Classification

Traditional statistical speech recognition systems typically make strong assumptions about the independence of obser- vation frames and generally do not make use of segmental information. In contrast, when the segmentation …...

Several classification and routing methods were im- plemented and compared. The experiments used FBIS documents from four categories, and the measures used were the ff.idf and Cosine similarity measures, and …...

This paper investigates several augmented mixture models that are competitive alternatives to standard Bayesian models and prove to be very suitable to word sense disambiguation and related classifica- tion tasks. …...

This paper presents an approach to depen- dency parsing which can utilize any stan- dard machine learning (classification) al- gorithm. A decision list learner was used in this work. The …...

This paper advocates a new architecture for tex- tual inference in which finding a good alignment is separated from evaluating entailment. Current ap- proaches to semantic inference in question answer- …...

Recent work on Semantic Role Labeling (SRL) has shown that to achieve high accuracy a joint inference on the whole predicate argument structure should be ap- plied. In this paper, …...

AbstractFor mining intellectual property texts (patents), a broad-coverage lexicon that covers general English words together with terminology from the patent domain is indispensable. The patent domain is very diffuse as …...

Near-synonyms are useful knowledge re- sources for many natural language applica- tions such as query expansion for information retrieval (IR) and paraphrasing for text gen- eration. However, near-synonyms are not …...

AbstractIn political speeches, the audience tends to react or resonate to signals of persuasive communication, including an expected theme, a name or an expression. Automatically predicting the impact of such …...

Resolving coordination ambiguity is a clas- sic hard problem. This paper looks at co- ordination disambiguation in complex noun phrases (NPs). Parsers trained on the Penn Treebank are reporting impressive …...

This paper describes the features and the ma- chine learning methods used by Dublin City University (DCU) and SYMANTEC for the WMT 2012 quality estimation task. Two sets of features …...

AbstractWe develop a supersense taxonomy for adjectives, based on that of GermaNet, and apply it to English adjectives in WordNet using human annotation and supervised classification. Results show that accuracy …...

AbstractIn this paper, we present the experiments we made to process entities from the biomedical domain. Depending on the task to process, we used two distinct supervised machine-learning techniques: Conditional …...

AbstractThis paper presents a system for suggesting a ranked list of appropriate vacancy descriptions to job seekers in a job board web site. In particular our work has explored the …...

Keyphrases have found wide ranging application in NLP and IR tasks such as document sum- marization, indexing, labeling, clustering and classification. In this paper we pose the problem of extracting …...

Different approaches to high-quality grammatical error correction have been proposed recently, many of which have their own strengths and weaknesses. Most of these approaches are based on classi- fication or …...

We present a system for fine-grained senti- ment analysis in Bulgarian movie reviews. As this is pioneering work for this com- bination of language and sentiment gran- ularity, we create …...

This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurative language in Twitter. We consid- ered two approaches, classification and regres- sion, to provide fine-grained …...

AbstractIn this paper, we present the automatic annotation of bibliographical references’ zone in papers and articles of XML/TEI format. Our work is applied through two phases: first, we use machine …...

AbstractThis paper presents classification results for the analysis of sentiment in political news articles. The domain of political news is particularly challenging, as journalists are presumably objective, whilst at the …...

This paper studies how word embeddings trained on the British National Corpus in- teract with part of speech boundaries. Our work targets the Universal PoS tag set, which is currently …...

AbstractThe conventional solution for handling sparsely labelled data is extensive feature engineering. This is time consuming and task and domain specific. We present a novel approach for learning embedded features …...

AbstractContinuous word representations have been remarkably useful across NLP tasks but remain poorly understood. We ground word embeddings in semantic spaces studied in the cognitive-psychometric literature, taking these spaces as …...

AbstractNatural language constitutes a predominant medium for much of human learning and pedagogy. We consider the problem of concept learning from natural language explanations, and a small number of labeled …...

We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for sarcasm research and for training and evaluating systems for sarcasm detection. The corpus has 1.3 million sarcastic statements — …...

AbstractThis paper discusses on task 1, “Affect in Tweets” sharedtask, conducted in SemEval-2018. This task comprises of various subtasks, which required participants to analyse over different emotions and sentiments based …...

