emnlp emnlp2013 knowledge-graph by maker-knowledge-mining
1 emnlp-2013-A Constrained Latent Variable Model for Coreference Resolution
Author: Kai-Wei Chang ; Rajhans Samdani ; Dan Roth
Abstract: Coreference resolution is a well known clustering task in Natural Language Processing. In this paper, we describe the Latent Left Linking model (L3M), a novel, principled, and linguistically motivated latent structured prediction approach to coreference resolution. We show that L3M admits efficient inference and can be augmented with knowledge-based constraints; we also present a fast stochastic gradient based learning. Experiments on ACE and Ontonotes data show that L3M and its constrained version, CL3M, are more accurate than several state-of-the-art approaches as well as some structured prediction models proposed in the literature.
2 emnlp-2013-A Convex Alternative to IBM Model 2
Author: Andrei Simion ; Michael Collins ; Cliff Stein
Abstract: The IBM translation models have been hugely influential in statistical machine translation; they are the basis of the alignment models used in modern translation systems. Excluding IBM Model 1, the IBM translation models, and practically all variants proposed in the literature, have relied on the optimization of likelihood functions or similar functions that are non-convex, and hence have multiple local optima. In this paper we introduce a convex relaxation of IBM Model 2, and describe an optimization algorithm for the relaxation based on a subgradient method combined with exponentiated-gradient updates. Our approach gives the same level of alignment accuracy as IBM Model 2.
3 emnlp-2013-A Corpus Level MIRA Tuning Strategy for Machine Translation
Author: Ming Tan ; Tian Xia ; Shaojun Wang ; Bowen Zhou
Abstract: MIRA based tuning methods have been widely used in statistical machine translation (SMT) system with a large number of features. Since the corpus-level BLEU is not decomposable, these MIRA approaches usually define a variety of heuristic-driven sentencelevel BLEUs in their model losses. Instead, we present a new MIRA method, which employs an exact corpus-level BLEU to compute the model loss. Our method is simpler in implementation. Experiments on Chinese-toEnglish translation show its effectiveness over two state-of-the-art MIRA implementations.
4 emnlp-2013-A Dataset for Research on Short-Text Conversations
Author: Hao Wang ; Zhengdong Lu ; Hang Li ; Enhong Chen
Abstract: Natural language conversation is widely regarded as a highly difficult problem, which is usually attacked with either rule-based or learning-based models. In this paper we propose a retrieval-based automatic response model for short-text conversation, to exploit the vast amount of short conversation instances available on social media. For this purpose we introduce a dataset of short-text conversation based on the real-world instances from Sina Weibo (a popular Chinese microblog service), which will be soon released to public. This dataset provides rich collection of instances for the research on finding natural and relevant short responses to a given short text, and useful for both training and testing of conversation models. This dataset consists of both naturally formed conversations, manually labeled data, and a large repository of candidate responses. Our preliminary experiments demonstrate that the simple retrieval-based conversation model performs reasonably well when combined with the rich instances in our dataset.
Author: Maria Liakata ; Simon Dobnik ; Shyamasree Saha ; Colin Batchelor ; Dietrich Rebholz-Schuhmann
Abstract: We present a method which exploits automatically generated scientific discourse annotations to create a content model for the summarisation of scientific articles. Full papers are first automatically annotated using the CoreSC scheme, which captures 11 contentbased concepts such as Hypothesis, Result, Conclusion etc at the sentence level. A content model which follows the sequence of CoreSC categories observed in abstracts is used to provide the skeleton of the summary, making a distinction between dependent and independent categories. Summary creation is also guided by the distribution of CoreSC categories found in the full articles, in order to adequately represent the article content. Fi- nally, we demonstrate the usefulness of the summaries by evaluating them in a complex question answering task. Results are very encouraging as summaries of papers from automatically obtained CoreSCs enable experts to answer 66% of complex content-related questions designed on the basis of paper abstracts. The questions were answered with a precision of 75%, where the upper bound for human summaries (abstracts) was 95%.
