acl acl2011 knowledge-graph by maker-knowledge-mining

acl 2011 knowledge graph

papers list:

1 acl-2011-(11-06-spirl)

Author: (hal)

Abstract: unkown-abstract

2 acl-2011-AM-FM: A Semantic Framework for Translation Quality Assessment

Author: Rafael E. Banchs ; Haizhou Li

Abstract: This work introduces AM-FM, a semantic framework for machine translation evaluation. Based upon this framework, a new evaluation metric, which is able to operate without the need for reference translations, is implemented and evaluated. The metric is based on the concepts of adequacy and fluency, which are independently assessed by using a cross-language latent semantic indexing approach and an n-gram based language model approach, respectively. Comparative analyses with conventional evaluation metrics are conducted on two different evaluation tasks (overall quality assessment and comparative ranking) over a large collection of human evaluations involving five European languages. Finally, the main pros and cons of the proposed framework are discussed along with future research directions. 1

3 acl-2011-A Bayesian Model for Unsupervised Semantic Parsing

Author: Ivan Titov ; Alexandre Klementiev

Abstract: We propose a non-parametric Bayesian model for unsupervised semantic parsing. Following Poon and Domingos (2009), we consider a semantic parsing setting where the goal is to (1) decompose the syntactic dependency tree of a sentence into fragments, (2) assign each of these fragments to a cluster of semantically equivalent syntactic structures, and (3) predict predicate-argument relations between the fragments. We use hierarchical PitmanYor processes to model statistical dependencies between meaning representations of predicates and those of their arguments, as well as the clusters of their syntactic realizations. We develop a modification of the MetropolisHastings split-merge sampler, resulting in an efficient inference algorithm for the model. The method is experimentally evaluated by us- ing the induced semantic representation for the question answering task in the biomedical domain.

4 acl-2011-A Class of Submodular Functions for Document Summarization

Author: Hui Lin ; Jeff Bilmes

Abstract: We design a class of submodular functions meant for document summarization tasks. These functions each combine two terms, one which encourages the summary to be representative of the corpus, and the other which positively rewards diversity. Critically, our functions are monotone nondecreasing and submodular, which means that an efficient scalable greedy optimization scheme has a constant factor guarantee of optimality. When evaluated on DUC 2004-2007 corpora, we obtain better than existing state-of-art results in both generic and query-focused document summarization. Lastly, we show that several well-established methods for document summarization correspond, in fact, to submodular function optimization, adding further evidence that submodular functions are a natural fit for document summarization.

5 acl-2011-A Comparison of Loopy Belief Propagation and Dual Decomposition for Integrated CCG Supertagging and Parsing

Author: Michael Auli ; Adam Lopez

Abstract: Via an oracle experiment, we show that the upper bound on accuracy of a CCG parser is significantly lowered when its search space is pruned using a supertagger, though the supertagger also prunes many bad parses. Inspired by this analysis, we design a single model with both supertagging and parsing features, rather than separating them into distinct models chained together in a pipeline. To overcome the resulting increase in complexity, we experiment with both belief propagation and dual decomposition approaches to inference, the first empirical comparison of these algorithms that we are aware of on a structured natural language processing problem. On CCGbank we achieve a labelled dependency F-measure of 88.8% on gold POS tags, and 86.7% on automatic part-of-speeoch tags, the best reported results for this task.

6 acl-2011-A Comprehensive Dictionary of Multiword Expressions

Author: Kosho Shudo ; Akira Kurahone ; Toshifumi Tanabe

Abstract: It has been widely recognized that one of the most difficult and intriguing problems in natural language processing (NLP) is how to cope with idiosyncratic multiword expressions. This paper presents an overview of the comprehensive dictionary (JDMWE) of Japanese multiword expressions. The JDMWE is characterized by a large notational, syntactic, and semantic diversity of contained expressions as well as a detailed description of their syntactic functions, structures, and flexibilities. The dictionary contains about 104,000 expressions, potentially 750,000 expressions. This paper shows that the JDMWE’s validity can be supported by comparing the dictionary with a large-scale Japanese N-gram frequency dataset, namely the LDC2009T08, generated by Google Inc. (Kudo et al. 2009). 1

7 acl-2011-A Corpus for Modeling Morpho-Syntactic Agreement in Arabic: Gender, Number and Rationality

Author: Sarah Alkuhlani ; Nizar Habash

Abstract: We present an enriched version of the Penn Arabic Treebank (Maamouri et al., 2004), where latent features necessary for modeling morpho-syntactic agreement in Arabic are manually annotated. We describe our process for efficient annotation, and present the first quantitative analysis of Arabic morphosyntactic phenomena.

