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The quality of an SMT system has improved quite a lot since late 90"s and different types of SMT systems have been proposed over the last decade. The quality of SMT, however, is still not sufficient for actual use. For instance, we have been using a syntax-based SMT system for the last several years to localize technic... | 0 |
In communicative contexts, there is more to language use than the individual processing of representations. When two or more interlocutors take part in a conversation, they engage in a joint activitya type of social interaction that requires an intricate level of interpersonal coordination. This often leads to interloc... | 0 |
The N-Best Paradigm [1] was introduced originally as a means for integrating the speech recognition and language understanding components of a spoken language system. Since then, we have generalized its use for integrating into the recognition search other expensive knowledge sources (such as higher-order n-gram langua... | 0 |
Words have associations with sentiment. For example, honest and competent are associated with positive sentiment, whereas dishonest and dull are associated with negative sentiment. Further, the degree of positivity (or negativity), also referred to as sentiment intensity, can vary. For example, most people will agree t... | 0 |
Word alignment is an important step in exploiting parallel corpora. When efficient techniques have been proposed (Brown et al., 1993; Och and Ney, 2003) , they have been mostly evaluated on "safe" pairs of languages where the notion of word is rather clear.We devoted two weeks to the intriguing task of aligning at the ... | 0 |
Corpora of collective discourse-texts generated by multiple authors in response to the same stimulus-have varying properties depending on the stimulus and goals of the authors. For instance, when multiple puzzle-composers write crossword puzzle clues for the same word, they will try to write creative, unique clues to m... | 0 |
The goal of multilingual syntactic transfer is to parse a resource lean target language utilizing annotations available in other languages. Recent approaches have demonstrated that such transfer is possible, even in the absence of parallel data. As a main source of guidance, these methods rely on the commonalities in d... | 0 |
Topic model (Blei et al., 2003) is one of the most popular approaches to learn hidden representations of text. The broad applications of topic model range from recommender systems (Wang and Blei, 2011) , computer vision (Fei-Fei and Perona, 2005) , to bioinformatics (Rogers et al., 2005) . Conventional topic models lea... | 0 |
The EUTRANS project, funded by the European Union, aims at developing Machine Translation (MT) systems for limited domain applications that require text and/or speech input, using Example Based techniques. This paper describes the techniques employed in the text-to-text translation system developed in this project. The... | 0 |
The analysis of diachronic corpora is of great interest to linguistics, history and cultural studies alike. The challenges in dealing with diachronic material are manifold, ranging from corpus compilation and annotation to analysis. Here, we address questions of analysis, notably the data-driven detection and evaluatio... | 0 |
Machine translation (MT) is now widely used in a variety of fields, mainly due to advancements in neural models (Sutskever et al., 2014; Bahdanau et al., 2015; Vaswani et al., 2017) . As a result of these recent advances, scientists have been increasingly attempting to include discourse into neural machine translation ... | 0 |
An important distinction in document summarization is between generic summaries, which capture the central ideas of the document in much the same way that the abstract of this paper was designed to distill its salient points, and query-relevant summaries, which reflect the relevance of a document to a user-specified qu... | 0 |
This paper presents the systems submitted by the Abu-MaTran project (runs named DCU-Prompsit-UA) to the WMT 2014 translation task for the English-French language pair. Phrase-based statistical machine translation (SMT) systems were submitted, considering the two translation directions, with the focus on the English to ... | 0 |
The written text that we interact with on an everyday basis-news articles, emails, social media, bookscontains a vast amount of information centered on people: news (including common NLP corpora such as the New York Times and the Wall Street Journal) details the roles of actors in current events, social media (includin... | 0 |
