Performance Improvement

We present two methods for learning the struc- ture of personal names from unlabeled data. The first simply uses a few implicit constraints governing this structure to gain a toehold …...

The development of FrameNet, a large database of semantically annotated sen- tences, has primed research into statistical methods for semantic tagging. We ad- vance previous work by adopting a Maximum …...

Recent work in Question Answering has focused on web-based systems that extract answers using simple lexico- syntactic patterns. We present an alternative strategy in which patterns are used to extract …...

This study describes a general framework for adaptive word sense disambiguation. The proposed framework begins with knowledge acquisition from the relatively easy context of a corpus. The proposed framework heavily …...

Supervised learning methods for WSD yield better performance than unsupervised methods. Yet the availability of clean training data for the former is still a severe challenge. In this paper, we …...

In this paper, we propose a machine learning al- gorithm for shallow semantic parsing, extend- ing the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our …...

We propose an approach to summarization exploiting both lexical information and the output of an automatic anaphoric re- solver, and using Singular Value Decom- position (SVD) to identify the main …...

In this paper we present an extension of a machine learning based coreference res- olution system which uses features in- duced from different semantic knowledge sources. These features represent knowl- …...

Syntactic knowledge is important for pro- noun resolution. Traditionally, the syntac- tic information for pronoun resolution is represented in terms of features that have to be selected and defined heuristically. …...

In this paper, we discuss how to utilize the co-occurrence of answers in building an automatic question answering system that answers a series of questions on a specific topic in …...

Different parsers trained on the same cor- pus deliver different results, both in terms of overall performance and in terms of the individual analyses they provide. In par- ticular, for …...

Creating large amounts of annotated data to train statistical PCFG parsers is expensive, and the performance of such parsers declines when training and test data are taken from different domains. …...

Recent research presents conflicting evi- dence on whether word sense disambigua- tion (WSD) systems can help to improve the performance of statistical machine transla- tion (MT) systems. In this paper, …...

We propose a machine learning based method of sentiment classification of sen- tences using word-level polarity. The polari- ties of words in a sentence are not always the same as …...

Inspired by the success of English grapheme-to-phoneme research in speech synthesis, many researchers have pro- posed phoneme-based English-to-Chinese transliteration models. However, such ap- proaches have severely suffered from the errors …...

We present a Hebrew to English transliter- ation method in the context of a machine translation system. Our method uses ma- chine learning to determine which terms are to be …...

We explore how to improve machine trans- lation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck the …...

The research question treated in this paper is centered on the idea of exploiting rich re- sources of one language to enhance the per- formance of a mention detection system …...

We propose a global algorithm for learn- ing entailment relations between predi- cates. We define a graph structure over predicates that represents entailment rela- tions as directed edges, and use …...

. This paper deals with how to enhance the performance of a rule-based parser using statistical Information. PP (Prepositional Phrase) attachment ambiguity is one of the main ambiguities found in …...

This paper focuses on mining the hypon- ymy (or is-a) relation from large-scale, open-domain web documents. A nonlinear probabilistic model is exploited to model the correlation between sentences in the …...

Sentiment analysis on Twitter data has attract- ed much attention recently. In this paper, we focus on target-dependent Twitter sentiment classification; namely, given a query, we clas- sify the sentiments …...

Combination of approaches for speaker role recognition In this article, we are particularly interested in recognizing speaker role inside broadcast news shows. Previous studies highlighted a link between speech spontaneity …...

In Statistical Machine Translation, in- domain and out-of-domain training data are not always clearly delineated. This paper investigates how we can still use mixture-modeling techniques for domain adaptation in such …...

We present algorithms for identifying Hindi Noun Groups and Verb Groups in a given text by using morphotactical constraints and sequencing that apply to the constituents of these groups. We …...

AbstractThis paper presents some novel results on Chinese spell checking. In this paper, a concise algorithm based on minimized-path segmentation is proposed to reduce the cost and suit the needs …...

Previous work on paraphrase extraction us- ing parallel or comparable corpora has gener- ally not considered the documents’ discourse structure as a useful information source. We propose a novel method …...

In this paper we explore the utility of sen- timent analysis and semantic word classes for improving why-question answering on a large-scale web corpus. Our work is moti- vated by …...

In a meeting, it is often desirable to extract keywords from each utterance as soon as it is spoken. Thus, this paper proposes a just-in- time keyword extraction from meeting …...

In this paper we report an empirical study on semi-supervised Chinese word segmenta- tion using co-training. We utilize two seg- menters: 1) a word-based segmenter lever- aging a word-level language …...

