acl acl2012 knowledge-graph by maker-knowledge-mining

acl 2012 knowledge graph

papers list:

1 acl-2012-ACCURAT Toolkit for Multi-Level Alignment and Information Extraction from Comparable Corpora

Author: Marcis Pinnis ; Radu Ion ; Dan Stefanescu ; Fangzhong Su ; Inguna Skadina ; Andrejs Vasiljevs ; Bogdan Babych

Abstract: The lack of parallel corpora and linguistic resources for many languages and domains is one of the major obstacles for the further advancement of automated translation. A possible solution is to exploit comparable corpora (non-parallel bi- or multi-lingual text resources) which are much more widely available than parallel translation data. Our presented toolkit deals with parallel content extraction from comparable corpora. It consists of tools bundled in two workflows: (1) alignment of comparable documents and extraction of parallel sentences and (2) extraction and bilingual mapping of terms and named entities. The toolkit pairs similar bilingual comparable documents and extracts parallel sentences and bilingual terminological and named entity dictionaries from comparable corpora. This demonstration focuses on the English, Latvian, Lithuanian, and Romanian languages.

2 acl-2012-A Broad-Coverage Normalization System for Social Media Language

Author: Fei Liu ; Fuliang Weng ; Xiao Jiang

Abstract: Social media language contains huge amount and wide variety of nonstandard tokens, created both intentionally and unintentionally by the users. It is of crucial importance to normalize the noisy nonstandard tokens before applying other NLP techniques. A major challenge facing this task is the system coverage, i.e., for any user-created nonstandard term, the system should be able to restore the correct word within its top n output candidates. In this paper, we propose a cognitivelydriven normalization system that integrates different human perspectives in normalizing the nonstandard tokens, including the enhanced letter transformation, visual priming, and string/phonetic similarity. The system was evaluated on both word- and messagelevel using four SMS and Twitter data sets. Results show that our system achieves over 90% word-coverage across all data sets (a . 10% absolute increase compared to state-ofthe-art); the broad word-coverage can also successfully translate into message-level performance gain, yielding 6% absolute increase compared to the best prior approach.

3 acl-2012-A Class-Based Agreement Model for Generating Accurately Inflected Translations

Author: Spence Green ; John DeNero

Abstract: When automatically translating from a weakly inflected source language like English to a target language with richer grammatical features such as gender and dual number, the output commonly contains morpho-syntactic agreement errors. To address this issue, we present a target-side, class-based agreement model. Agreement is promoted by scoring a sequence of fine-grained morpho-syntactic classes that are predicted during decoding for each translation hypothesis. For English-to-Arabic translation, our model yields a +1.04 BLEU average improvement over a state-of-the-art baseline. The model does not require bitext or phrase table annotations and can be easily implemented as a feature in many phrase-based decoders. 1

4 acl-2012-A Comparative Study of Target Dependency Structures for Statistical Machine Translation

Author: Xianchao Wu ; Katsuhito Sudoh ; Kevin Duh ; Hajime Tsukada ; Masaaki Nagata

Abstract: This paper presents a comparative study of target dependency structures yielded by several state-of-the-art linguistic parsers. Our approach is to measure the impact of these nonisomorphic dependency structures to be used for string-to-dependency translation. Besides using traditional dependency parsers, we also use the dependency structures transformed from PCFG trees and predicate-argument structures (PASs) which are generated by an HPSG parser and a CCG parser. The experiments on Chinese-to-English translation show that the HPSG parser’s PASs achieved the best dependency and translation accuracies. 1

5 acl-2012-A Comparison of Chinese Parsers for Stanford Dependencies

Author: Wanxiang Che ; Valentin Spitkovsky ; Ting Liu

Abstract: Stanford dependencies are widely used in natural language processing as a semanticallyoriented representation, commonly generated either by (i) converting the output of a constituent parser, or (ii) predicting dependencies directly. Previous comparisons of the two approaches for English suggest that starting from constituents yields higher accuracies. In this paper, we re-evaluate both methods for Chinese, using more accurate dependency parsers than in previous work. Our comparison of performance and efficiency across seven popular open source parsers (four constituent and three dependency) shows, by contrast, that recent higher-order graph-based techniques can be more accurate, though somewhat slower, than constituent parsers. We demonstrate also that n-way jackknifing is a useful technique for producing automatic (rather than gold) partof-speech tags to train Chinese dependency parsers. Finally, we analyze the relations produced by both kinds of parsing and suggest which specific parsers to use in practice.

