acl acl2013 acl2013-171 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Yuanbin Wu ; Hwee Tou Ng
Abstract: unkown-abstract
Reference: text
sentIndex sentText sentNum sentScore
1 1456 Proce dingsS o f ita h,e B 5u1lgsta Arinan,u Aaulg Musete 4ti-n9g 2 o0f1 t3h. [sent-8118, score-0.029]
2 Ac s2s0o1ci3a Atiosnso fcoirat Cio nm foprut Caotimonpaulta Lti nognuails Lti cnsg,u piasgteics 1456–1465, ? [sent-8120, score-0.13]
wordName wordTfidf (topN-words)
[('zv', 0.643), ('zu', 0.573), ('lk', 0.296), ('zk', 0.279), ('tg', 0.133), ('max', 0.123), ('tf', 0.088), ('ss', 0.075), ('arg', 0.07), ('wv', 0.07), ('kar', 0.066), ('ax', 0.059), ('gs', 0.059), ('fm', 0.057), ('hc', 0.052), ('lti', 0.049), ('ll', 0.047), ('pl', 0.047), ('kl', 0.046), ('cio', 0.03), ('dingss', 0.03), ('ita', 0.029), ('nm', 0.028), ('piasgteics', 0.027), ('wu', 0.027), ('aaulg', 0.025), ('atiosnso', 0.025), ('caotimonpaulta', 0.025), ('fcoirat', 0.025), ('foprut', 0.025), ('musete', 0.025), ('nognuails', 0.025), ('st', 0.019), ('ac', 0.016), ('proce', 0.006)]
simIndex simValue paperId paperTitle
same-paper 1 1.0 171 acl-2013-Grammatical Error Correction Using Integer Linear Programming
Author: Yuanbin Wu ; Hwee Tou Ng
Abstract: unkown-abstract
2 0.058091074 82 acl-2013-Co-regularizing character-based and word-based models for semi-supervised Chinese word segmentation
Author: Xiaodong Zeng ; Derek F. Wong ; Lidia S. Chao ; Isabel Trancoso
Abstract: This paper presents a semi-supervised Chinese word segmentation (CWS) approach that co-regularizes character-based and word-based models. Similarly to multi-view learning, the “segmentation agreements” between the two different types of view are used to overcome the scarcity of the label information on unlabeled data. The proposed approach trains a character-based and word-based model on labeled data, respectively, as the initial models. Then, the two models are constantly updated using unlabeled examples, where the learning objective is maximizing their segmentation agreements. The agreements are regarded as a set of valuable constraints for regularizing the learning of both models on unlabeled data. The segmentation for an input sentence is decoded by using a joint scoring function combining the two induced models. The evaluation on the Chinese tree bank reveals that our model results in better gains over the state-of-the-art semi-supervised models reported in the literature.
3 0.040332783 296 acl-2013-Recognizing Identical Events with Graph Kernels
Author: Goran Glavas ; Jan Snajder
Abstract: Identifying news stories that discuss the same real-world events is important for news tracking and retrieval. Most existing approaches rely on the traditional vector space model. We propose an approach for recognizing identical real-world events based on a structured, event-oriented document representation. We structure documents as graphs of event mentions and use graph kernels to measure the similarity between document pairs. Our experiments indicate that the proposed graph-based approach can outperform the traditional vector space model, and is especially suitable for distinguishing between topically similar, yet non-identical events.
4 0.032160945 351 acl-2013-Topic Modeling Based Classification of Clinical Reports
Author: Efsun Sarioglu ; Kabir Yadav ; Hyeong-Ah Choi
Abstract: Kabir Yadav Emergency Medicine Department The George Washington University Washington, DC, USA kyadav@ gwu . edu Hyeong-Ah Choi Computer Science Department The George Washington University Washington, DC, USA hcho i gwu . edu @ such as recommending the need for a certain medical test while avoiding intrusive tests or medical Electronic health records (EHRs) contain important clinical information about pa- tients. Some of these data are in the form of free text and require preprocessing to be able to used in automated systems. Efficient and effective use of this data could be vital to the speed and quality of health care. As a case study, we analyzed classification of CT imaging reports into binary categories. In addition to regular text classification, we utilized topic modeling of the entire dataset in various ways. Topic modeling of the corpora provides interpretable themes that exist in these reports. Representing reports according to their topic distributions is more compact than bag-of-words representation and can be processed faster than raw text in subsequent automated processes. A binary topic model was also built as an unsupervised classification approach with the assumption that each topic corresponds to a class. And, finally an aggregate topic classifier was built where reports are classified based on a single discriminative topic that is determined from the training dataset. Our proposed topic based classifier system is shown to be competitive with existing text classification techniques and provides a more efficient and interpretable representation.