8 acl-2011-A Corpus of Scope-disambiguated English Text

Author: Mehdi Manshadi ; James Allen ; Mary Swift

Abstract: Previous work on quantifier scope annotation focuses on scoping sentences with only two quantified noun phrases (NPs), where the quantifiers are restricted to a predefined list. It also ignores negation, modal/logical operators, and other sentential adverbials. We present a comprehensive scope annotation scheme. We annotate the scope interaction between all scopal terms in the sentence from quantifiers to scopal adverbials, without putting any restriction on the number of scopal terms in a sentence. In addition, all NPs, explicitly quantified or not, with no restriction on the type of quantification, are investigated for possible scope interactions. 1

9 acl-2011-A Cross-Lingual ILP Solution to Zero Anaphora Resolution

Author: Ryu Iida ; Massimo Poesio

Abstract: We present an ILP-based model of zero anaphora detection and resolution that builds on the joint determination of anaphoricity and coreference model proposed by Denis and Baldridge (2007), but revises it and extends it into a three-way ILP problem also incorporating subject detection. We show that this new model outperforms several baselines and competing models, as well as a direct translation of the Denis / Baldridge model, for both Italian and Japanese zero anaphora. We incorporate our model in complete anaphoric resolvers for both Italian and Japanese, showing that our approach leads to improved performance also when not used in isolation, provided that separate classifiers are used for zeros and for ex- plicitly realized anaphors.

10 acl-2011-A Discriminative Model for Joint Morphological Disambiguation and Dependency Parsing

Author: John Lee ; Jason Naradowsky ; David A. Smith

Abstract: Most previous studies of morphological disambiguation and dependency parsing have been pursued independently. Morphological taggers operate on n-grams and do not take into account syntactic relations; parsers use the “pipeline” approach, assuming that morphological information has been separately obtained. However, in morphologically-rich languages, there is often considerable interaction between morphology and syntax, such that neither can be disambiguated without the other. In this paper, we propose a discriminative model that jointly infers morphological properties and syntactic structures. In evaluations on various highly-inflected languages, this joint model outperforms both a baseline tagger in morphological disambiguation, and a pipeline parser in head selection.

11 acl-2011-A Fast and Accurate Method for Approximate String Search

Author: Ziqi Wang ; Gu Xu ; Hang Li ; Ming Zhang

Abstract: This paper proposes a new method for approximate string search, specifically candidate generation in spelling error correction, which is a task as follows. Given a misspelled word, the system finds words in a dictionary, which are most “similar” to the misspelled word. The paper proposes a probabilistic approach to the task, which is both accurate and efficient. The approach includes the use of a log linear model, a method for training the model, and an algorithm for finding the top k candidates. The log linear model is defined as a conditional probability distribution of a corrected word and a rule set for the correction conditioned on the misspelled word. The learning method employs the criterion in candidate generation as loss function. The retrieval algorithm is efficient and is guaranteed to find the optimal k candidates. Experimental results on large scale data show that the proposed approach improves upon existing methods in terms of accuracy in different settings.

12 acl-2011-A Generative Entity-Mention Model for Linking Entities with Knowledge Base

Author: Xianpei Han ; Le Sun

Abstract: Linking entities with knowledge base (entity linking) is a key issue in bridging the textual data with the structural knowledge base. Due to the name variation problem and the name ambiguity problem, the entity linking decisions are critically depending on the heterogenous knowledge of entities. In this paper, we propose a generative probabilistic model, called entitymention model, which can leverage heterogenous entity knowledge (including popularity knowledge, name knowledge and context knowledge) for the entity linking task. In our model, each name mention to be linked is modeled as a sample generated through a three-step generative story, and the entity knowledge is encoded in the distribution of entities in document P(e), the distribution of possible names of a specific entity P(s|e), and the distribution of possible contexts of a specific entity P(c|e). To find the referent entity of a name mention, our method combines the evidences from all the three distributions P(e), P(s|e) and P(c|e). Experimental results show that our method can significantly outperform the traditional methods. 1

13 acl-2011-A Graph Approach to Spelling Correction in Domain-Centric Search

Author: Zhuowei Bao ; Benny Kimelfeld ; Yunyao Li

Abstract: Spelling correction for keyword-search queries is challenging in restricted domains such as personal email (or desktop) search, due to the scarcity of query logs, and due to the specialized nature of the domain. For that task, this paper presents an algorithm that is based on statistics from the corpus data (rather than the query log). This algorithm, which employs a simple graph-based approach, can incorporate different types of data sources with different levels of reliability (e.g., email subject vs. email body), and can handle complex spelling errors like splitting and merging of words. An experimental study shows the superiority of the algorithm over existing alternatives in the email domain.