Author: Byron C. Wallace ; Thomas A Trikalinos ; M. Barton Laws ; Ira B. Wilson ; Eugene Charniak
Abstract: We develop a novel generative model of conversation that jointly captures both the topical content and the speech act type associated with each utterance. Our model expresses both token emission and state transition probabilities as log-linear functions of separate components corresponding to topics and speech acts (and their interactions). We apply this model to a dataset comprising annotated patient-physician visits and show that the proposed joint approach outperforms a baseline univariate model.
Author: Baichuan Li ; Jing Liu ; Chin-Yew Lin ; Irwin King ; Michael R. Lyu
Abstract: Social media like forums and microblogs have accumulated a huge amount of user generated content (UGC) containing human knowledge. Currently, most of UGC is listed as a whole or in pre-defined categories. This “list-based” approach is simple, but hinders users from browsing and learning knowledge of certain topics effectively. To address this problem, we propose a hierarchical entity-based approach for structuralizing UGC in social media. By using a large-scale entity repository, we design a three-step framework to organize UGC in a novel hierarchical structure called “cluster entity tree (CET)”. With Yahoo! Answers as a test case, we conduct experiments and the results show the effectiveness of our framework in constructing CET. We further evaluate the performance of CET on UGC organization in both user and system aspects. From a user aspect, our user study demonstrates that, with CET-based structure, users perform significantly better in knowledge learning than using traditional list-based approach. From a system aspect, CET substantially boosts the performance of two information retrieval models (i.e., vector space model and query likelihood language model).
Author: Micha Elsner ; Sharon Goldwater ; Naomi Feldman ; Frank Wood
Abstract: We present a cognitive model of early lexical acquisition which jointly performs word segmentation and learns an explicit model of phonetic variation. We define the model as a Bayesian noisy channel; we sample segmentations and word forms simultaneously from the posterior, using beam sampling to control the size of the search space. Compared to a pipelined approach in which segmentation is performed first, our model is qualitatively more similar to human learners. On data with vari- able pronunciations, the pipelined approach learns to treat syllables or morphemes as words. In contrast, our joint model, like infant learners, tends to learn multiword collocations. We also conduct analyses of the phonetic variations that the model learns to accept and its patterns of word recognition errors, and relate these to developmental evidence.
9 emnlp-2013-A Log-Linear Model for Unsupervised Text Normalization
Author: Yi Yang ; Jacob Eisenstein
Abstract: We present a unified unsupervised statistical model for text normalization. The relationship between standard and non-standard tokens is characterized by a log-linear model, permitting arbitrary features. The weights of these features are trained in a maximumlikelihood framework, employing a novel sequential Monte Carlo training algorithm to overcome the large label space, which would be impractical for traditional dynamic programming solutions. This model is implemented in a normalization system called UNLOL, which achieves the best known results on two normalization datasets, outperforming more complex systems. We use the output of UNLOL to automatically normalize a large corpus of social media text, revealing a set of coherent orthographic styles that underlie online language variation.
10 emnlp-2013-A Multi-Teraflop Constituency Parser using GPUs
Author: John Canny ; David Hall ; Dan Klein
Abstract: Constituency parsing with rich grammars remains a computational challenge. Graphics Processing Units (GPUs) have previously been used to accelerate CKY chart evaluation, but gains over CPU parsers were modest. In this paper, we describe a collection of new techniques that enable chart evaluation at close to the GPU’s practical maximum speed (a Teraflop), or around a half-trillion rule evaluations per second. Net parser performance on a 4-GPU system is over 1 thousand length30 sentences/second (1 trillion rules/sec), and 400 general sentences/second for the Berkeley Parser Grammar. The techniques we introduce include grammar compilation, recursive symbol blocking, and cache-sharing.
