Набор данных 1

This paper proposes a multi-dimensional framework for classifying text documents. In this framework, the concept of multi- dimensional category model is introduced for representing classes. In contrast with traditional flat …...

Previously topic models such as PLSI (Prob- abilistic Latent Semantic Indexing) and LDA (Latent Dirichlet Allocation) were developed for modeling the contents of plain texts. Re- cently, topic models for …...

The ability to correctly classify sentences that describe events is an important task for many natural language applications such as Question Answering (QA) and Sum- marisation. In this paper, we …...

We explore the task of automatic classifica- tion of texts by the emotions expressed. Our novel method arranges neutrality, polarity and emotions hierarchically. We test the method on two datasets …...

This paper investigates novel methods for in- corporating syntactic information in proba- bilistic latent variable models of lexical choice and contextual similarity. The resulting mod- els capture the effects of …...

. With the advent of consumer generated media (e.g., Amazon reviews, Twitter, etc.), sentiment classification becomes a heated topic. Previous work heavily relies on a large amount of linguistic resources, …...

Most traditional summarization methods treat their outputs as static and plain texts, which fail to capture user interests during summa- rization because the generated summaries are the same for different …...

We investigate an important and challeng- ing problem in summary generation, i.e., Evolutionary Trans-Temporal Summarization (ETTS), which generates news timelines from massive data on the Internet. ETTS greatly facilitates fast …...

A large number of Open Relation Extrac- tion approaches have been proposed recently, covering a wide range of NLP machinery, from “shallow” (e.g., part-of-speech tagging) to “deep” (e.g., semantic role …...

Extracting opinion targets and opinion words from online reviews are two fun- damental tasks in opinion mining. This paper proposes a novel approach to col- lectively extract them with graph …...

A prerequisite relation describes a basic relation among concepts in cognition, ed- ucation and other areas. However, as a se- mantic relation, it has not been well stud- ied in …...

In this paper we describe our system (DT- Sim) submitted at SemEval-2016 Task 1: Se- mantic Textual Similarity (STS Core). We developed Support Vector Regression model with various features including …...

Recurrent neural network language models (RNNLM) have recently demonstrated vast potential in modelling long-term dependen- cies for NLP problems, ranging from speech recognition to machine translation. In this work, we …...

Recent approaches based on artificial neural networks (ANNs) have shown promising re- sults for short-text classification. However, many short texts occur in sequences (e.g., sen- tences in a document or …...

AbstractThe SpeDial consortium is sharing two datasets that were used during the SpeDial project. By sharing them with the community we are providing a resource to reduce the duration of …...

We present a new evaluation scheme for the lexical substitution task. Following (McCarthy and Navigli, 2007) we conducted an annotation task for French that mixes two datasets: in the first …...

Classifying the relationship between entities is an important natural language processing (NLP) task. Scientific Relation Classification aims at automatically categorizing scientific semantic relationships among entities in sci- entific documents. Conventionally, …...

AbstractAutomating the assessment of learner summary provides a useful tool for assessing learner reading comprehension. We present a summarization task for evaluating non-native reading comprehension and propose three novel approaches …...

AbstractWe propose BeamSeg, a joint model for segmentation and topic identification of documents from the same domain. The model assumes that lexical cohesion can be observed across documents, meaning that …...

AbstractWhile the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different …...

Post-editing (PE) machine translation (MT) is widely used for dissemination because it leads to higher productivity than human translation from scratch (HT). In addition, PE translations are found to be …...

AbstractAn arithmetic word problem typically includes a textual description containing several constant quantities. The key to solving the problem is to reveal the underlying mathematical relations (such as addition and …...

AbstractIn this paper, we study the problem of hybrid language modeling, that is using models which can predict both characters and larger units such as character ngrams or words. Using …...

AbstractWe present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that …...

AbstractEntity set expansion, aiming at expanding a small seed entity set with new entities belonging to the same semantic class, is a critical task that benefits many downstream NLP and …...

AbstractAutomatic analysis of connected speech by natural language processing techniques is a promising direction for diagnosing cognitive impairments. However, some difficulties still remain: the time required for manual narrative transcription …...