In the field ofknowledge-bMed text analysis it has been regarded as insufficient to analyze a text against the background of static and stereotypic default assumptions for some time (cf. , [Scha/Bruce/Polanyi87]). By applying this method the pre--coded concepts are invoked again and again during the process of text ana... | 0 |
The automated analysis of large corpora has many useful applications (Church and Mercer 1993) . Suitable language repositories can be used for deriving models of a given natural language, as needed for speech recognition (Ostendorf, Digalakis, and Kimball 1996; Jelinek 1997; Chelba and Jelinek 2002) , language generati... | 0 |
Researchers have studied extensively the problems related to verbs (e.g., Dorr et al., 2002; Lapata and Brew, 2004) and phrases-based translations (e.g., Chuang et al., 2005; Koehn et al., 2003) . Some techniques were developed for text of special domains (Seneff et al., 2006) . The techniques are applicable in many re... | 0 |
Motivation. A KB-QA system takes a natural language utterance as input and produces one or more crisp answers as output (Bast and Haussmann, 2015; Berant et al., 2013; Reddy et al., 2014; Yih et al., 2015) . This is usually done through semantic parsing: translating the utterance to a formal query in a language such as... | 0 |
In Information Extraction (IE), an event mention is represented via an anchor/trigger word that evokes an event in the input sentence. We study the problem of Event Factuality Prediction (EFP) that aims to identify the degrees of uncertainty/factuality for event mentions in text. Among others, EFP finds its application... | 0 |
Research in NLP has seen an increasing attention to narrative understanding over the past decade. Indeed, the NLP community is not alone. From studies in economics (Shiller, 2020) to climate science (Bushell et al., 2017) to political polarization (Kubin et al., 2021) to mental health (Adler et al., 2016) , there is a ... | 0 |
In Chinese texts, words composed of single or multiple characters are not separated by white space, which is different from most of the European languages.In statistical machine translation, the conventional way is to segment the Chinese character sequence into Chinese words before the training and translation.We compa... | 0 |
Pre-trained language models have demonstrated superior performance in various natural language processing tasks, such as natural language understanding (Devlin et al., 2019; He et al., 2021b) and natural language generation (Radford et al., 2019; Brown et al., 2020) . These models can contain billions of parameters, e.... | 0 |
The remarkable capability of quickly learning new concepts from a few training samples is one of the advantages of the human learning system over the current machine learning system. Motivated by this gap, research in few-shot learning has received increasing attention in the past decade. Meta-learning (Vinyals et al.,... | 0 |
Grapheme-to-phoneme (G2P) conversion is the problem of converting a string of letters into a string of phonetic symbols. Closely related to G2P are other string transduction problems in natural language processing (NLP) such as transliteration , lemmatization (Dreyer et al., 2008) , and spelling error correction (Brill... | 0 |
It has long been a dream to communicate with a computer as one might with another human being using natural language speech and text. Nowadays, we are coming closer to this dream, as natural language interfaces become increasingly popular. Our phones are already reasonably good at recognizing speech, and personal assis... | 0 |
Compound words (समास) are abundant in Sanskrit. These words are formed by joining two or more nominal words together and it is even possible to have a sequence of more than 10 words in a compound word (En.wikipedia.org, 2015) . Computational analysis of a compound word is hard because of its productive nature, unexpres... | 0 |
Building robust machine translation (MT) models that can perform well on a test set outside the domain of training examples is highly desired in real-world scenarios. Despite recent great progress in neural machine translation (NMT) research, NMT models have been found sensitive to distribution shift and adversarial ex... | 0 |
Due to the rising adoption of smartphones over the past decade, the number of services with full or partial information about people's spatial mobility has skyrocketed. Inspired by the natural language processing (NLP) literature, we investigate various properties of location embeddings. We explore whether valuable inf... | 0 |
Recently, Neural Machine Translation (NMT) (Kalchbrenner and Blunsom, 2013; Sutskever et al., 2014) has been growing in popularity due to its capacity to model the translation process end-to-end within a single probabilistic model, and its potential for higher performance compared to existing phrase-based statistical m... | 0 |