The goal of our research is to distinguish vet- erinary message board posts that describe a case involving a specific patient from posts that ask a general question. We create …...

We report the results of our work on automat- ing the transliteration decision of named en- tities for English to Arabic machine trans- lation. We construct a classification-based framework to …...

Prior research into learning translations from source and target language monolingual texts has treated the task as an unsupervised learn- ing problem. Although many techniques take advantage of a seed …...

Given the large amounts of online textual documents available these days, e.g., news articles, weblogs, and scientific papers, ef- fective methods for extracting keyphrases, which provide a high-level topic descrip- …...

Cross-narrative temporal ordering of med- ical events is essential to the task of gen- erating a comprehensive timeline over a patient’s history. We address the prob- lem of aligning multiple …...

We propose a robust answer reranking model for non-factoid questions that inte- grates lexical semantics with discourse in- formation, driven by two representations of discourse: a shallow representation cen- tered …...

AbstractStress has long been established as a major cue in word segmentation for English infants. We show that enabling a current state-of-the-art Bayesian word segmentation model to take advantage of …...

We present a novel way of extracting fea- tures from short texts, based on the acti- vation values of an inner layer of a deep convolutional neural network. We use …...

This paper discusses the use of factoriza- tion techniques in distributional semantic models. We focus on a method for re- distributing the weight of latent variables, which has previously been …...

Part-of-Speech (POS) tagging is a key step in many NLP algorithms. However, tweets are difficult to POS tag because there are many phenomena that frequently appear in Twitter that are …...

We introduce a deep memory network for aspect level sentiment classification. Un- like feature-based SVM and sequential neural models such as LSTM, this approach explic- itly captures the importance of …...

AbstractSequence labeling architectures use word embeddings for capturing similarity, but suffer when handling previously unseen or rare words. We investigate character-level extensions to such models and propose a novel architecture …...

AbstractAlthough more additional corpora are now available for Statistical Machine Translation (SMT), only the ones which belong to the same or similar domains of the original corpus can indeed enhance …...

AbstractEmotion Recognition (ER) is an important part of dialogue analysis which can be used in order to improve the quality of Spoken Dialogue Systems (SDSs). The emotional hypothesis of the …...

When considering a social media corpus, we often have access to structural information about how messages are flowing between peo- ple or organizations. This information is par- ticularly useful when …...

Frames defined in FrameNet (FN) share highly similar structures with events in ACE event extraction program. An even- t in ACE is composed of an event trig- ger and a …...

This paper presents neural probabilistic parsing models which explore up to third- order graph-based parsing with maximum likelihood training criteria. Two neural network extensions are exploited for per- formance improvement. …...

AbstractCrowdsourcing offers a convenient means of obtaining labeled data quickly and inexpensively. However, crowdsourced labels are often noisier than expert-annotated data, making it difficult to aggregate them meaningfully. We present …...

AbstractQuestion answering (QA) systems are easily distracted by irrelevant or redundant words in questions, especially when faced with long or multi-sentence questions in difficult domains. This paper introduces and studies …...

AbstractLearning internal word structure has recently been recognized as an important step in various multilingual processing tasks and in theoretical language comparison. In this paper, we present a neural encoder-decoder …...

AbstractThe generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical …...

AbstractThe large and growing amounts of online scholarly data present both challenges and opportunities to enhance knowledge discovery. One such challenge is to automatically extract a small set of keyphrases …...

AbstractSelectional preferences have long been claimed to be essential for coreference resolution. However, they are modeled only implicitly by current coreference resolvers. We propose a dependency-based embedding model of selectional …...

AbstractWord embeddings seek to recover a Euclidean metric space by mapping words into vectors, starting from words co-occurrences in a corpus. Word embeddings may underestimate the similarity between nearby words, …...

AbstractSubmodular maximization with the greedy algorithm has been studied as an effective approach to extractive summarization. This approach is known to have three advantages: its applicability to many useful submodular …...

AbstractIn this paper, we present an adaptive convolution for text classification to give flexibility to convolutional neural networks (CNNs). Unlike traditional convolutions which utilize the same set of filters regardless …...

AbstractThis paper examines various unsupervised pretraining objectives for learning dialog context representations. Two novel methods of pretraining dialog context encoders are proposed, and a total of four methods are examined. …...

AbstractBERT-based architectures currently give state-of-the-art performance on many NLP tasks, but little is known about the exact mechanisms that contribute to its success. In the current work, we focus on …...

AbstractChinese idioms are special fixed phrases usually derived from ancient stories, whose meanings are oftentimes highly idiomatic and non-compositional. The Chinese idiom prediction task is to select the correct idiom …...