5 0.025478201 48 acl-2013-An Open Source Toolkit for Quantitative Historical Linguistics
Author: Johann-Mattis List ; Steven Moran
Abstract: Given the increasing interest and development of computational and quantitative methods in historical linguistics, it is important that scholars have a basis for documenting, testing, evaluating, and sharing complex workflows. We present a novel open-source toolkit for quantitative tasks in historical linguistics that offers these features. This toolkit also serves as an interface between existing software packages and frequently used data formats, and it provides implementations of new and existing algorithms within a homogeneous framework. We illustrate the toolkit’s functionality with an exemplary workflow that starts with raw language data and ends with automatically calculated phonetic alignments, cognates and borrowings. We then illustrate evaluation metrics on gold standard datasets that are provided with the toolkit.
6 0.022375088 197 acl-2013-Incremental Topic-Based Translation Model Adaptation for Conversational Spoken Language Translation
7 0.021753617 157 acl-2013-Fast and Robust Compressive Summarization with Dual Decomposition and Multi-Task Learning
8 0.017890455 320 acl-2013-Shallow Local Multi-Bottom-up Tree Transducers in Statistical Machine Translation
9 0.017864905 35 acl-2013-Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation
10 0.016606014 143 acl-2013-Exact Maximum Inference for the Fertility Hidden Markov Model
11 0.015017689 238 acl-2013-Measuring semantic content in distributional vectors
12 0.013242043 302 acl-2013-Robust Automated Natural Language Processing with Multiword Expressions and Collocations
13 0.012522177 86 acl-2013-Combining Referring Expression Generation and Surface Realization: A Corpus-Based Investigation of Architectures
14 0.012346482 309 acl-2013-Scaling Semi-supervised Naive Bayes with Feature Marginals
15 0.01229436 378 acl-2013-Using subcategorization knowledge to improve case prediction for translation to German
16 0.012095448 374 acl-2013-Using Context Vectors in Improving a Machine Translation System with Bridge Language
17 0.011667898 74 acl-2013-Building Comparable Corpora Based on Bilingual LDA Model
18 0.011630127 264 acl-2013-Online Relative Margin Maximization for Statistical Machine Translation
19 0.011079919 221 acl-2013-Learning Non-linear Features for Machine Translation Using Gradient Boosting Machines
20 0.01093802 358 acl-2013-Transition-based Dependency Parsing with Selectional Branching
topicId topicWeight
[(0, 0.015), (1, -0.004), (2, -0.004), (3, 0.004), (4, 0.006), (5, 0.01), (6, -0.002), (7, 0.01), (8, -0.022), (9, 0.005), (10, -0.002), (11, 0.002), (12, 0.018), (13, -0.022), (14, -0.008), (15, -0.005), (16, 0.022), (17, 0.006), (18, 0.018), (19, 0.022), (20, -0.017), (21, -0.012), (22, -0.005), (23, -0.001), (24, -0.009), (25, 0.017), (26, -0.011), (27, -0.034), (28, -0.019), (29, -0.013), (30, 0.018), (31, 0.012), (32, -0.008), (33, 0.029), (34, 0.022), (35, -0.016), (36, 0.008), (37, -0.022), (38, 0.046), (39, 0.059), (40, -0.048), (41, 0.007), (42, -0.017), (43, -0.014), (44, -0.018), (45, 0.011), (46, -0.0), (47, -0.023), (48, 0.02), (49, 0.005)]
simIndex simValue paperId paperTitle
same-paper 1 0.98870534 171 acl-2013-Grammatical Error Correction Using Integer Linear Programming
Author: Yuanbin Wu ; Hwee Tou Ng
Abstract: unkown-abstract
2 0.3698051 257 acl-2013-Natural Language Models for Predicting Programming Comments
Author: Dana Movshovitz-Attias ; William W. Cohen
Abstract: Statistical language models have successfully been used to describe and analyze natural language documents. Recent work applying language models to programming languages is focused on the task of predicting code, while mainly ignoring the prediction of programmer comments. In this work, we predict comments from JAVA source files of open source projects, using topic models and n-grams, and we analyze the performance of the models given varying amounts of background data on the project being predicted. We evaluate models on their comment-completion capability in a setting similar to codecompletion tools built into standard code editors, and show that using a comment completion tool can save up to 47% of the comment typing. 