Over the past several years sentiment analysis has been increasingly important in political science (Ceron et al., 2015) and journalism (Jiang et al., 2017) . Such applications necessitate resources for languages spoken in the conflict zones. Our study focuses on Russian, which to date has little annotated data (Loukac... | 0 |
In this paper, we present the DCU multi-engine MT system MATREX (Machine Translation using Examples). This system exploits example-based MT, statistical MT (SMT), and system combination techniques.We participated in the English-Spanish (enes) and English-Czech (en-cs) translation tasks. For these two tasks, we employ s... | 0 |
Natural Language Inference (NLI), i.e. the task of determining whether an NL hypothesis can be inferred from an NL premise, has been an active research theme in computational semantics in which various approaches have been proposed (see, for example (MacCartney, 2009) and some of the references therein). In this paper,... | 0 |
Neural machine translation (NMT) brings significant gains to the field of machine translation. However, it is known to be very sensitive to the quality of parallel data (Khayrallah and Koehn, 2018) . This becomes a serious problem when using large but very noisy corpora for training. There is a lot of work on filtering... | 0 |
Modern spoken dialogue systems use multiple lexical utterance classification modules that can detect dialogue act (Stolcke et al., 2000; Khanpour et al., 2016) , intent (Tur et al., 2011) , domain (Xu and Sarikaya, 2014) , question type (Wu et al., 2005) , etc. to properly understand natural languages. The modules are ... | 0 |
In many natural language processing tasks, we will require a down-stream system to consume the input of an up-stream system, such as word segmenters, part of speech taggers, or automatic speech recognizers. Among these, one of the most prototypical and widely used examples is speech translation, where a down-stream tra... | 0 |
Reading comprehension (RC) tasks are important to measure machines' capabilities of reading and understanding. Given a question and context, a typical extractive RC task aims to automatically extract an appropriate answer from the given context.A large number of datasets for RC tasks, which contains various types of co... | 0 |
Recently, automated grammar checking for learners of English as a foreign language has attracted more attention. For example, Helping Our Own (HOO) is a series of shared tasks in correcting textual errors (Dale and Kilgarriff, 2011; Dale et al., 2012) . The shared tasks at CoNLL 2013 and CoNLL 2014 focused on grammatic... | 0 |
With the dramatic increase in the amount of textual information available in digital archives and the WWW, there has been growing interest in techniques for automatically extracting information from text. Information Extraction (IE) is such a technology that IE systems are expected to identify relevant information (usu... | 0 |
Coreference resolution is a key task in natural language processing (Jurafsky and Martin, 2008) aiming to detect the referential expressions (mentions) in a text that point to the same entity. Roughly over the past two decades, research in coreference (for the English language) had been plagued by individually crafted ... | 0 |
Visual grounding (VG), which aims to locate the object according to a structured language query, is a crucial task in natural language processing (NLP), computer vision (CV), and robotics. Recent VG studies most focus on web-crawled images such as (Kazemzadeh et al., 2014; Krishna et al., 2017; Mao et al., 2016; Yu et ... | 0 |
The transformer model (Vaswani et al., 2017) performs well for a variety of tasks, including neural machine translation (Dong et al., 2018; Junczys-Dowmunt, 2018 ). However, like many neural networks, it is overparametrised, and inference is costly. Attention heads are the headline feature of the transformer model, ess... | 0 |
The task of Machine Translation aims to obtain valid translation of text of one language to another. Data driven MT system uses parallel sentences (i.e, x th sentences in two languages show same meaning). For the data driven system to learn translation, it requires sufficient amount of parallel text (bi-text) (Turchi e... | 0 |
This paper describes an implemented system for the detection of nonreferential it in spoken multiparty dialog. The system has been developed on the basis of meeting transcriptions from the ICSI Meeting Corpus (Janin et al., 2003) , and it is intended as a preprocessing component for a coreference resolution system in t... | 0 |