6 acl-2012-A Comprehensive Gold Standard for the Enron Organizational Hierarchy

Author: Apoorv Agarwal ; Adinoyi Omuya ; Aaron Harnly ; Owen Rambow

Abstract: Many researchers have attempted to predict the Enron corporate hierarchy from the data. This work, however, has been hampered by a lack of data. We present a new, large, and freely available gold-standard hierarchy. Using our new gold standard, we show that a simple lower bound for social network-based systems outperforms an upper bound on the approach taken by current NLP systems.

7 acl-2012-A Computational Approach to the Automation of Creative Naming

Author: Gozde Ozbal ; Carlo Strapparava

Abstract: In this paper, we propose a computational approach to generate neologisms consisting of homophonic puns and metaphors based on the category of the service to be named and the properties to be underlined. We describe all the linguistic resources and natural language processing techniques that we have exploited for this task. Then, we analyze the performance of the system that we have developed. The empirical results show that our approach is generally effective and it constitutes a solid starting point for the automation ofthe naming process.

8 acl-2012-A Corpus of Textual Revisions in Second Language Writing

Author: John Lee ; Jonathan Webster

Abstract: This paper describes the creation of the first large-scale corpus containing drafts and final versions of essays written by non-native speakers, with the sentences aligned across different versions. Furthermore, the sentences in the drafts are annotated with comments from teachers. The corpus is intended to support research on textual revision by language learners, and how it is influenced by feedback. This corpus has been converted into an XML format conforming to the standards of the Text Encoding Initiative (TEI).

9 acl-2012-A Cost Sensitive Part-of-Speech Tagging: Differentiating Serious Errors from Minor Errors

Author: Hyun-Je Song ; Jeong-Woo Son ; Tae-Gil Noh ; Seong-Bae Park ; Sang-Jo Lee

Abstract: All types of part-of-speech (POS) tagging errors have been equally treated by existing taggers. However, the errors are not equally important, since some errors affect the performance of subsequent natural language processing (NLP) tasks seriously while others do not. This paper aims to minimize these serious errors while retaining the overall performance of POS tagging. Two gradient loss functions are proposed to reflect the different types of errors. They are designed to assign a larger cost to serious errors and a smaller one to minor errors. Through a set of POS tagging experiments, it is shown that the classifier trained with the proposed loss functions reduces serious errors compared to state-of-the-art POS taggers. In addition, the experimental result on text chunking shows that fewer serious errors help to improve the performance of sub- sequent NLP tasks.

10 acl-2012-A Discriminative Hierarchical Model for Fast Coreference at Large Scale

Author: Michael Wick ; Sameer Singh ; Andrew McCallum

Abstract: Sameer Singh Andrew McCallum University of Massachusetts University of Massachusetts 140 Governor’s Drive 140 Governor’s Drive Amherst, MA Amherst, MA s ameer@ cs .umas s .edu mccal lum@ c s .umas s .edu Hamming” who authored “The unreasonable effectiveness of mathematics.” Features of the mentions Methods that measure compatibility between mention pairs are currently the dominant ap- proach to coreference. However, they suffer from a number of drawbacks including difficulties scaling to large numbers of mentions and limited representational power. As these drawbacks become increasingly restrictive, the need to replace the pairwise approaches with a more expressive, highly scalable alternative is becoming urgent. In this paper we propose a novel discriminative hierarchical model that recursively partitions entities into trees of latent sub-entities. These trees succinctly summarize the mentions providing a highly compact, information-rich structure for reasoning about entities and coreference uncertainty at massive scales. We demonstrate that the hierarchical model is several orders of magnitude faster than pairwise, allowing us to perform coreference on six million author mentions in under four hours on a single CPU.