1 Introduction and Related Work Statistical language models have traditionally been used to describe and analyze natural language documents. Recently, software engineering researchers have adopted the use of language models for modeling software code. Hindle et al. (2012) observe that, as code is created by humans it is likely to be repetitive and predictable, similar to natural language. NLP models have thus been used for a variety of software development tasks such as code token completion (Han et al., 2009; Jacob and Tairas, 2010), analysis of names in code (Lawrie et al., 2006; Binkley et al., 2011) and mining software repositories (Gabel and Su, 2008). An important part of software programming and maintenance lies in documentation, which may come in the form of tutorials describing the code, or inline comments provided by the programmer. The documentation provides a high level description of the task performed by the code, and may William W. Cohen Computer Science Department Carnegie Mellon University wcohen @ c s .cmu .edu include examples of use-cases for specific code segments or identifiers such as classes, methods and variables. Well documented code is easier to read and maintain in the long-run but writing comments is a laborious task that is often overlooked or at least postponed by many programmers. Code commenting not only provides a summarization of the conceptual idea behind the code (Sridhara et al., 2010), but can also be viewed as a form of document expansion where the comment contains significant terms relevant to the described code. Accurately predicted comment words can therefore be used for a variety of linguistic uses including improved search over code bases using natural language queries, code categorization, and locating parts of the code that are relevant to a specific topic or idea (Tseng and Juang, 2003; Wan et al., 2007; Kumar and Carterette, 2013; Shepherd et al., 2007; Rastkar et al., 2011). A related and well studied NLP task is that of predicting natural language caption and commentary for images and videos (Blei and Jordan, 2003; Feng and Lapata, 2010; Feng and Lapata, 2013; Wu and Li, 2011). In this work, our goal is to apply statistical language models for predicting class comments. We show that n-gram models are extremely successful in this task, and can lead to a saving of up to 47% in comment typing. This is expected as n-grams have been shown as a strong model for language and speech prediction that is hard to improve upon (Rosenfeld, 2000). In some cases however, for example in a document expansion task, we wish to extract important terms relevant to the code regardless of local syntactic dependencies. We hence also evaluate the use of LDA (Blei et al., 2003) and link-LDA (Erosheva et al., 2004) topic models, which are more relevant for the term ex- traction scenario. We find that the topic model performance can be improved by distinguishing code and text tokens in the code. 35 Proce dinSgosfi oa,f tB huel 5g1arsita, An Anu gauls Mt 4e-e9ti n2g01 o3f. th ?c e2 A0s1s3oc Aiastsio cnia fotiron C fo mrp Cuotmatpiounta tlio Lninaglu Li sntgicusi,s ptaicgses 35–40, 2 Method 2.1 Models We train n-gram models (n = 1, 2, 3) over source code documents containing sequences of combined code and text tokens from multiple training datasets (described below). We use the Berkeley Language Model package (Pauls and Klein, 2011) with absolute discounting (Kneser-Ney smoothing; (1995)) which includes a backoff strategy to lower-order n-grams. Next, we use LDA topic models (Blei et al., 2003) trained on the same data, with 1, 5, 10 and 20 topics. The joint distribution of a topic mixture θ, and a set of N topics z, for a single source code document with N observed word tokens, d = {wi}iN=1, given the Dirichlet parameters α sa,n dd β, {isw th}erefore p(θ, z, w|α, β) = p(θ|α) Yp(z|θ)p(w|z, (1) β) Yw Under the models described so far, there is no distinction between text and code tokens. Finally, we consider documents as having a mixed membership of two entity types, code and text tokens, d = where tthexet text ws,o drd =s are tok}ens f,r{owm comment and string literals, and the code words include the programming language syntax tokens (e.g., publ ic, private, for, etc’ ) and all identifiers. In this case, we train link-LDA models (Erosheva et al., 2004) with 1, 5, 10 and 20 topics. Under the linkLDA model, the mixed-membership joint distribution of a topic mixture, words and topics is then ({wciode}iC=n1, {witext}iT=n1), p(θ, z, w|α, β) = p(θ|α) Y wYtext · p(ztext|θ)p(wtext|ztext,β)· (2) Y p(zcode|θ)p(wcode|zcode,β) wYcode where θ is the joint topic distribution, w is the set of observed document words, ztext is a topic associated with a text word, and zcode a topic associated with a code word. The LDA and link-LDA models use Gibbs sampling (Griffiths and Steyvers, 2004) for topic inference, based on the implementation of Balasubramanyan and Cohen (201 1) with single or multiple entities per document, respectively. 