An authentication question (also known as a security question), such as "What is your mother's maiden name?" is widely used for verifying user identity for many online accounts -such as email, banking, e-commerce and social networking. However, past numerous breaches on security questions identify the weakness of the c... | 0 |
Taxonomic relations that are read off of structured ontological knowledge bases have been shown to play important roles in many computational linguistics tasks, such as document clustering (Hotho et al., 2003) , navigating text databases (Chakrabarti et al., 1997) , Question Answering (QA) (Saxena et al., 2007) and sum... | 0 |
Neural machine translation (NMT), as a new technology emerged from the field of deep learning, has improved the quality of automated machine translation into a significantly higher level compared to statistical machine translation (SMT) (Wu et al., 2016; Sennrich et al., 2017; Klein et al., 2017) . State-of-the-art NMT... | 0 |
One of the important goals in AI is to develop a robot/agent that can understand instructions from humans and perform actions in complex environments. In order to do so, such a robot is required to perceive the surrounding scene, understand our spoken language, and act in a real-world 1 Our code, data, and models publi... | 0 |
Today's statistical machine translation systems rely on high quality phrase translation pairs to acquire state-of-the-art performance, see Zens and Ney, 2004; Och and Ney, 2003) . Here, phrase pairs, or "blocks" are obtained automatically from parallel sentence pairs via the underlying word alignments. Word alignments ... | 0 |
A defining property of multiword expressions (MWEs) is that they are idiosyncratic (Sag et al., 2002) . For Statistical Machine Translation (SMT), MWEs whose meaning is non-compositional, i.e. which cannot be translated word by word, can cause two major problems. The obvious problem is that MWEs may be translated incor... | 0 |
Emotion recognition is a research area in natural language processing concerned with associating words, phrases or documents with predefined emotions from psychological models. Discrete emotion recognition assigns categorial emotions (Ekman, 1999; Plutchik, 2001) , namely Anger, Anticipation, Disgust, Fear, Joy, Sadnes... | 0 |
Word Sense Disambiguation (WSD) is a NLP task aiming at identifying the sense of a word occurrence from its context, given a predefined sense inventory. Although the task emerged almost 70 years ago with the first work on Automatic Machine Translation (Weaver, 1955) , it remains unresolved. The recent breakthrough in n... | 0 |
Current spoken dialogue systems often produce prescripted system utterances or use templates with variable substitution during language generation. If a dialogue system uses grammar-based generation at all, it produces complete utterances that are then synthesised and realised in one big chunk. As systems become increa... | 0 |
The process of human communication is based on certain beliefs shared by those who are communicating. Language is one such mutual belief, and it is used to convey meaning based on its relevance to other mutual beliefs (Sperber and Wilson, 1995) . These mutual beliefs are formed through interaction with the environment ... | 0 |
Entity coreference resolution, the task of recognizing mentions in text that refer to the same realworld entity, has been one of the central tasks of natural language processing (NLP) for decades (Grosz et al., 1983; Connolly et al., 1997; Ponzetto and Strube, 2006) . Coreference resolution owes this attention to numer... | 0 |
Citations are a valuable resource for characterizing scientific publications and their links to each other. They have been exploited for a number of natural language processing (NLP) and information retrieval (IR) applications, including summarization (Qazvinian and Radev, 2008; Qazvinian et al., 2010) [CJPF] 1 , impro... | 0 |
Basing a parser on Government-Binding theory has led to a design that is quite different from traditional algorithms. 1 The parser presented here operates in two stages, lexical and syntactic. Each stage is carried out by the same parsing engine. The lexical parser projects each constituent lexical item (morpheme) acco... | 0 |
In recent development of dependency parsers, learning representations is gaining in importance. From observed features (words, positions, POS tags) to latent parsing states, building expressive representations is shown to be crucial for getting accurate and robust parsing performances.Here we focus on graph-based depen... | 0 |
