Papers Document Classification
“Document Classification” 태그가 달린 논문 670편 · 필터 해제
Multiple topic identification in telephone conversations
This paper deals with the automatic analysis of conversations between a customer and an agent in a call centre of a customer care service. The purpose of the analysis is to hypothesize themes about problems and complaint…
Document ClassificationThe Recognition Of Persian Phonemes Using PPNet
In this paper, a novel approach is proposed for the recognition of Persian phonemes in the Persian Consonant-Vowel Combination (PCVC) speech dataset. Nowadays, deep neural networks play a crucial role in classification t…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)ClassificationDocument Classification+5Measuring Similarity: Computationally Reproducing the Scholar's Interests
Computerized document classification already orders the news articles that Apple's "News" app or Google's "personalized search" feature groups together to match a reader's interests. The invisible and therefore illegible…
ArticlesClassificationDocument ClassificationGeneral Classification+2Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals
Reviewing radiology reports in emergency departments is an essential but laborious task. Timely follow-up of patients with abnormal cases in their radiology reports may dramatically affect the patient's outcome, especial…
ClassificationDocument ClassificationFeature EngineeringGeneral Classification+1Cluster Labeling by Word Embeddings and WordNet's Hypernymy
Cluster labeling is the assignment of representative labels to clusters obtained from the organization of a document collection. Once assigned, the labels can play an important role in applications such as navigation, se…
ClusteringDescriptiveDocument ClassificationLearning Word Embeddings+1Document classification using a Bi-LSTM to unclog Brazil's supreme court
The Brazilian court system is currently the most clogged up judiciary system in the world. Thousands of lawsuit cases reach the supreme court every day. These cases need to be analyzed in order to be associated to releva…
Document ClassificationGeneral ClassificationHPI-DHC at TREC 2018 Precision Medicine Track
The TREC-PM challenge aims for advances in the field of information retrieval applied to precision medicine. Here we describe our experimental setup and the achieved results in its 2018 edition. We explored the use of un…
ArticlesDocument ClassificationInformation RetrievalNegation+3Semantic Term "Blurring" and Stochastic "Barcoding" for Improved Unsupervised Text Classification
The abundance of text data being produced in the modern age makes it increasingly important to intuitively group, categorize, or classify text data by theme for efficient retrieval and search. Yet, the high dimensionalit…
ClusteringDocument ClassificationGeneral ClassificationRetrieval+4Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News
Fake news are nowadays an issue of pressing concern, given their recent rise as a potential threat to high-quality journalism and well-informed public discourse. The Fake News Challenge (FNC-1) was organized in 2017 to e…
Document ClassificationGeneral ClassificationNatural Language InferenceRepresentation Learning+2When does deep multi-task learning work for loosely related document classification tasks?
This work aims to contribute to our understanding of \textit{when} multi-task learning through parameter sharing in deep neural networks leads to improvements over single-task learning. We focus on the setting of learnin…
Document ClassificationGeneral ClassificationMachine TranslationMulti-Task Learning+4Convolutions Are All You Need (For Classifying Character Sequences)
While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences. When text is modeled as characters instead of words, …
AllClassificationDocument ClassificationGeneral Classification+5Variational learning across domains with triplet information
The work investigates deep generative models, which allow us to use training data from one domain to build a model for another domain. We propose the Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint d…
Cross-Lingual Document ClassificationDocument ClassificationImage GenerationImage-to-Image Translation+3Improving Topic Models with Latent Feature Word Representations
Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this pape…
ClusteringDocument ClassificationGeneral ClassificationTopic ModelsEnd-to-End Text Classification via Image-based Embedding using Character-level Networks
For analysing and/or understanding languages having no word boundaries based on morphological analysis such as Japanese, Chinese, and Thai, it is desirable to perform appropriate word segmentation before word embeddings.…
Document ClassificationGeneral ClassificationMorphological Analysistext-classification+2Labeled Anchors and a Scalable, Transparent, and Interactive Classifier
We propose Labeled Anchors, an interactive and supervised topic model based on the anchor words algorithm (Arora et al., 2013). Labeled Anchors is similar to Supervised Anchors (Nguyen et al., 2014) in that it extends th…
Document ClassificationGeneral ClassificationText ClassificationTopic ModelsStreaming word similarity mining on the cheap
Accurately and efficiently estimating word similarities from text is fundamental in natural language processing. In this paper, we propose a fast and lightweight method for estimating similarities from streams by explici…
Document ClassificationWord AlignmentWord EmbeddingsWord SimilarityAuto-Encoding Dictionary Definitions into Consistent Word Embeddings
Monolingual dictionaries are widespread and semantically rich resources. This paper presents a simple model that learns to compute word embeddings by processing dictionary definitions and trying to reconstruct them. It e…
Document ClassificationMachine TranslationSemantic SimilaritySemantic Textual Similarity+1Speed Reading: Learning to Read ForBackward via Shuttle
We present LSTM-Shuttle, which applies human speed reading techniques to natural language processing tasks for accurate and efficient comprehension. In contrast to previous work, LSTM-Shuttle not only reads shuttling for…
Document ClassificationDocument SummarizationGeneral ClassificationMachine Translation+6UZH@SMM4H: System Descriptions
Our team at the University of Z{\"u}rich participated in the first 3 of the 4 sub-tasks at the Social Media Mining for Health Applications (SMM4H) shared task. We experimented with different approaches for text classific…
Document ClassificationGeneral ClassificationLemmatizationPart-Of-Speech Tagging+3Language Independent Sentiment Analysis with Sentiment-Specific Word Embeddings
Data annotation is a critical step to train a text model but it is tedious, expensive and time-consuming. We present a language independent method to train a sentiment polarity model with limited amount of manually-label…
Document ClassificationGeneral ClassificationSentiment AnalysisSentiment Classification+2