11 emnlp-2013-A Multimodal LDA Model integrating Textual, Cognitive and Visual Modalities
Author: Stephen Roller ; Sabine Schulte im Walde
Abstract: Recent investigations into grounded models of language have shown that holistic views of language and perception can provide higher performance than independent views. In this work, we improve a two-dimensional multimodal version of Latent Dirichlet Allocation (Andrews et al., 2009) in various ways. (1) We outperform text-only models in two different evaluations, and demonstrate that low-level visual features are directly compatible with the existing model. (2) We present a novel way to integrate visual features into the LDA model using unsupervised clusters of images. The clusters are directly interpretable and improve on our evaluation tasks. (3) We provide two novel ways to extend the bimodal mod- els to support three or more modalities. We find that the three-, four-, and five-dimensional models significantly outperform models using only one or two modalities, and that nontextual modalities each provide separate, disjoint knowledge that cannot be forced into a shared, latent structure.
12 emnlp-2013-A Semantically Enhanced Approach to Determine Textual Similarity
Author: Eduardo Blanco ; Dan Moldovan
Abstract: This paper presents a novel approach to determine textual similarity. A layered methodology to transform text into logic forms is proposed, and semantic features are derived from a logic prover. Experimental results show that incorporating the semantic structure of sentences is beneficial. When training data is unavailable, scores obtained from the logic prover in an unsupervised manner outperform supervised methods.
Author: Ivan Vulic ; Marie-Francine Moens
Abstract: We present a new language pair agnostic approach to inducing bilingual vector spaces from non-parallel data without any other resource in a bootstrapping fashion. The paper systematically introduces and describes all key elements of the bootstrapping procedure: (1) starting point or seed lexicon, (2) the confidence estimation and selection of new dimensions of the space, and (3) convergence. We test the quality of the induced bilingual vector spaces, and analyze the influence of the different components of the bootstrapping approach in the task of bilingual lexicon extraction (BLE) for two language pairs. Results reveal that, contrary to conclusions from prior work, the seeding of the bootstrapping process has a heavy impact on the quality of the learned lexicons. We also show that our approach outperforms the best performing fully corpus-based BLE methods on these test sets.
14 emnlp-2013-A Synchronous Context Free Grammar for Time Normalization
Author: Steven Bethard
Abstract: We present an approach to time normalization (e.g. the day before yesterday⇒20 13-04- 12) based on a synchronous contex⇒t free grammar. Synchronous rules map the source language to formally defined operators for manipulating times (FINDENCLOSED, STARTATENDOF, etc.). Time expressions are then parsed using an extended CYK+ algorithm, and converted to a normalized form by applying the operators recursively. For evaluation, a small set of synchronous rules for English time expressions were developed. Our model outperforms HeidelTime, the best time normalization system in TempEval 2013, on four different time normalization corpora.
15 emnlp-2013-A Systematic Exploration of Diversity in Machine Translation
Author: Kevin Gimpel ; Dhruv Batra ; Chris Dyer ; Gregory Shakhnarovich
Abstract: This paper addresses the problem of producing a diverse set of plausible translations. We present a simple procedure that can be used with any statistical machine translation (MT) system. We explore three ways of using diverse translations: (1) system combination, (2) discriminative reranking with rich features, and (3) a novel post-editing scenario in which multiple translations are presented to users. We find that diversity can improve performance on these tasks, especially for sentences that are difficult for MT.
16 emnlp-2013-A Unified Model for Topics, Events and Users on Twitter
Author: Qiming Diao ; Jing Jiang
Abstract: With the rapid growth of social media, Twitter has become one of the most widely adopted platforms for people to post short and instant message. On the one hand, people tweets about their daily lives, and on the other hand, when major events happen, people also follow and tweet about them. Moreover, people’s posting behaviors on events are often closely tied to their personal interests. In this paper, we try to model topics, events and users on Twitter in a unified way. We propose a model which combines an LDA-like topic model and the Recurrent Chinese Restaurant Process to capture topics and events. We further propose a duration-based regularization component to find bursty events. We also propose to use event-topic affinity vectors to model the asso- . ciation between events and topics. Our experiments shows that our model can accurately identify meaningful events and the event-topic affinity vectors are effective for event recommendation and grouping events by topics.