Automatically learning gazetteers with minimal supervision is a long standing problem in named entity recognition.We propose EAGER as a novel approach to extending automatically gazetteers for entity recognition, utilizing DBPEDIA (Bizer et al., 2009) rather than WIKIPEDIA. DBPEDIA serves as a much better foundation th... | 0 |
A probabilistic grammar associates a probability P (T |U ) with every parse-tree T and sentence U . These conditional probabilities enable the selection of a single preferred parse-tree for the input sentence. The majority of existing work is based on the paradigm of selecting a parse T * that has the Maximum A Posteri... | 0 |
When a major societal event is mentioned in the news (e.g., civil unrest, terrorism, natural disaster), it is important to understand whether the event has already happened (PAST), is currently happening (ON-GOING), or may happen in the future (FUTURE). We introduce a new task and corpus for studying the temporal/aspec... | 0 |
In the past years, the analysis of affective language has become one of the most productive and vivid areas in computational linguistics. In the early days, the prediction of the semantic polarity (positiveness or negativeness) was in the center of interest, but in the meantime, research activities shifted towards a mo... | 0 |
It is estimated that more than two million foreigners are now living in Japan and roughly a half million of those do not have enough Japanese fluency. Since only Japanese is used in ordinary Japanese society, it has been a problem in Japan in terms of information accessibility to such foreigners.One solution for this i... | 0 |
In Phrase-Based Models of Machine Translation all phrases consistent with the word alignment are extracted (Koehn et al., 2003) , with shorter phrases needed for high coverage of unseen data and longer phrases providing improved fluency in target language translations. Hierarchical Models (Chiang, 2007; Chiang, 2005 ) ... | 0 |
Le travail que nous présentons s'inscrit dans la perspective de l'analyse temporelle des textes, qui comprend généralement les étapes suivantes :-reconnaissance et caractérisation des expressions temporelles -résolution référentielle et normalisation des expressions temporelles -reconnaissance des événements (avec temp... | 0 |
The currently dominant text generation paradigm is based on generating a sequence of discrete tokens in a left-to-right autoregressive way. Most neural language models (LMs) fall into this autoregressive generation category. Some neural architectures are sequential in nature, such as those based on recurrent neural net... | 0 |
Phrase-based SMT has achieved better performance than word-based SMT. One of the reasons is that continuous phrases, rather than single words, are used as translation units so that useful context information can be captured for selecting appropriate translations. Even so, when translating sentences containing ambiguous... | 0 |
An increasing number of linked datasets is published on the Web. At present, there have been more than 200 datasets in the LOD cloud. Among these datasets, DBpedia (Bizer, C. et al., 2009 ) and 1 http://zhishi.me/ Yago (Suchanek, F.M. et al., 2007) serve as hubs in LOD cloud. As the first effort of Chinese LOD, Zhishi.... | 0 |
Machine translation greatly facilitates communication between people of different language, and the current neural machine translation model has achieved great success in machine translation field. However, for some occasions that have higher requirements for translation speed, such as in simultaneous interpretation dy... | 0 |
There are wide applications for Information Extraction in general (Jin et al., 2018) and Relation Extraction (RE) in particular, one reason why relation extraction continues to be an active area of research (Bach and Badaskar, 2007; Kambhatla, 2004; Kumar, 2017) . Traditionally, a standard RE model would start with ent... | 0 |
Automatically transforming a large corpus into a structured knowledge base (KB) has long been a goal of information extraction (IE) research. KB population incorporates many IE tasks including named entity recognition, entity linking and relation extraction, each of which rely on deeper linguistic analysis, e.g., synta... | 0 |
An enormous amount of information exists in natural language texts only but to analyse and process this information automatically, it has to be first distilled into a more structured form. Information Extraction (1E) systems extract pieces of information by mapping natural language texts into predefined structured repr... | 0 |