AbstractRecently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, since the lattice structure is complex and …...

AbstractTraining the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems. Existing methods tend to use the meta-learning framework which pre-trains the parameters …...

AbstractLinguistic steganography studies how to hide secret messages in natural language cover texts. Traditional methods aim to transform a secret message into an innocent text via lexical substitution or syntactical …...

AbstractAspect-based sentiment analysis (ABSA) involves three subtasks, i.e., aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Most existing studies focused on one of these subtasks only. Several recent …...

AbstractWe present Seq2SeqPy a lightweight toolkit for sequence-to-sequence modeling that prioritizes simplicity and ability to customize the standard architectures easily. The toolkit supports several known architectures such as Recurrent Neural …...

AbstractChinese Spelling Check (CSC) is a task to detect and correct spelling errors in Chinese natural language. Existing methods have made attempts to incorporate the similarity knowledge between Chinese characters. …...

AbstractSpelling error correction is an important yet challenging task because a satisfactory solution of it essentially needs human-level language understanding ability. Without loss of generality we consider Chinese spelling error …...

AbstractEmotion stimulus detection is the task of finding the cause of an emotion in a textual description, similar to target or aspect detection for sentiment analysis. Previous work approached this …...

AbstractAs an important task in opinion mining, comparative opinion mining aims to identify comparative sentences from product reviews, extract the comparative elements, and obtain the corresponding comparative opinion tuples. However, …...

AbstractTraditional event extraction methods require predefined event types and their corresponding annotations to learn event extractors. These prerequisites are often hard to be satisfied in real-world applications. This work presents …...

AbstractFrame identification is one of the key challenges for frame-semantic parsing. The goal of this task is to determine which frame best captures the meaning of a target word or …...

AbstractWe study a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical …...

AbstractMost current neural machine translation models adopt a monotonic decoding order of either left-to-right or right-to-left. In this work, we propose a novel method that breaks up the limitation of …...

AbstractDialog is a core building block of human natural language interactions. It contains multi-party utterances used to convey information from one party to another in a dynamic and evolving manner. …...

AbstractCross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario. A common practice is first to learn a NER model in a rich-resource general …...

AbstractComparative Question Answering (cQA) is the task of providing concrete and accurate responses to queries such as: “Is Lyft cheaper than a regular taxi?” or “What makes a mortgage different …...

AbstractAs a fundamental task in opinion mining, aspect and opinion co-extraction aims to identify the aspect terms and opinion terms in reviews. However, due to the lack of fine-grained annotated …...

AbstractIn this paper we present our approach and system description on iSarcasmEval: a SemEval task for intended sarcasm detection on social networks. This derives from our participation in SubTask A: …...

AbstractModern Natural Language Processing (NLP) models are known to be sensitive to input perturbations and their performance can decrease when applied to real-world, noisy data. However, it is still unclear …...

AbstractDespite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the …...

AbstractThe de-identification of sensible data, also known as automatic textual anonymisation, is essential for data sharing and reuse, both for research and commercial purposes. The first step for data anonymisation …...

AbstractTraining mixed-domain translation models is a complex task that demands tailored architec- tures and costly data preparation techniques. In this work, we leverage federated learning (FL) in order to tackle …...

AbstractCurrent clustering-based Open Relation Extraction (OpenRE) methods usually adopt a two-stage pipeline, which simultaneously learns relation representations and assignments in the first stage, then manually labels relation for each cluster. …...

AbstractIn-context learning has shown great success in i.i.d semantic parsing splits, where the training and test sets are drawn from the same distribution. In this setup, models are typically prompted …...

AbstractConceptual metaphors present a powerful cognitive vehicle to transfer knowledge structures from a source to a target domain. Prior neural approaches focus on detecting whether natural language sequences are metaphoric …...

AbstractA novel feature represents a cluster of semantically equivalent novel user requests e.g., requests to play a song on a service or read user’s messages. Detecting and supporting novel features …...

AbstractExtracting generalized and robust representations is a major challenge in emotion recognition in conversations (ERC). To address this, we propose a supervised adversarial contrastive learning (SACL) framework for learning class-spread …...