AbstractWe study German affixoids, a type of morpheme in between affixes and free stems. Several properties have been associated with them – increased productivity; a bleached semantics, which is often …...

AbstractA context-aware language model uses location, user and/or domain metadata (context) to adapt its predictions. In neural language models, context information is typically represented as an embedding and it is …...

Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem. This has immense importance in widespread Natural Language Processing (NLP) applications ranging from extractive text document summarization …...

AbstractIn this work we evaluate applicability of entity pair models and neural network architectures for relation extraction and classification in scientific papers at SemEval-2018. We carry out experiments with representing …...

AbstractGovernmental institutions are employing artificial intelligence techniques to deal with their specific problems and exploit their huge amounts of both structured and unstructured information. In particular, natural language processing and …...

AbstractTwitter is used for various applications such as disaster monitoring and news material gathering. In these applications, each Tweet is classified into pre-defined classes. These classes have a semantic relationship …...

AbstractIn recent years an increasing number of analyses of offensive language has been published, however, dealing mainly with the automatic detection and classification of isolated instances. In this paper we …...

AbstractFree text data from social media is now widely used in natural language processing research, and one of the most common machine learning tasks performed on this data is classification. …...

AbstractArgument mining systems often consider contextual information, i.e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction. However, …...

AbstractFake news has altered society in negative ways in politics and culture. It has adversely affected both online social network systems as well as offline communities and conversations. Using automatic …...

Japanese has a grammatical class commonly referred to as “verbal nouns (VN)”, whose members form a phrasal verb in combination with the light verb SURU. While most VNs can be …...

AbstractThis work presents our contribution in the context of the 6th task of SemEval-2020: Extracting Definitions from Free Text in Textbooks (DeftEval). This competition consists of three subtasks with different …...

AbstractPre-training has improved model accuracy for both classification and generation tasks at the cost of introducing much larger and slower models. Pruning methods have proven to be an effective way …...

Preprocessing is essential for creating more ef- fective features and reducing noise in classifi- cation, especially in user-generated data (e.g. Twitter). How each individual preprocessing decision changes an individual classifier’s …...

AbstractThe recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, …...

AbstractAdvancements in Natural Language Generation have raised concerns on its potential misuse for deep fake news. Grover is a model for both generation and detection of neural fake news. While …...

AbstractUnsupervised relation extraction works by clustering entity pairs that have the same relations in the text. Some existing variational autoencoder (VAE)-based approaches train the relation extraction model as an encoder …...

The analysis of short text documents has be- come a vital and challenging task. Topic mod- els are utilized to extract topics from a large amount of text data. However, …...

AbstractAutomatic Readability Assessment (ARA), the task of assigning a reading level to a text, is traditionally treated as a classification problem in NLP research. In this paper, we propose the …...

AbstractWith the increase in availability of large pre-trained language models (LMs) in Natural Language Processing (NLP), it becomes critical to assess their fit for a specific target task a priori—as …...

AbstractThis paper presents the results and main findings of our system on SemEval-2022 Task 3 Presupposed Taxonomies: Evaluating Neural Network Semantics (PreTENS). This task aims at semantic competence with specific …...

AbstractWe introduce MetaICL (Meta-training for In-Context Learning), a new meta-training framework for few-shot learning where a pretrained language model is tuned to do in-context learning on a large set of …...

AbstractThe prevalent use of offensive content in social media has become an important reason for concern for online platforms (customer service chat-boxes, social media platforms, etc). Classifying offensive and hate-speech …...

AbstractAs manually labelling data can be costly, some recent studies tend to augment the training data for improving the generalization power of machine learning models, known as data augmentation (DA). …...

AbstractIn human languages, there are many presuppositional constructions that impose a constrain on the taxonomic relations between two nouns depending on their order. These constructions create a challenge in validating …...

AbstractRecent legislation of the “right to be forgotten” has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about specific …...

AbstractA real-world text corpus sometimes comprises not only text documents, but also semantic links between them (e.g., academic papers in a bibliographic network are linked by citations and co-authorships).Text documents …...