11 acl-2012-A Feature-Rich Constituent Context Model for Grammar Induction

Author: Dave Golland ; John DeNero ; Jakob Uszkoreit

Abstract: We present LLCCM, a log-linear variant ofthe constituent context model (CCM) of grammar induction. LLCCM retains the simplicity of the original CCM but extends robustly to long sentences. On sentences of up to length 40, LLCCM outperforms CCM by 13.9% bracketing F1 and outperforms a right-branching baseline in regimes where CCM does not.

12 acl-2012-A Graph-based Cross-lingual Projection Approach for Weakly Supervised Relation Extraction

Author: Seokhwan Kim ; Gary Geunbae Lee

Abstract: Although researchers have conducted extensive studies on relation extraction in the last decade, supervised approaches are still limited because they require large amounts of training data to achieve high performances. To build a relation extractor without significant annotation effort, we can exploit cross-lingual annotation projection, which leverages parallel corpora as external resources for supervision. This paper proposes a novel graph-based projection approach and demonstrates the merits of it by using a Korean relation extraction system based on projected dataset from an English-Korean parallel corpus.

13 acl-2012-A Graphical Interface for MT Evaluation and Error Analysis

Author: Meritxell Gonzalez ; Jesus Gimenez ; Lluis Marquez

Abstract: Error analysis in machine translation is a necessary step in order to investigate the strengths and weaknesses of the MT systems under development and allow fair comparisons among them. This work presents an application that shows how a set of heterogeneous automatic metrics can be used to evaluate a test bed of automatic translations. To do so, we have set up an online graphical interface for the ASIYA toolkit, a rich repository of evaluation measures working at different linguistic levels. The current implementation of the interface shows constituency and dependency trees as well as shallow syntactic and semantic annotations, and word alignments. The intelligent visualization of the linguistic structures used by the metrics, as well as a set of navigational functionalities, may lead towards advanced methods for automatic error analysis.

14 acl-2012-A Joint Model for Discovery of Aspects in Utterances

Author: Asli Celikyilmaz ; Dilek Hakkani-Tur

Abstract: We describe a joint model for understanding user actions in natural language utterances. Our multi-layer generative approach uses both labeled and unlabeled utterances to jointly learn aspects regarding utterance’s target domain (e.g. movies), intention (e.g., finding a movie) along with other semantic units (e.g., movie name). We inject information extracted from unstructured web search query logs as prior information to enhance the generative process of the natural language utterance understanding model. Using utterances from five domains, our approach shows up to 4.5% improvement on domain and dialog act performance over cascaded approach in which each semantic component is learned sequentially and a supervised joint learning model (which requires fully labeled data).

15 acl-2012-A Meta Learning Approach to Grammatical Error Correction

Author: Hongsuck Seo ; Jonghoon Lee ; Seokhwan Kim ; Kyusong Lee ; Sechun Kang ; Gary Geunbae Lee

Abstract: We introduce a novel method for grammatical error correction with a number of small corpora. To make the best use of several corpora with different characteristics, we employ a meta-learning with several base classifiers trained on different corpora. This research focuses on a grammatical error correction task for article errors. A series of experiments is presented to show the effectiveness of the proposed approach on two different grammatical error tagged corpora. 1.

16 acl-2012-A Nonparametric Bayesian Approach to Acoustic Model Discovery

Author: Chia-ying Lee ; James Glass

Abstract: We investigate the problem of acoustic modeling in which prior language-specific knowledge and transcribed data are unavailable. We present an unsupervised model that simultaneously segments the speech, discovers a proper set of sub-word units (e.g., phones) and learns a Hidden Markov Model (HMM) for each induced acoustic unit. Our approach is formulated as a Dirichlet process mixture model in which each mixture is an HMM that represents a sub-word unit. We apply our model to the TIMIT corpus, and the results demonstrate that our model discovers sub-word units that are highly correlated with English phones and also produces better segmentation than the state-of-the-art unsupervised baseline. We test the quality of the learned acoustic models on a spoken term detection task. Compared to the baselines, our model improves the relative precision of top hits by at least 22.1% and outper- forms a language-mismatched acoustic model.

