficult, with disagreement regarding the number and nature of the relations, low inter-annotator agreement, and limited annotated data. In this paper, we present a novel taxonomy of relations that integrates previous relations, the largest publicly-available annotated dataset, and a supervised classification method for automatic noun compound interpretation.

20 acl-2010-A Transition-Based Parser for 2-Planar Dependency Structures

Author: Carlos Gomez-Rodriguez ; Joakim Nivre

Abstract: Finding a class of structures that is rich enough for adequate linguistic representation yet restricted enough for efficient computational processing is an important problem for dependency parsing. In this paper, we present a transition system for 2-planar dependency trees trees that can be decomposed into at most two planar graphs and show that it can be used to implement a classifier-based parser that runs in linear time and outperforms a stateof-the-art transition-based parser on four data sets from the CoNLL-X shared task. In addition, we present an efficient method – – for determining whether an arbitrary tree is 2-planar and show that 99% or more of the trees in existing treebanks are 2-planar.

21 acl-2010-A Tree Transducer Model for Synchronous Tree-Adjoining Grammars

22 acl-2010-A Unified Graph Model for Sentence-Based Opinion Retrieval

23 acl-2010-Accurate Context-Free Parsing with Combinatory Categorial Grammar

24 acl-2010-Active Learning-Based Elicitation for Semi-Supervised Word Alignment

25 acl-2010-Adapting Self-Training for Semantic Role Labeling

26 acl-2010-All Words Domain Adapted WSD: Finding a Middle Ground between Supervision and Unsupervision

27 acl-2010-An Active Learning Approach to Finding Related Terms

28 acl-2010-An Entity-Level Approach to Information Extraction

29 acl-2010-An Exact A* Method for Deciphering Letter-Substitution Ciphers

30 acl-2010-An Open-Source Package for Recognizing Textual Entailment

31 acl-2010-Annotation

32 acl-2010-Arabic Named Entity Recognition: Using Features Extracted from Noisy Data