Natural language generation is widely used in variety of Natural Language Processing (NLP) applications. These include paraphrasing, question answering systems, and Machine Translation (MT). To improve the quality of generated sentences, arranging effective evaluation criteria is critical (Callison-Burch et al., 2007) ... | 0 |
Semantic Textual Similarity (STS) refers to the Natural Language Processing (NLP) task which is aimed at measuring the degree of similarity/dissimilarity between two text units (Agirre et al., 2012 (Agirre et al., , 2016 . In other words given a pair of text snippets (generally a pair of sentences) the task is to deter... | 0 |
We have, in the past, reported our work in speaker adaptation for large vocabulary continuous speech recognition using a probabilistic spectral mapping [5] . In that work we transformed well-trained phonetic hidden Markov models of a single reference speaker so that they were appropriate for a new (target) speaker. Thi... | 0 |
Ever since conceptual metaphor theory was laid out in Lakoff and Johnson (1980) , the most vexing question has remained a methodological one: how can conceptual metaphors be reliably identified in language use? Although manual identification was put on a stronger methodological footing with the Metaphor Identification ... | 0 |
We developed a baseline system called Automatic Extraction of Financial Data from Text (AEFDT) that tags and extracts financial concepts based on the natural language text from a financial document such as 10Q, 10K and analyst's reports.Such financial entities (FNEs -Financial Named Entities, numerical entities, and se... | 0 |
In bilingual lexicon extraction, the context-based approach introduced by Rapp (1995) is widely used (Fung, 1995; Diab and Finch, 2000; among others) . The focus has been on learning from comparable corpora since the late 1990s (Rapp, 1999; Koehn and Knight, 2002 ; among others). However, so far, the accuracy of biling... | 0 |
Speech disfluencies (also known as speech repairs) occur frequently in spontaneous speech and can pose difficulties to natural language processing (NLP) since most NLP tools (e.g. parsers, part-of-speech taggers, information extraction modules) are traditionally trained on written language. Speech disfluencies can be d... | 0 |
During the last two decades, work on subjective language processing has been very common in literature. The work on sentiment analysis was a major theme that was pursued during this time. Sentiment Analysis is a process, according to (Liu, 2012) , in which we extract out and examine the emotional stance in a particular... | 0 |
The NLP Group of the Idiap Research Institute participated in both sub-tasks of the DiscoMT 2015 Shared Task: pronoun-focused translation and pronoun prediction (Hardmeier et al., 2015) . The first task aimed at evaluating the quality of pronoun translation in the output of a full-fledged machine translation (MT) syste... | 0 |
In the semantic dependency parsing (SDP) task of SemEval 2014, the meaning of a sentence is represented in terms of binary head-argument relations between the lexical units -bi-lexical dependencies (Oepen et al., 2014) . Since words can be semantic dependents of multiple other words, this framework results in graph rep... | 0 |
While most of the contemporary medical documents are created in electronic form, many of the older patient files are kept in paper version only. These files represent an invaluable source of information and experience for medical investigations, especially in domains with low-frequency diseases such as foetopathology, ... | 0 |
Recent advancements of deep learning models with electronic health records (EHR) have shown a great deal of success in many clinical applications (Shickel et al., 2017) , such as disease detection (Choi et al., 2016b) , diagnostics (Choi et al., 2017) , risk predictions (Futoma et al., 2015) and patient subtyping (Che ... | 0 |
Time-Event Relation Extraction (TERE) is the task of linking event mentions and relation mentions to occurrences of "time stamps" in text. We define it as follows: given a set of textual expressions denoting events and relations, and a set of time expressions in the same text document, find all instances of temporal re... | 0 |
Computational linguistics research in hedging, use of linguistic expressions whose contribution to sentence meaning is a modulation of the accuracy of the content they embed, and speculation detection has been done intensively in the domain of scholarly texts. The interest created in this domain has expanded to some ex... | 0 |
Many dialogue system developers use data gathered from previous versions of a system to train models for analyzing users' interactions with later versions of the system in new ways, e.g. detecting users' affect enables the system to respond more appropriately. However, this training data does not always accurately refl... | 0 |