17 emnlp-2013-A Walk-Based Semantically Enriched Tree Kernel Over Distributed Word Representations
Author: Shashank Srivastava ; Dirk Hovy ; Eduard Hovy
Abstract: In this paper, we propose a walk-based graph kernel that generalizes the notion of treekernels to continuous spaces. Our proposed approach subsumes a general framework for word-similarity, and in particular, provides a flexible way to incorporate distributed representations. Using vector representations, such an approach captures both distributional semantic similarities among words as well as the structural relations between them (encoded as the structure of the parse tree). We show an efficient formulation to compute this kernel using simple matrix operations. We present our results on three diverse NLP tasks, showing state-of-the-art results.
18 emnlp-2013-A temporal model of text periodicities using Gaussian Processes
Author: Daniel Preotiuc-Pietro ; Trevor Cohn
Abstract: Temporal variations of text are usually ignored in NLP applications. However, text use changes with time, which can affect many applications. In this paper we model periodic distributions of words over time. Focusing on hashtag frequency in Twitter, we first automatically identify the periodic patterns. We use this for regression in order to forecast the volume of a hashtag based on past data. We use Gaussian Processes, a state-ofthe-art bayesian non-parametric model, with a novel periodic kernel. We demonstrate this in a text classification setting, assigning the tweet hashtag based on the rest of its text. This method shows significant improvements over competitive baselines.
19 emnlp-2013-Adaptor Grammars for Learning Non-Concatenative Morphology
Author: Jan A. Botha ; Phil Blunsom
Abstract: This paper contributes an approach for expressing non-concatenative morphological phenomena, such as stem derivation in Semitic languages, in terms of a mildly context-sensitive grammar formalism. This offers a convenient level of modelling abstraction while remaining computationally tractable. The nonparametric Bayesian framework of adaptor grammars is extended to this richer grammar formalism to propose a probabilistic model that can learn word segmentation and morpheme lexicons, including ones with discontiguous strings as elements, from unannotated data. Our experiments on Hebrew and three variants of Arabic data find that the additional expressiveness to capture roots and templates as atomic units improves the quality of concatenative segmentation and stem identification. We obtain 74% accuracy in identifying triliteral Hebrew roots, while performing morphological segmentation with an F1-score of 78. 1.
20 emnlp-2013-An Efficient Language Model Using Double-Array Structures
Author: Makoto Yasuhara ; Toru Tanaka ; Jun-ya Norimatsu ; Mikio Yamamoto
Abstract: Ngram language models tend to increase in size with inflating the corpus size, and consume considerable resources. In this paper, we propose an efficient method for implementing ngram models based on doublearray structures. First, we propose a method for representing backwards suffix trees using double-array structures and demonstrate its efficiency. Next, we propose two optimization methods for improving the efficiency of data representation in the double-array structures. Embedding probabilities into unused spaces in double-array structures reduces the model size. Moreover, tuning the word IDs in the language model makes the model smaller and faster. We also show that our method can be used for building large language models using the division method. Lastly, we show that our method outperforms methods based on recent related works from the viewpoints of model size and query speed when both optimization methods are used.