2.2 Testing Methodology Our goal is to predict the tokens of the JAVA class comment (the one preceding the class definition) in each of the test files. Each of the models described above assigns a probability to the next comment token. In the case of n-grams, the probability of a token word wi is given by considering previous words p(wi |wi−1 , . . . , w0). This probability is estimated given the previous n 1tokens as p(wi|wi−1, wi−(n−1)). For t|hwe topic models, we separate the docu- ..., − ment tokens into the class definition and the comment we wish to predict. The set of tokens of the class comment are all considered as text tokens. The rest of the tokens in the document are considered to be the class definition, and they may contain both code and text tokens (from string literals and other comments in the source file). We then compute the posterior probability of document topics by solving the following inference problem conditioned on the tokens wc, wr, wr p(θ,zr|wr,α,β) =p(θp,(zwr,rw|αr,|αβ),β) (3) This gives us an estimate of the document distribution, θ, with which we infer the probability of the comment tokens as p(wc|θ,β) = Xp(wc|z,β)p(z|θ) (4) Xz Following Blei et al. (2003), for the case of a single entity LDA, the inference problem from equation (3) can be solved by considering p(θ, z, w|α, β), as in equation (1), and by taking tph(eθ marginal )di,s atrsib iunti eoqnu aotfio othne ( 1d)o,c aunmde bnyt t toakkeinngs as a continuous mixture distribution for the set w = by integrating over θ and summing over the set of topics z wr, p(w|α,β) =Zp(θ|α)· (5) YwXzp(z|θ)p(w|z,β)!dθ For the case of link-LDA where the document is comprised of two entities, in our case code tokens and text tokens, we can consider the mixedmembership joint distribution θ, as in equation (2), and similarly the marginal distribution p(w|α, β) over bimoithla rclyod teh ean mda tregxint tlok deisntsri bfruotmion w pr(.w |Sαi,nβce) comment words in are all considered as text tokens they are sampled using text topics, namely ztext, in equation (4). wc 36 3 Experimental Settings 3.1 Data and Training Methodology We use source code from nine open source JAVA projects: Ant, Cassandra, Log4j, Maven, MinorThird, Batik, Lucene, Xalan and Xerces. For each project, we divide the source files into a training and testing dataset. Then, for each project in turn, we consider the following three main training scenarios, leading to using three training datasets. To emulate a scenario in which we are predicting comments in the middle of project development, we can use data (documented code) from the same project. In this case, we use the in-project training dataset (IN). Alternatively, if we train a comment prediction model at the beginning of the development, we need to use source files from other, possibly related projects. To analyze this scenario, for each of the projects above we train models using an out-of-project dataset (OUT) containing data from the other eight projects. Typically, source code files contain a greater amount ofcode versus comment text. Since we are interested in predicting comments, we consider a third training data source which contains more English text as well as some code segments. We use data from the popular Q&A; website StackOverflow (SO) where users ask and answer technical questions about software development, tools, algorithms, etc’ . We downloaded a dataset of all actions performed on the site since it was launched in August 2008 until August 2012. The data includes 3,453,742 questions and 6,858,133 answers posted by 1,295,620 users. We used only posts that are tagged as JAVA related questions and answers. All the models for each project are then tested on the testing set of that project. We report results averaged over all projects in Table 1. Source files were tokenized using the Eclipse JDT compiler tools, separating code tokens and identifiers. Identifier names (of classes, methods and variables), were further tokenized by camel case notation (e.g., ’minMargin’ was converted to ’min margin’). Non alpha-numeric tokens (e.g., dot, semicolon) were discarded from the code, as well as numeric and single character literals. Text from comments or any string literals within the code were further tokenized with the Mallet statistical natural language processing package (Mc- Callum, 2002). Posts from SO were parsed using the Apache Tika toolkit1 and then tokenized with the Mallet package. We considered as raw code tokens anything labeled using a markup (as indicated by the SO users who wrote the post). 