Reading comprehension (RC) is a high-level task in natural language understanding that requires reading a document and answering questions about its content. RC has attracted substantial attention over the last few years with the advent of large annotated datasets (Hermann et al., 2015; Rajpurkar et al., 2016; Trischle... | 0 |
Adversarial examples are inputs to a predictive machine learning model that are maliciously designed to cause poor performance (Goodfellow et al., 2015) . Adversarial examples expose regions of the input space where the model performs poorly, which can aid in understanding and improving the model. By using these exampl... | 0 |
The paper provides a preliminary investigation of how the improvements in natural language parsing techniques can be used for modelling brain activity during online sentence comprehension. Specifically, we focus on the RNNG generative parsing framework (Dyer et al., 2016) , that allows us to extract information-theoret... | 0 |
Named Entity Recognition (NER) is the task of locating and classifying entities mentioned in unstructured text into predefined categories such as names of people, organizations and locations. It is a crucial component of many applications, such as web search, relation extraction (Yu et al., 2019) and e-commerce attribu... | 0 |
Semantic Role Labeling (SRL) aims at determining 'who' did 'what' to 'whom' in sentences by identifying and associating predicates with their semantic arguments. This information is useful for many downstream applications, for example for question answering (Shen, 2007) . The PropBank corpus (PB) (Palmer et al., 2005 )... | 0 |
The main focus of this paper is the generation of realistic samples with a similar quality to those in a training set of medical texts. At the same time, an informative, continuous representation is created from the textual input.Obtaining a good representation for medical texts may prove vital to building more sophist... | 0 |
Current state-of-the art syntactic parsers have achieved accuracies in the range of 90% F1 on the Penn Treebank, but a range of errors remain. From a dependency viewpoint, structural errors can be cast as incorrect attachments, even for constituent (phrase-structure) parsers. For example, in the Berkeley parser (Petrov... | 0 |
Questions are asked in many situations, such as conference sessions and email communications. However, questions can sometimes be lengthy and hard to understand, because they often contain peripheral information in addition to the main focus of the question. To address this issue, we propose the task of question summar... | 0 |
Software Development Life Cycle (SDLC) processes generate large and complex natural language text documents, which provide a rich playground for NLP tecnhiques. In particular, NLP techniques have been extensively applied to analyze requirements specifications for early detection of problems such as ambiguity and incomp... | 0 |
The state-of-the-art information retrieval (IR) systems of today rely on keyword matching, which suffers from the term mismatch problem. To this end, various techniques such as pseudorelevance feedback and knowledge-based query expansion have been developed. More recently, a number of semantic analysis approaches such ... | 0 |
Event extraction (EE) is a long-standing crucial information extraction task, which aims at extracting event structures from unstructured text. As illustrated in Figure 1 , it contains event detection task to identify event triggers (the word "attack") and classify event types (Attack), as well as event argument extrac... | 0 |
Many user information needs would be better answered by presenting a ranked list of entities directly, instead of just a list of relevant documents. Based on this assumption, increasing attention has been devoted to related entity finding tasks that aimed at finding documents representing entities of a correct type tha... | 0 |
As an information provider, Thomson West stores vast quantities of documents that are served up in response to user queries. Determining the relationships between entities of interest in these documents can be a complex and time consuming part of enduser research. Nor is this sort of information always explicitly prese... | 0 |
Pretrained language models (LMs), such as BERT (Devlin et al., 2018) , RoBERTa (Liu et al., 2019) and GPT-3 (Brown et al., 2020) , capture contextualized information effectively and are used in a wide variety of natural language processing (NLP) tasks. They have revolutionized NLP research. The main mechanism of these ... | 0 |
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