21 emnlp-2013-An Empirical Study Of Semi-Supervised Chinese Word Segmentation Using Co-Training
22 emnlp-2013-Anchor Graph: Global Reordering Contexts for Statistical Machine Translation
23 emnlp-2013-Animacy Detection with Voting Models
24 emnlp-2013-Application of Localized Similarity for Web Documents
25 emnlp-2013-Appropriately Incorporating Statistical Significance in PMI
26 emnlp-2013-Assembling the Kazakh Language Corpus
27 emnlp-2013-Authorship Attribution of Micro-Messages
28 emnlp-2013-Automated Essay Scoring by Maximizing Human-Machine Agreement
29 emnlp-2013-Automatic Domain Partitioning for Multi-Domain Learning
30 emnlp-2013-Automatic Extraction of Morphological Lexicons from Morphologically Annotated Corpora
31 emnlp-2013-Automatic Feature Engineering for Answer Selection and Extraction
32 emnlp-2013-Automatic Idiom Identification in Wiktionary
34 emnlp-2013-Automatically Classifying Edit Categories in Wikipedia Revisions
35 emnlp-2013-Automatically Detecting and Attributing Indirect Quotations
37 emnlp-2013-Automatically Identifying Pseudepigraphic Texts
38 emnlp-2013-Bilingual Word Embeddings for Phrase-Based Machine Translation
41 emnlp-2013-Building Event Threads out of Multiple News Articles
42 emnlp-2013-Building Specialized Bilingual Lexicons Using Large Scale Background Knowledge
44 emnlp-2013-Centering Similarity Measures to Reduce Hubs
45 emnlp-2013-Chinese Zero Pronoun Resolution: Some Recent Advances
46 emnlp-2013-Classifying Message Board Posts with an Extracted Lexicon of Patient Attributes
47 emnlp-2013-Collective Opinion Target Extraction in Chinese Microblogs
48 emnlp-2013-Collective Personal Profile Summarization with Social Networks
49 emnlp-2013-Combining Generative and Discriminative Model Scores for Distant Supervision
51 emnlp-2013-Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction
53 emnlp-2013-Cross-Lingual Discriminative Learning of Sequence Models with Posterior Regularization
54 emnlp-2013-Decipherment with a Million Random Restarts
55 emnlp-2013-Decoding with Large-Scale Neural Language Models Improves Translation
56 emnlp-2013-Deep Learning for Chinese Word Segmentation and POS Tagging
57 emnlp-2013-Dependency-Based Decipherment for Resource-Limited Machine Translation
58 emnlp-2013-Dependency Language Models for Sentence Completion
59 emnlp-2013-Deriving Adjectival Scales from Continuous Space Word Representations
61 emnlp-2013-Detecting Promotional Content in Wikipedia
64 emnlp-2013-Discriminative Improvements to Distributional Sentence Similarity
65 emnlp-2013-Document Summarization via Guided Sentence Compression
66 emnlp-2013-Dynamic Feature Selection for Dependency Parsing
67 emnlp-2013-Easy Victories and Uphill Battles in Coreference Resolution
68 emnlp-2013-Effectiveness and Efficiency of Open Relation Extraction
69 emnlp-2013-Efficient Collective Entity Linking with Stacking
70 emnlp-2013-Efficient Higher-Order CRFs for Morphological Tagging
71 emnlp-2013-Efficient Left-to-Right Hierarchical Phrase-Based Translation with Improved Reordering
72 emnlp-2013-Elephant: Sequence Labeling for Word and Sentence Segmentation
73 emnlp-2013-Error-Driven Analysis of Challenges in Coreference Resolution
74 emnlp-2013-Event-Based Time Label Propagation for Automatic Dating of News Articles
75 emnlp-2013-Event Schema Induction with a Probabilistic Entity-Driven Model
76 emnlp-2013-Exploiting Discourse Analysis for Article-Wide Temporal Classification
77 emnlp-2013-Exploiting Domain Knowledge in Aspect Extraction
78 emnlp-2013-Exploiting Language Models for Visual Recognition
79 emnlp-2013-Exploiting Multiple Sources for Open-Domain Hypernym Discovery
80 emnlp-2013-Exploiting Zero Pronouns to Improve Chinese Coreference Resolution