3.2 Evaluation Since our models are trained using various data sources the vocabularies used by each of them are different, making the comment likelihood given by each model incomparable due to different sets of out-of-vocabulary tokens. We thus evaluate models using a character saving metric which aims at quantifying the percentage of characters that can be saved by using the model in a word-completion settings, similar to standard code completion tools built into code editors. For a comment word with n characters, w = w1, . . . , wn, we predict the two most likely words given each model filtered by the first 0, . . . , n characters ofw. Let k be the minimal ki for which w is in the top two predicted word tokens where tokens are filtered by the first ki characters. Then, the number of saved characters for w is n k. In Table 1we report the average percentage o−f ksa.v Iend T Tcahbalera 1cte wrse per ocrotm thmee avnet using eearcchen not-f the above models. The final results are also averaged over the nine input projects. As an example, in the predicted comment shown in Table 2, taken from the project Minor-Third, the token entity is the most likely token according to the model SO trigram, out of tokens starting with the prefix ’en’ . The saved characters in this case are ’tity’ . − 4 Results Table 1 displays the average percentage of characters saved per class comment using each of the models. Models trained on in-project data (IN) perform significantly better than those trained on another data source, regardless of the model type, with an average saving of 47. 1% characters using a trigram model. This is expected, as files from the same project are likely to contain similar comments, and identifier names that appear in the comment of one class may appear in the code of another class in the same project. Clearly, in-project data should be used when available as it improves comment prediction leading to an average increase of between 6% for the worst model (26.6 for OUT unigram versus 33.05 for IN) and 14% for the best (32.96 for OUT trigram versus 47. 1for IN). 1http://tika.apache.org/ 37 Model n / topics n-gram LDA Link-LDA 1 2 3 20 10 5 1 20 10 5 1 IN 33.05 (3.62) 43.27 (5.79) 47.1 (6.87) 34.20 (3.63) 33.93 (3.67) 33.63 (3.67) 33.05 (3.62) 35.76 (3.95) 35.81 (4.12) 35.37 (3.98) 34.59 (3.92) OUT 26.6 (3.37) 31.52 (4.17) 32.96 (4.33) 26.79 (3.26) 26.8 (3.36) 26.86 (3.44) 26.6 (3.37) 28.03 (3.60) 28 (3.56) 28 (3.67) 27.82 (3.62) SO 27.8 (3.51) 33.29 (4.40) 34.56 (4.78) 27.25 (3.67) 27.22 (3.44) 27.34 (3.55) 27.8 (3.51) 28.08 (3.48) 28.12 (3.58) 27.94 (3.56) 27.9 (3.45) Table 1: Average percentage of characters saved per comment using n-gram, LDA and link-LDA models trained on three training sets: IN, OUT, and SO. The results are averaged over nine JAVA projects (with standard deviations in parenthesis). Model Predicted Comment trigram IN link-LDA OUT trigram SO trigram “Train “Train “Train “Train IN named-entity a named-entity a named-entity a named-entity a extractor“ extractor“ extractor“ extractor“ Table 2: Sample comment from the Minor-Third project predicted using IN, OUT and SO based models. Saved characters are underlined. Of the out-of-project data sources, models using a greater amount of text (SO) mostly outperformed models based on more code (OUT). This increase in performance, however, comes at a cost of greater run-time due to the larger word dictionary associated with the SO data. Note that in the scope of this work we did not investigate the contribution of each of the background projects used in OUT, and how their relevance to the target prediction project effects their performance. The trigram model shows the best performance across all training data sources (47% for IN, 32% for OUT and 34% for SO). Amongst the tested topic models, link-LDA models which distinguish code and text tokens perform consistently better than simple LDA models in which all tokens are considered as text. We did not however find a correlation between the number of latent topics learned by a topic model and its performance. In fact, for each of the data sources, a different num- ber of topics gave the optimal character saving results. Note that in this work, all topic models are based on unigram tokens, therefore their results are most comparable with that of the unigram in Dataset n-gram link-LDA IN 2778.35 574.34 OUT 1865.67 670.34 SO 1898.43 638.55 Table 3: Average words per project for which each tested model completes the word better than the other. This indicates that each of the models is better at predicting a different set of comment words. Table 1, which does not benefit from the backoff strategy used by the bigram and trigram models. By this comparison, the link-LDA topic model proves more successful in the comment prediction task than the simpler models which do not distin- guish code and text tokens. Using n-grams without backoff leads to results significantly worse than any of the presented models (not shown). Table 2 shows a sample comment segment for which words were predicted using trigram models from all training sources and an in-project linkLDA. The comment is taken from the TrainExtractor class in the Minor-Third project, a machine learning library for annotating and categorizing text. Both IN models show a clear advantage in completing the project-specific word Train, compared to models based on out-of-project data (OUT and SO). Interestingly, in this example the trigram is better at completing the term namedentity given the prefix named. However, the topic model is better at completing the word extractor which refers to the target class. This example indicates that each model type may be more successful in predicting different comment words, and that combining multiple models may be advantageous. 