naacl 2013 论文列表
Proceedings of the Second Joint Conference on Lexical and Computational Semantics, *SEM 2013, June 13-14, 2013, Atlanta, Georgia, USA.
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Semantic Parsing Freebase: Towards Open-domain Semantic Parsing.
Bootstrapping Semantic Role Labelers from Parallel Data.
Automatically Identifying Implicit Arguments to Improve Argument Linking and Coherence Modeling.
Choosing the Right Words: Characterizing and Reducing Error of the Word Count Approach.
Using the text to evaluate short answers for reading comprehension exercises.
Metaphor Identification as Interpretation.
Predicting the Compositionality of Multiword Expressions Using Translations in Multiple Languages.
Exploring Vector Space Models to Predict the Compositionality of German Noun-Noun Compounds.
More Words and Bigger Pictures.
Unsupervised Word Usage Similarity in Social Media Texts.
A Dataset of Syntactic-Ngrams over Time from a Very Large Corpus of English Books.
CNGL-CORE: Referential Translation Machines for Measuring Semantic Similarity.
INAOE_UPV-CORE: Extracting Word Associations from Document Corpora to estimate Semantic Textual Similarity.
CPN-CORE: A Text Semantic Similarity System Infused with Opinion Knowledge.
CFILT-CORE: Semantic Textual Similarity using Universal Networking Language.
UniMelb_NLP-CORE: Integrating predictions from multiple domains and feature sets for estimating semantic textual similarity.
CLaC-CORE: Exhaustive Feature Combination for Measuring Textual Similarity.
SOFTCARDINALITY-CORE: Improving Text Overlap with Distributional Measures for Semantic Textual Similarity.
IBM_EG-CORE: Comparing multiple Lexical and NE matching features in measuring Semantic Textual similarity.
KLUE-CORE: A regression model of semantic textual similarity.
DLS$@$CU-CORE: A Simple Machine Learning Model of Semantic Textual Similarity.
UNIBA-CORE: Combining Strategies for Semantic Textual Similarity.
LIPN-CORE: Semantic Text Similarity using n-grams, WordNet, Syntactic Analysis, ESA and Information Retrieval based Features.
SRIUBC-Core: Multiword Soft Similarity Models for Textual Similarity.
MayoClinicNLP-CORE: Semantic representations for textual similarity.
UPC-CORE: What Can Machine Translation Evaluation Metrics and Wikipedia Do for Estimating Semantic Textual Similarity?
KnCe2013-CORE: Semantic Text Similarity by use of Knowledge Bases.
UBC_UOS-TYPED: Regression for typed-similarity.
ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements.
BUT-TYPED: Using domain knowledge for computing typed similarity.
UMCC_DLSI: Textual Similarity based on Lexical-Semantic features.
DeepPurple: Lexical, String and Affective Feature Fusion for Sentence-Level Semantic Similarity Estimation.
HENRY-CORE: Domain Adaptation and Stacking for Text Similarity.
PolyUCOMP-CORE_TYPED: Computing Semantic Textual Similarity using Overlapped Senses.
UCAM-CORE: Incorporating structured distributional similarity into STS.
Distinguishing Common and Proper Nouns.
SXUCFN-Core: STS Models Integrating FrameNet Parsing Information.
NTNU-CORE: Combining strong features for semantic similarity.
UNITOR-CORE_TYPED: Combining Text Similarity and Semantic Filters through SV Regression.
iKernels-Core: Tree Kernel Learning for Textual Similarity.
UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems.
*SEM 2013 shared task: Semantic Textual Similarity.
Coarse to Fine Grained Sense Disambiguation in Wikipedia.
Montague Meets Markov: Deep Semantics with Probabilistic Logical Form.
Towards a Formal Distributional Semantics: Simulating Logical Calculi with Tensors.