84 emnlp-2013-Factored Soft Source Syntactic Constraints for Hierarchical Machine Translation
85 emnlp-2013-Fast Joint Compression and Summarization via Graph Cuts
86 emnlp-2013-Feature Noising for Log-Linear Structured Prediction
89 emnlp-2013-Gender Inference of Twitter Users in Non-English Contexts
90 emnlp-2013-Generating Coherent Event Schemas at Scale
92 emnlp-2013-Growing Multi-Domain Glossaries from a Few Seeds using Probabilistic Topic Models
93 emnlp-2013-Harvesting Parallel News Streams to Generate Paraphrases of Event Relations
94 emnlp-2013-Identifying Manipulated Offerings on Review Portals
95 emnlp-2013-Identifying Multiple Userids of the Same Author
96 emnlp-2013-Identifying Phrasal Verbs Using Many Bilingual Corpora
97 emnlp-2013-Identifying Web Search Query Reformulation using Concept based Matching
98 emnlp-2013-Image Description using Visual Dependency Representations
99 emnlp-2013-Implicit Feature Detection via a Constrained Topic Model and SVM
100 emnlp-2013-Improvements to the Bayesian Topic N-Gram Models
101 emnlp-2013-Improving Alignment of System Combination by Using Multi-objective Optimization
102 emnlp-2013-Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues
103 emnlp-2013-Improving Pivot-Based Statistical Machine Translation Using Random Walk
104 emnlp-2013-Improving Statistical Machine Translation with Word Class Models
105 emnlp-2013-Improving Web Search Ranking by Incorporating Structured Annotation of Queries
106 emnlp-2013-Inducing Document Plans for Concept-to-Text Generation
107 emnlp-2013-Interactive Machine Translation using Hierarchical Translation Models
109 emnlp-2013-Is Twitter A Better Corpus for Measuring Sentiment Similarity?
110 emnlp-2013-Joint Bootstrapping of Corpus Annotations and Entity Types
112 emnlp-2013-Joint Coreference Resolution and Named-Entity Linking with Multi-Pass Sieves
113 emnlp-2013-Joint Language and Translation Modeling with Recurrent Neural Networks
114 emnlp-2013-Joint Learning and Inference for Grammatical Error Correction
115 emnlp-2013-Joint Learning of Phonetic Units and Word Pronunciations for ASR
116 emnlp-2013-Joint Parsing and Disfluency Detection in Linear Time
117 emnlp-2013-Latent Anaphora Resolution for Cross-Lingual Pronoun Prediction
118 emnlp-2013-Learning Biological Processes with Global Constraints
119 emnlp-2013-Learning Distributions over Logical Forms for Referring Expression Generation
121 emnlp-2013-Learning Topics and Positions from Debatepedia
123 emnlp-2013-Learning to Rank Lexical Substitutions
124 emnlp-2013-Leveraging Lexical Cohesion and Disruption for Topic Segmentation
125 emnlp-2013-Lexical Chain Based Cohesion Models for Document-Level Statistical Machine Translation
126 emnlp-2013-MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text
127 emnlp-2013-Max-Margin Synchronous Grammar Induction for Machine Translation
128 emnlp-2013-Max-Violation Perceptron and Forced Decoding for Scalable MT Training
129 emnlp-2013-Measuring Ideological Proportions in Political Speeches
130 emnlp-2013-Microblog Entity Linking by Leveraging Extra Posts
132 emnlp-2013-Mining Scientific Terms and their Definitions: A Study of the ACL Anthology
133 emnlp-2013-Modeling Scientific Impact with Topical Influence Regression
135 emnlp-2013-Monolingual Marginal Matching for Translation Model Adaptation
136 emnlp-2013-Multi-Domain Adaptation for SMT Using Multi-Task Learning
137 emnlp-2013-Multi-Relational Latent Semantic Analysis
138 emnlp-2013-Naive Bayes Word Sense Induction
141 emnlp-2013-Online Learning for Inexact Hypergraph Search
142 emnlp-2013-Open-Domain Fine-Grained Class Extraction from Web Search Queries
143 emnlp-2013-Open Domain Targeted Sentiment