38 This can also be seen by the analysis in Table 3 where we compare the average number of words completed better by either the best n-gram or topic model given each training dataset. Again, while n-grams generally complete more words better, a considerable portion of the words is better completed using a topic model, further motivating a hybrid solution. 5 Conclusions We analyze the use of language models for predicting class comments for source file documents containing a mixture of code and text tokens. Our experiments demonstrate the effectiveness of using language models for comment completion, showing a saving of up to 47% of the comment characters. When available, using in-project training data proves significantly more successful than using out-of-project data. However, we find that when using out-of-project data, a dataset based on more words than code performs consistently better. The results also show that different models are better at predicting different comment words, which motivates a hybrid solution combining the advantages of multiple models. Acknowledgments This research was supported by the NSF under grant CCF-1247088. References Ramnath Balasubramanyan and William W Cohen. 2011. Block-lda: Jointly modeling entity-annotated text and entity-entity links. In Proceedings ofthe 7th SIAM International Conference on Data Mining. Dave Binkley, Matthew Hearn, and Dawn Lawrie. 2011. Improving identifier informativeness using part of speech information. In Proc. of the Working Conference on Mining Software Repositories. ACM. David M Blei and Michael I Jordan. 2003. Modeling annotated data. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval. ACM. David M Blei, Andrew Y Ng, and Michael IJordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research. Elena Erosheva, Stephen Fienberg, and John Lafferty. 2004. Mixed-membership models of scientific publications. Proceedings of the National Academy of Sciences of the United States of America. Yansong Feng and Mirella Lapata. 2010. How many words is a picture worth? automatic caption generation for news images. In Proc. of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. Yansong Feng and Mirella Lapata. 2013. Automatic caption generation for news images. IEEE transactions on pattern analysis and machine intelligence. Mark Gabel and Zhendong Su. 2008. Javert: fully automatic mining of general temporal properties from dynamic traces. In Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering, pages 339–349. ACM. Thomas L Griffiths and Mark Steyvers. 2004. Finding scientific topics. Proc. of the National Academy of Sciences of the United States of America. Sangmok Han, David R Wallace, and Robert C Miller. 2009. Code completion from abbreviated input. In Automated Software Engineering, 2009. ASE’09. 24th IEEE/ACM International Conference on, pages 332–343. IEEE. Abram Hindle, Earl T Barr, Zhendong Su, Mark Gabel, and Premkumar Devanbu. 2012. On the naturalness of software. In Software Engineering (ICSE), 2012 34th International Conference on. IEEE. Ferosh Jacob and Robert Tairas. 2010. Code template inference using language models. In Proceedings of the 48th Annual Southeast Regional Conference. ACM. Reinhard Kneser and Hermann Ney. 1995. Improved backing-off for m-gram language modeling. In Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., volume 1, pages 181–184. IEEE. Naveen Kumar and Benjamin Carterette. 2013. Time based feedback and query expansion for twitter search. In Advances in Information Retrieval, pages 734–737. Springer. Dawn Lawrie, Christopher Morrell, Henry Feild, and David Binkley. 2006. Whats in a name? a study of identifiers. In Program Comprehension, 2006. ICPC 2006. 14th IEEE International Conference on, pages 3–12. IEEE. Andrew Kachites McCallum. 2002. Mallet: A machine learning for language toolkit. Adam Pauls and Dan Klein. 2011. Faster and smaller language models. In Proceedings of the 49th annual meeting of the Association for Computational Linguistics: Human Language Technologies, volume 1, pages 258–267. n-gram Sarah Rastkar, Gail C Murphy, and Alexander WJ Bradley. 