17 acl-2012-A Novel Burst-based Text Representation Model for Scalable Event Detection

Author: Xin Zhao ; Rishan Chen ; Kai Fan ; Hongfei Yan ; Xiaoming Li

Abstract: Mining retrospective events from text streams has been an important research topic. Classic text representation model (i.e., vector space model) cannot model temporal aspects of documents. To address it, we proposed a novel burst-based text representation model, denoted as BurstVSM. BurstVSM corresponds dimensions to bursty features instead of terms, which can capture semantic and temporal information. Meanwhile, it significantly reduces the number of non-zero entries in the representation. We test it via scalable event detection, and experiments in a 10-year news archive show that our methods are both effective and efficient.

18 acl-2012-A Probabilistic Model for Canonicalizing Named Entity Mentions

Author: Dani Yogatama ; Yanchuan Sim ; Noah A. Smith

Abstract: We present a statistical model for canonicalizing named entity mentions into a table whose rows represent entities and whose columns are attributes (or parts of attributes). The model is novel in that it incorporates entity context, surface features, firstorder dependencies among attribute-parts, and a notion of noise. Transductive learning from a few seeds and a collection of mention tokens combines Bayesian inference and conditional estimation. We evaluate our model and its components on two datasets collected from political blogs and sports news, finding that it outperforms a simple agglomerative clustering approach and previous work.

19 acl-2012-A Ranking-based Approach to Word Reordering for Statistical Machine Translation

Author: Nan Yang ; Mu Li ; Dongdong Zhang ; Nenghai Yu

Abstract: Long distance word reordering is a major challenge in statistical machine translation research. Previous work has shown using source syntactic trees is an effective way to tackle this problem between two languages with substantial word order difference. In this work, we further extend this line of exploration and propose a novel but simple approach, which utilizes a ranking model based on word order precedence in the target language to reposition nodes in the syntactic parse tree of a source sentence. The ranking model is automatically derived from word aligned parallel data with a syntactic parser for source language based on both lexical and syntactical features. We evaluated our approach on largescale Japanese-English and English-Japanese machine translation tasks, and show that it can significantly outperform the baseline phrase- based SMT system.

20 acl-2012-A Statistical Model for Unsupervised and Semi-supervised Transliteration Mining

Author: Hassan Sajjad ; Alexander Fraser ; Helmut Schmid

Abstract: We propose a novel model to automatically extract transliteration pairs from parallel corpora. Our model is efficient, language pair independent and mines transliteration pairs in a consistent fashion in both unsupervised and semi-supervised settings. We model transliteration mining as an interpolation of transliteration and non-transliteration sub-models. We evaluate on NEWS 2010 shared task data and on parallel corpora with competitive results.

21 acl-2012-A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle

22 acl-2012-A Topic Similarity Model for Hierarchical Phrase-based Translation

23 acl-2012-A Two-step Approach to Sentence Compression of Spoken Utterances

24 acl-2012-A Web-based Evaluation Framework for Spatial Instruction-Giving Systems

25 acl-2012-An Exploration of Forest-to-String Translation: Does Translation Help or Hurt Parsing?

26 acl-2012-Applications of GPC Rules and Character Structures in Games for Learning Chinese Characters

27 acl-2012-Arabic Retrieval Revisited: Morphological Hole Filling

28 acl-2012-Aspect Extraction through Semi-Supervised Modeling

29 acl-2012-Assessing the Effect of Inconsistent Assessors on Summarization Evaluation

30 acl-2012-Attacking Parsing Bottlenecks with Unlabeled Data and Relevant Factorizations

31 acl-2012-Authorship Attribution with Author-aware Topic Models

32 acl-2012-Automated Essay Scoring Based on Finite State Transducer: towards ASR Transcription of Oral English Speech