144 emnlp-2013-Opinion Mining in Newspaper Articles by Entropy-Based Word Connections
145 emnlp-2013-Optimal Beam Search for Machine Translation
146 emnlp-2013-Optimal Incremental Parsing via Best-First Dynamic Programming
147 emnlp-2013-Optimized Event Storyline Generation based on Mixture-Event-Aspect Model
148 emnlp-2013-Orthonormal Explicit Topic Analysis for Cross-Lingual Document Matching
149 emnlp-2013-Overcoming the Lack of Parallel Data in Sentence Compression
151 emnlp-2013-Paraphrasing 4 Microblog Normalization
152 emnlp-2013-Predicting the Presence of Discourse Connectives
153 emnlp-2013-Predicting the Resolution of Referring Expressions from User Behavior
154 emnlp-2013-Prior Disambiguation of Word Tensors for Constructing Sentence Vectors
155 emnlp-2013-Question Difficulty Estimation in Community Question Answering Services
156 emnlp-2013-Recurrent Continuous Translation Models
157 emnlp-2013-Recursive Autoencoders for ITG-Based Translation
158 emnlp-2013-Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
159 emnlp-2013-Regularized Minimum Error Rate Training
160 emnlp-2013-Relational Inference for Wikification
162 emnlp-2013-Russian Stress Prediction using Maximum Entropy Ranking
163 emnlp-2013-Sarcasm as Contrast between a Positive Sentiment and Negative Situation
164 emnlp-2013-Scaling Semantic Parsers with On-the-Fly Ontology Matching
166 emnlp-2013-Semantic Parsing on Freebase from Question-Answer Pairs
167 emnlp-2013-Semi-Markov Phrase-Based Monolingual Alignment
168 emnlp-2013-Semi-Supervised Feature Transformation for Dependency Parsing
169 emnlp-2013-Semi-Supervised Representation Learning for Cross-Lingual Text Classification
170 emnlp-2013-Sentiment Analysis: How to Derive Prior Polarities from SentiWordNet
171 emnlp-2013-Shift-Reduce Word Reordering for Machine Translation
172 emnlp-2013-Simple Customization of Recursive Neural Networks for Semantic Relation Classification
173 emnlp-2013-Simulating Early-Termination Search for Verbose Spoken Queries
174 emnlp-2013-Single-Document Summarization as a Tree Knapsack Problem
175 emnlp-2013-Source-Side Classifier Preordering for Machine Translation
176 emnlp-2013-Structured Penalties for Log-Linear Language Models
178 emnlp-2013-Success with Style: Using Writing Style to Predict the Success of Novels
181 emnlp-2013-The Effects of Syntactic Features in Automatic Prediction of Morphology
182 emnlp-2013-The Topology of Semantic Knowledge
183 emnlp-2013-The VerbCorner Project: Toward an Empirically-Based Semantic Decomposition of Verbs
185 emnlp-2013-Towards Situated Dialogue: Revisiting Referring Expression Generation
186 emnlp-2013-Translating into Morphologically Rich Languages with Synthetic Phrases
187 emnlp-2013-Translation with Source Constituency and Dependency Trees
190 emnlp-2013-Ubertagging: Joint Segmentation and Supertagging for English
191 emnlp-2013-Understanding and Quantifying Creativity in Lexical Composition
192 emnlp-2013-Unsupervised Induction of Contingent Event Pairs from Film Scenes
193 emnlp-2013-Unsupervised Induction of Cross-Lingual Semantic Relations
194 emnlp-2013-Unsupervised Relation Extraction with General Domain Knowledge
195 emnlp-2013-Unsupervised Spectral Learning of WCFG as Low-rank Matrix Completion
196 emnlp-2013-Using Crowdsourcing to get Representations based on Regular Expressions
198 emnlp-2013-Using Soft Constraints in Joint Inference for Clinical Concept Recognition
200 emnlp-2013-Well-Argued Recommendation: Adaptive Models Based on Words in Recommender Systems
201 emnlp-2013-What is Hidden among Translation Rules
203 emnlp-2013-With Blinkers on: Robust Prediction of Eye Movements across Readers
204 emnlp-2013-Word Level Language Identification in Online Multilingual Communication