14 acl-2011-A Hierarchical Model of Web Summaries

Author: Yves Petinot ; Kathleen McKeown ; Kapil Thadani

Abstract: We investigate the relevance of hierarchical topic models to represent the content of Web gists. We focus our attention on DMOZ, a popular Web directory, and propose two algorithms to infer such a model from its manually-curated hierarchy of categories. Our first approach, based on information-theoretic grounds, uses an algorithm similar to recursive feature selection. Our second approach is fully Bayesian and derived from the more general model, hierarchical LDA. We evaluate the performance of both models against a flat 1-gram baseline and show improvements in terms of perplexity over held-out data.

15 acl-2011-A Hierarchical Pitman-Yor Process HMM for Unsupervised Part of Speech Induction

Author: Phil Blunsom ; Trevor Cohn

Abstract: In this work we address the problem of unsupervised part-of-speech induction by bringing together several strands of research into a single model. We develop a novel hidden Markov model incorporating sophisticated smoothing using a hierarchical Pitman-Yor processes prior, providing an elegant and principled means of incorporating lexical characteristics. Central to our approach is a new type-based sampling algorithm for hierarchical Pitman-Yor models in which we track fractional table counts. In an empirical evaluation we show that our model consistently out-performs the current state-of-the-art across 10 languages.

16 acl-2011-A Joint Sequence Translation Model with Integrated Reordering

Author: Nadir Durrani ; Helmut Schmid ; Alexander Fraser

Abstract: We present a novel machine translation model which models translation by a linear sequence of operations. In contrast to the “N-gram” model, this sequence includes not only translation but also reordering operations. Key ideas of our model are (i) a new reordering approach which better restricts the position to which a word or phrase can be moved, and is able to handle short and long distance reorderings in a unified way, and (ii) a joint sequence model for the translation and reordering probabilities which is more flexible than standard phrase-based MT. We observe statistically significant improvements in BLEU over Moses for German-to-English and Spanish-to-English tasks, and comparable results for a French-to-English task.

17 acl-2011-A Large Scale Distributed Syntactic, Semantic and Lexical Language Model for Machine Translation

Author: Ming Tan ; Wenli Zhou ; Lei Zheng ; Shaojun Wang

Abstract: This paper presents an attempt at building a large scale distributed composite language model that simultaneously accounts for local word lexical information, mid-range sentence syntactic structure, and long-span document semantic content under a directed Markov random field paradigm. The composite language model has been trained by performing a convergent N-best list approximate EM algorithm that has linear time complexity and a followup EM algorithm to improve word prediction power on corpora with up to a billion tokens and stored on a supercomputer. The large scale distributed composite language model gives drastic perplexity reduction over ngrams and achieves significantly better translation quality measured by the BLEU score and “readability” when applied to the task of re-ranking the N-best list from a state-of-the- art parsing-based machine translation system.

18 acl-2011-A Latent Topic Extracting Method based on Events in a Document and its Application

Author: Risa Kitajima ; Ichiro Kobayashi

Abstract: Recently, several latent topic analysis methods such as LSI, pLSI, and LDA have been widely used for text analysis. However, those methods basically assign topics to words, but do not account for the events in a document. With this background, in this paper, we propose a latent topic extracting method which assigns topics to events. We also show that our proposed method is useful to generate a document summary based on a latent topic.

19 acl-2011-A Mobile Touchable Application for Online Topic Graph Extraction and Exploration of Web Content

Author: Gunter Neumann ; Sven Schmeier

Abstract: We present a mobile touchable application for online topic graph extraction and exploration of web content. The system has been implemented for operation on an iPad. The topic graph is constructed from N web snippets which are determined by a standard search engine. We consider the extraction of a topic graph as a specific empirical collocation extraction task where collocations are extracted between chunks. Our measure of association strength is based on the pointwise mutual information between chunk pairs which explicitly takes their distance into account. An initial user evaluation shows that this system is especially helpful for finding new interesting information on topics about which the user has only a vague idea or even no idea at all.

20 acl-2011-A New Dataset and Method for Automatically Grading ESOL Texts

Author: Helen Yannakoudakis ; Ted Briscoe ; Ben Medlock

Abstract: We demonstrate how supervised discriminative machine learning techniques can be used to automate the assessment of ‘English as a Second or Other Language’ (ESOL) examination scripts. In particular, we use rank preference learning to explicitly model the grade relationships between scripts. A number of different features are extracted and ablation tests are used to investigate their contribution to overall performance. A comparison between regression and rank preference models further supports our method. Experimental results on the first publically available dataset show that our system can achieve levels of performance close to the upper bound for the task, as defined by the agreement between human examiners on the same corpus. Finally, using a set of ‘outlier’ texts, we test the validity of our model and identify cases where the model’s scores diverge from that of a human examiner.

acl 2011 knowledge graph

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