2011. Generating natural language summaries for crosscutting source code concerns. In Software Maintenance (ICSM), 2011 27th IEEE International Conference on, pages 103–1 12. IEEE. 39 Ronald Rosenfeld. 2000. Two decades of statistical language modeling: Where do we go from here? Proceedings of the IEEE, 88(8): 1270–1278. David Shepherd, Zachary P Fry, Emily Hill, Lori Pollock, and K Vijay-Shanker. 2007. Using natural language program analysis to locate and understand action-oriented concerns. In Proceedings of the 6th international conference on Aspect-oriented software development, pages 212–224. ACM. Giriprasad Sridhara, Emily Hill, Divya Muppaneni, Lori Pollock, and K Vijay-Shanker. 2010. Towards automatically generating summary comments for java methods. In Proceedings of the IEEE/ACM international conference on Automated software engineering, pages 43–52. ACM. Yuen-Hsien Tseng and Da-Wei Juang. 2003. Document-self expansion for text categorization. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 399–400. ACM. Xiaojun Wan, Jianwu Yang, and Jianguo Xiao. 2007. Single document summarization with document expansion. In Proc. of the National Conference on Artificial Intelligence. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999. Roung-Shiunn Wu and Po-Chun Li. 2011. Video annotation using hierarchical dirichlet process mixture model. Expert Systems with Applications, 38(4):3040–3048. 40
3 0.32435548 163 acl-2013-From Natural Language Specifications to Program Input Parsers
Author: Tao Lei ; Fan Long ; Regina Barzilay ; Martin Rinard
Abstract: We present a method for automatically generating input parsers from English specifications of input file formats. We use a Bayesian generative model to capture relevant natural language phenomena and translate the English specification into a specification tree, which is then translated into a C++ input parser. We model the problem as a joint dependency parsing and semantic role labeling task. Our method is based on two sources of information: (1) the correlation between the text and the specification tree and (2) noisy supervision as determined by the success of the generated C++ parser in reading input examples. Our results show that our approach achieves 80.0% F-Score accu- , racy compared to an F-Score of 66.7% produced by a state-of-the-art semantic parser on a dataset of input format specifications from the ACM International Collegiate Programming Contest (which were written in English for humans with no intention of providing support for automated processing).1
4 0.30102712 228 acl-2013-Leveraging Domain-Independent Information in Semantic Parsing
Author: Dan Goldwasser ; Dan Roth
Abstract: Semantic parsing is a domain-dependent process by nature, as its output is defined over a set of domain symbols. Motivated by the observation that interpretation can be decomposed into domain-dependent and independent components, we suggest a novel interpretation model, which augments a domain dependent model with abstract information that can be shared by multiple domains. Our experiments show that this type of information is useful and can reduce the annotation effort significantly when moving between domains.
5 0.29157993 212 acl-2013-Language-Independent Discriminative Parsing of Temporal Expressions
Author: Gabor Angeli ; Jakob Uszkoreit
Abstract: Temporal resolution systems are traditionally tuned to a particular language, requiring significant human effort to translate them to new languages. We present a language independent semantic parser for learning the interpretation of temporal phrases given only a corpus of utterances and the times they reference. We make use of a latent parse that encodes a language-flexible representation of time, and extract rich features over both the parse and associated temporal semantics. The parameters of the model are learned using a weakly supervised bootstrapping approach, without the need for manually tuned parameters or any other language expertise. We achieve state-of-the-art accuracy on all languages in the TempEval2 temporal normalization task, reporting a 4% improvement in both English and Spanish accuracy, and to our knowledge the first results for four other languages.
6 0.28054655 220 acl-2013-Learning Latent Personas of Film Characters
7 0.27461714 143 acl-2013-Exact Maximum Inference for the Fertility Hidden Markov Model
8 0.26347399 327 acl-2013-Sorani Kurdish versus Kurmanji Kurdish: An Empirical Comparison
9 0.26244202 237 acl-2013-Margin-based Decomposed Amortized Inference
10 0.26222822 209 acl-2013-Joint Modeling of News Readerâ•Žs and Comment Writerâ•Žs Emotions
11 0.25981957 317 acl-2013-Sentence Level Dialect Identification in Arabic
12 0.25852513 203 acl-2013-Is word-to-phone mapping better than phone-phone mapping for handling English words?