AbstractThe performance of standard coreference resolution is known to drop significantly on Twitter texts. We improve the performance of the (Lee et al., 2018) system, which is originally trained on …...

We present a method for Chinese spelling check that automatically learns to correct a sentence with potential spelling errors. In our approach, a character-based neural machine translation (NMT) model is …...

AbstractWe investigate how to improve quality of low-resource morphological inflection without annotating more data. We examine two methods, language models and data augmentation. We show that the model whose decoder …...

AbstractHindi-English Machine Translation is a challenging problem, owing to multiple factors including the morphological complexity and relatively free word order of Hindi, in addition to the lack of sufficient parallel …...

AbstractComplex reasoning over text requires understanding and chaining together free-form predicates and logical connectives. Prior work has largely tried to do this either symbolically or with black-box transformers. We present …...

AbstractWe propose a multi task learning-based neural model for resolving bridging references tackling two key challenges. The first challenge is the lack of large corpora annotated with bridging references. To …...

AbstractWe present Odinson, a rule-based information extraction framework, which couples a simple yet powerful pattern language that can operate over multiple representations of text, with a runtime system that operates …...

AbstractLarge scale pre-trained language models have shown groundbreaking performance improvements for transfer learning in the domain of natural language processing. In our paper, we study a pre-trained multilingual BERT model …...

AbstractVerifying fact on semi-structured evidence like tables requires the ability to encode structural information and perform symbolic reasoning. Pre-trained language models trained on natural language could not be directly applied …...

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, …...

AbstractModeration of reader comments is a significant problem for online news platforms. Here, we experiment with models for automatic moderation, using a dataset of comments from a popular Croatian newspaper. …...

AbstractDependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA) (CITATION). However, the trees obtained from off-the-shelf dependency parsers are static, and could be sub-optimal …...

AbstractThe keyphrase generation task is a challenging work that aims to generate a set of keyphrases for a piece of text. Many previous studies based on the sequence-to-sequence model were …...

AbstractThis paper presents Diff-Explainer, the first hybrid framework for explainable multi-hop inference that integrates explicit constraints with neural architectures through differentiable convex optimization. Specifically, Diff- Explainer allows for the fine-tuning …...

AbstractThe performance of multilingual pretrained models is highly dependent on the availability of monolingual or parallel text present in a target language. Thus, the majority of the world’s languages cannot …...

AbstractPrompt-based techniques have demostrated great potential for improving the few-shot generalization of pretrained language models. However, their performance heavily relies on the manual design of prompts and thus requiring a …...

AbstractIn this paper, we use several combinations of feature front-end modules and attention mechanisms to improve the performance of our speaker verification system. An updated version of ECAPA-TDNN is chosen …...

AbstractLarge pretrained multilingual models, trained on dozens of languages, have delivered promising results due to cross-lingual learning capabilities on a variety of language tasks. Further adapting these models to specific …...

AbstractStructured Sentiment Analysis is the task of extracting sentiment tuples in a graph structure commonly from review texts. We adapt the Aspect-Based Sentiment Analysis pointer network BARTABSA to model this …...

AbstractMeta-learning has emerged as an effective approach for few-shot text classification. However, current studies fail to realize the importance of the semantic interaction between sentence features and neglect to enhance …...

AbstractUnderstanding tables is an important aspect of natural language understanding. Existing models for table understanding require linearization of the table structure, where row or column order is encoded as an …...

AbstractOur goal is a teachable reasoning system for question-answering (QA), where a user can interact with faithful answer explanations, and correct its errors so that the system improves over time. …...

AbstractRelation extraction (RE), which has relied on structurally annotated corpora for model training, has been particularly challenging in low-resource scenarios and domains. Recent literature has tackled low-resource RE by self-supervised …...

AbstractRecent NLP models have shown the remarkable ability to effectively generalise ‘zero-shot’ to new tasks using only natural language instructions as guidance. However, many of these approaches suffer from high …...

AbstractArgument maps structure discourse into nodes in a tree with each node being an argument that supports or opposes its parent argument. This format is more comprehensible and less redundant …...

AbstractThe following is a description of the RIGA team’s submissions for the English track of the SemEval-2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER) II. Our approach achieves 17% …...

AbstractPrior work has shown that coupling sequential latent variable models with semantic ontological knowledge can improve the representational capabilities of event modeling approaches. In this work, we present a novel, …...

AbstractInternet Memes remain a challenging form of user-generated content for automated sentiment classification. The availability of labelled memes is a barrier to developing sentiment classifiers of multimodal memes. To address …...