COLING 2016

Effective LSTMs for Target-Dependent Sentiment Classification

COLING 2016 songyouwei/ABSA-PyTorch

Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence.

ASPECT-BASED SENTIMENT ANALYSIS

pke: an open source python-based keyphrase extraction toolkit

COLING 2016 boudinfl/pke

We describe pke, an open source python-based keyphrase extraction toolkit.

TEXT CATEGORIZATION

Modeling topic dependencies in semantically coherent text spans with copulas

COLING 2016 balikasg/topicModelling

The exchangeability assumption in topic models like Latent Dirichlet Allocation (LDA) often results in inferring inconsistent topics for the words of text spans like noun-phrases, which are usually expected to be topically coherent.

TOPIC MODELS

Better call Saul: Flexible Programming for Learning and Inference in NLP

COLING 2016 IllinoisCogComp/saul

We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP).

PART-OF-SPEECH TAGGING PROBABILISTIC PROGRAMMING SEMANTIC ROLE LABELING

Hierarchical Memory Networks for Answer Selection on Unknown Words

COLING 2016 jacoxu/HMN4QA

Recently, end-to-end memory networks have shown promising results on Question Answering task, which encode the past facts into an explicit memory and perform reasoning ability by making multiple computational steps on the memory.

ANSWER SELECTION

PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors

COLING 2016 dmort27/panphon

This paper contributes to a growing body of evidence that{---}when coupled with appropriate machine-learning techniques{--}linguistically motivated, information-rich representations can outperform one-hot encodings of linguistic data.

Kyoto-NMT: a Neural Machine Translation implementation in Chainer

COLING 2016 fabiencro/knmt

We present Kyoto-NMT, an open-source implementation of the Neural Machine Translation paradigm.

LANGUAGE MODELLING MACHINE TRANSLATION

Sentence Similarity Learning by Lexical Decomposition and Composition

COLING 2016 Leputa/CIKM-AnalytiCup-2018

Most conventional sentence similarity methods only focus on similar parts of two input sentences, and simply ignore the dissimilar parts, which usually give us some clues and semantic meanings about the sentences.

PARAPHRASE IDENTIFICATION QUESTION ANSWERING

Data-Driven Morphological Analysis and Disambiguation for Morphologically Rich Languages and Universal Dependencies

COLING 2016 habeanf/yap

Parsing texts into universal dependencies (UD) in realistic scenarios requires infrastructure for the morphological analysis and disambiguation (MA{\&}D) of typologically different languages as a first tier.

MORPHOLOGICAL ANALYSIS