13 0.25815794 157 acl-2013-Fast and Robust Compressive Summarization with Dual Decomposition and Multi-Task Learning
14 0.25702885 334 acl-2013-Supervised Model Learning with Feature Grouping based on a Discrete Constraint
15 0.25133967 243 acl-2013-Mining Informal Language from Chinese Microtext: Joint Word Recognition and Segmentation
16 0.25069147 101 acl-2013-Cut the noise: Mutually reinforcing reordering and alignments for improved machine translation
17 0.24489127 370 acl-2013-Unsupervised Transcription of Historical Documents
18 0.24377784 166 acl-2013-Generalized Reordering Rules for Improved SMT
19 0.23546126 191 acl-2013-Improved Bayesian Logistic Supervised Topic Models with Data Augmentation
20 0.2332761 339 acl-2013-Temporal Signals Help Label Temporal Relations
topicId topicWeight
[(15, 0.045), (24, 0.02), (35, 0.042), (42, 0.035), (45, 0.615), (95, 0.02)]
simIndex simValue paperId paperTitle
same-paper 1 0.94927549 171 acl-2013-Grammatical Error Correction Using Integer Linear Programming
Author: Yuanbin Wu ; Hwee Tou Ng
Abstract: unkown-abstract
2 0.44377044 371 acl-2013-Unsupervised joke generation from big data
Author: Sasa Petrovic ; David Matthews
Abstract: Humor generation is a very hard problem. It is difficult to say exactly what makes a joke funny, and solving this problem algorithmically is assumed to require deep semantic understanding, as well as cultural and other contextual cues. We depart from previous work that tries to model this knowledge using ad-hoc manually created databases and labeled training examples. Instead we present a model that uses large amounts of unannotated data to generate I like my X like I like my Y, Z jokes, where X, Y, and Z are variables to be filled in. This is, to the best of our knowledge, the first fully unsupervised humor generation system. Our model significantly outperforms a competitive baseline and generates funny jokes 16% of the time, compared to 33% for human-generated jokes.
3 0.3193379 102 acl-2013-DErivBase: Inducing and Evaluating a Derivational Morphology Resource for German
Author: Britta Zeller ; Jan Snajder ; Sebastian Pado
Abstract: Derivational models are still an underresearched area in computational morphology. Even for German, a rather resourcerich language, there is a lack of largecoverage derivational knowledge. This paper describes a rule-based framework for inducing derivational families (i.e., clusters of lemmas in derivational relationships) and its application to create a highcoverage German resource, DERIVBASE, mapping over 280k lemmas into more than 17k non-singleton clusters. We focus on the rule component and a qualitative and quantitative evaluation. Our approach achieves up to 93% precision and 71% recall. We attribute the high precision to the fact that our rules are based on information from grammar books.
4 0.29282176 103 acl-2013-DISSECT - DIStributional SEmantics Composition Toolkit
Author: Georgiana Dinu ; Nghia The Pham ; Marco Baroni
Abstract: We introduce DISSECT, a toolkit to build and explore computational models of word, phrase and sentence meaning based on the principles of distributional semantics. The toolkit focuses in particular on compositional meaning, and implements a number of composition methods that have been proposed in the literature. Furthermore, DISSECT can be useful to researchers and practitioners who need models of word meaning (without composition) as well, as it supports various methods to construct distributional semantic spaces, assessing similarity and even evaluating against benchmarks, that are independent of the composition infrastructure.
5 0.12220185 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.
6 0.10192289 232 acl-2013-Linguistic Models for Analyzing and Detecting Biased Language
7 0.098024257 199 acl-2013-Integrating Multiple Dependency Corpora for Inducing Wide-coverage Japanese CCG Resources
8 0.093240492 47 acl-2013-An Information Theoretic Approach to Bilingual Word Clustering
9 0.088454187 262 acl-2013-Offspring from Reproduction Problems: What Replication Failure Teaches Us
10 0.087288328 304 acl-2013-SEMILAR: The Semantic Similarity Toolkit
11 0.086995639 233 acl-2013-Linking Tweets to News: A Framework to Enrich Short Text Data in Social Media
12 0.08580403 10 acl-2013-A Markov Model of Machine Translation using Non-parametric Bayesian Inference
13 0.084618412 122 acl-2013-Discriminative Approach to Fill-in-the-Blank Quiz Generation for Language Learners
14 0.082759574 311 acl-2013-Semantic Neighborhoods as Hypergraphs
15 0.081998192 160 acl-2013-Fine-grained Semantic Typing of Emerging Entities
16 0.081981197 166 acl-2013-Generalized Reordering Rules for Improved SMT
17 0.081590615 172 acl-2013-Graph-based Local Coherence Modeling
18 0.080616049 21 acl-2013-A Statistical NLG Framework for Aggregated Planning and Realization
19 0.080552116 302 acl-2013-Robust Automated Natural Language Processing with Multiword Expressions and Collocations
20 0.080517486 77 acl-2013-Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT?