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How can the development of ideas in a sci- entific field be studied over time? We ap- ply unsupervised topic modeling to the ACL Anthology to analyze historical trends in …...

Topic modeling for information retrieval (IR) has attracted significant attention and demonstrated good performance in a wide variety of tasks over the years. In this paper, we first present a …...

We propose a novel probabilistic technique for modeling and extracting salient struc- ture from large document collections. As in clustering and topic modeling, our goal is to provide an organizing …...

We introduce the social study of bullying to the NLP community. Bullying, in both physi- cal and cyber worlds (the latter known as cy- berbullying), has been recognized as a …...

Online discussion forums are a popular platform for people to voice their opinions on any subject matter and to discuss or debate any issue of interest. In forums where users …...

We propose a flexible and effective frame- work for extracting a bilingual dictionary from comparable corpora. Our approach is based on a novel combination of topic modeling and word alignment …...

We investigate the value-add of topic model- ing in text analysis for depression, and for neu- roticism as a strongly associated personality measure. Using Pennebaker’s Linguistic In- quiry and Word …...

Spectral methods offer scalable alternatives to Markov chain Monte Carlo and expec- tation maximization. However, these new methods lack the rich priors associated with probabilistic models. We examine Arora et …...

Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular method for the task. However, unsupervised topic models often generate incoherent aspects. To address the is- …...

Topic modeling is an unsupervised ma- chine-learning task of discovering topics, the underlying thematic structure in a text corpus. Dynamic topic models are capa- ble of analysing the time evolution …...

AbstractWe present heterogeneous networks as a way to unify lexical networks with relational data. We build a unified ACL Anthology network, tying together the citation, author collaboration, and term-cooccurence networks …...

. In this article, we propose a study on the words or multi-words which are good indicators of the opinion polarity of a text but have different polarity depending on …...

ThisThis work is licensed under a Creative Commons Attribution 4.0 International Li- cence. Page numbers and proceedings footer are added by the organisers. Licence details: http://creativecommons.org/licenses/by/4.0/ paper describes the second …...

Knowledge Base Population (KBP) tasks, such as slot filling, show the particular importance of entity-oriented automatic relevant document acquisition. Rich, diverse and reliable relevant documents satisfy the fundamental require- ment …...

Argumentation mining and stance classifica- tion were recently introduced as interesting tasks in text mining. In this paper, a novel framework for argument tagging based on topic modeling is proposed. …...

In this paper, we investigate the challenging task of understanding short text (STU task) by jointly considering topic modeling and knowl- edge incorporation. Knowledge incorporation can solve the content sparsity …...

We describe effort to improve machine translation of item titles found in a large e-commerce inventory through topic modeling and adaptation. Item titles are short texts which typically contain brand …...

Content analysis, a widely-applied social sci- ence research method, is increasingly being supplemented by topic modeling. However, while the discourse on content analysis cen- ters heavily on reproducibility, computer sci- …...

AbstractRecently, topic modeling has been widely applied in data mining due to its powerful ability. A common, major challenge in applying such topic models to other tasks is to accurately …...

AbstractRule-based stemmers such as the Porter stemmer are frequently used to preprocess English corpora for topic modeling. In this work, we train and evaluate topic models on a variety of …...

Significant events are characterized by interac- tions between entities (such as countries, or- ganizations, or individuals) that deviate from typical interaction patterns. Analysts, includ- ing historians, political scientists, and journal- …...

Word embedding maps words into a low- dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window. On the other hand, topic modeling maps …...

AbstractMicroblogging services allow users to create hashtags to categorize their posts. In recent years, the task of recommending hashtags for microblogs has been given increasing attention. However, most of existing …...

AbstractGaussian LDA integrates topic modeling with word embeddings by replacing discrete topic distribution over word types with multivariate Gaussian distribution on the embedding space. This can take semantic information of …...

AbstractWe propose an approach to Named Entity Disambiguation that avoids a problem of standard work on the task (likewise affecting fully supervised, weakly supervised, or distantly supervised machine learning techniques): …...

We describe our submission to SemEval2016 Task 4: Sentiment Analysis in Twitter. The proposed system ranked first for the sub- task B. Our system comprises of multiple in- dependent models …...

AbstractWhile generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only …...

AbstractPart of speech (POS) taggers and dependency parsers tend to work well on homogeneous datasets but their performance suffers on datasets containing data from different genres. In our current work, …...

AbstractHighlighting the recurrence of topics usage in candidates speeches is a key feature to identify the main ideas of each candidate during a political campaign. In this paper, we present …...

AbstractText generation is among the most fundamental tasks in natural language processing. In this paper, we propose a text generation model that learns semantics and structural features simultaneously. This model …...

AbstractCross-referencing, which links passages of text to other related passages, can be a valuable study aid for facilitating comprehension of a text. However, cross-referencing requires first, a comprehensive thematic knowledge …...

AbstractAbusive language detection has received much attention in the last years, and recent approaches perform the task in a number of different languages. We investigate which factors have an effect …...

AbstractVisualization and topic modeling are widely used approaches for text analysis. Traditional visualization methods find low-dimensional representations of documents in the visualization space (typically 2D or 3D) that can be …...

AbstractCrowdsourcing is used in academia and industry to solve tasks that are easy for humans but hard for computers, in natural language processing mostly to annotate data. The quality of …...

AbstractIn natural language processing, the performance of a semantic similarity task relies heavily on the availability of a large corpus. Various monolingual corpora are available (mainly English); but multilingual resources …...

AbstractTopic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we …...

Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus. It has been increasingly widely adopted as a tool in the social sciences, in- cluding …...

AbstractThere is a shortage of high-quality corpora for South-Slavic languages. Such corpora are useful to computer scientists and researchers in social sciences and humanities alike, focusing on numerous linguistic, content …...

AbstractThough word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity in short-text or small collection …...

AbstractUnderstanding voluminous historical records provides clues on the past in various aspects, such as social and political issues and even natural science facts. However, it is generally difficult to fully …...

AbstractRecent research in opinion mining proposed word embedding-based topic modeling methods that provide superior coherence compared to traditional topic modeling. In this paper, we demonstrate how these methods can be …...

AbstractMedical code prediction from clinical notes aims at automatically associating medical codes with the clinical notes. Rare code problem, the medical codes with low occurrences, is prominent in medical code …...

AbstractWe propose an approach for comparing curricula of study programs in higher education. Pre-trained word embeddings are fine-tuned in a study program classification task, where each curriculum is represented by …...

AbstractRecently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable performance to the continuous counterparts …...

AbstractVarious historical languages, which used to be lingua franca of science and arts, deserve the attention of current NLP research. In this work, we take the first data-driven steps towards …...

AbstractSocial media platforms host discussions about a wide variety of topics that arise everyday. Making sense of all the content and organising it into categories is an arduous task. A …...

AbstractTopic modeling is a commonly used method for identifying and extracting topics from a corpus of documents. While several evaluation techniques, such as perplexity and topic coherence, have been developed …...

AbstractUnsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. …...

AbstractExisting research on multimodal relation extraction (MRE) faces two co-existing challenges, internal-information over-utilization and external-information under-exploitation. To combat that, we propose a novel framework that simultaneously implements the idea of …...

AbstractNatural Language Processing techniques can be leveraged to process legal proceedings for various downstream applications, such as sum- marization of a given judgement, prediction of the judgement for a given …...

AbstractTopic modeling of a text corpus is one of the most well-studied areas of information retrieval and knowledge discovery. Despite several decades of research in the area that begets an …...