Machine Learning

S Tadaaki Oshio Japan Patent Information Organization 1....

We predict discourse segment boundaries from linguistic features of utterances, using a corpus of spoken narratives as data. We present two methods for developing seg- mentation algorithms from training data: …...

The development of natural language pro- ccssing (NLP) systems that perform ma- chine translation (MT) and information re- trieval (IR) has highlighted the need for the automatic recognition of proper …...

We present a knowledge and context-based system for parsing and translating natu- ral language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system …...

Any large language processing software relies in its operation on heuristic decisions concerning the strategy of processing. These decisions are usually "hard-wired" into the software in the form of hand- …...

Any large language processing software relies in its operation on heuristic decisions concerning the strategy of processing. These decisions are usually "hard-wired" into the software in the form of hand- …...

We apply machine learning techniques to classify automatically a set of verbs into lexical semantic classes, based on distributional approximations of diathe- ses, extracted from a very large anno- tated …...

In this paper, we report results on answering questions for the reading comprehension task, using a machine learning approach. We eval- uated our approach on the Remedia data set, a …...

We present a machine learning approach to evaluating the well- formedness of output of a machine translation system, using classifiers that learn to distinguish human reference translations from machine translations. …...

This paper presents a method that as- sists in maintaining a rule-based named-entity recognition and classifi- cation system. The underlying idea is to use a separate system, constructed with the …...

This paper applies machine learning techniques to acquiring aspects of the meaning of discourse mark- ers. Three subtasks of acquiring the meaning of a discourse marker are considered: learning its …...

Temporal relation resolution involves extraction of temporal information explicitly or implicitly embedded in a language. This information is of- ten inferred from a variety of interactive gram- matical and lexical …...

This paper presents a machine learning approach to bare sluice disambiguation in dialogue. We ex- tract a set of heuristic principles from a corpus-based sample and formulate them as probabilistic …...

We propose a novel method to predict the inter- paragraph discourse structure of text, i.e. to infer which paragraphs are related to each other and form larger segments on a …...

In the development of machine learning sys- tems for identification of reference chains, hand-annotated corpora play a crucial role. This paper concerns the question of how predicative NPs should be …...

In this presentation, I will look back at 10 years of CoNLL conferences and the state of the art of machine learning of lan- guage that is evident from this …...

This paper investigates a machine learn- ing approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning as an over- sampling …...

. The Olympics-oriented mobile game news ordering system introduced in the paper involved the technology of natural language processing, machine learning and information extraction. We collected text information from the …...

This paper proposes an other-anaphora resolution approach in bio-medical texts. It utilizes automatically mined patterns to discover the semantic relation between an anaphor and a candidate antecedent. The knowledge from …...

Code-switching is an interesting linguistic phenomenon commonly observed in highly bilingual communities. It consists of mixing languages in the same conversational event. This paper presents results on Part-of-Speech tagging Spanish-English …...

Document Planning - the task of deciding which content messages should be realised in a target document based on raw data provided by an underlying application, and how these messages …...

AbstractThis paper describes a new language resource of events and semantic roles that characterize real-world situations. Narrative schemas contain sets of related events (edit and publish), a temporal ordering of …...

AbstractThis paper presents the ADN-Classifier, an Automatic classification system of Spanish Deverbal Nominalizations aimed at identifying its semantic denotation (i.e. event, result, underspecified, or lexicalized). The classifier can be used …...

AbstractThere is by now widespread agreement that the most realistic way to construct the large-scale commonsense knowledge repositories required by natural language and artificial intelligence applications is by letting machines …...

Written contracts are a fundamental framework for commercial and coopera- tive transactions and relationships. Lim- ited research has been published on the ap- plication of machine learning and natural language …...

AbstractIn this paper, we report on the design of a part-of-speech-tagset for Wolof and on the creation of a semi-automatically annotated gold standard. In order to achieve high-quality annotation relatively …...

. Recent considerations in opinion mining, oriented by real applicative tasks, require creating lexical and semantic resources quantitatively and qualitatively rich. In this context, we present a method to automatically …...

AbstractExisting approaches to classifying documents by sentiment include machine learning with features created from n-grams and part of speech. This paper explores a different approach and examines performance of one …...

While subjectivity related research in other languages has increased, most of the work focuses on single languages. This paper explores the integration of features originating from multiple languages into a …...

AbstractWe present the problem of categorizing web services according to a shallow ontology for presentation on a specialist portal, using their WSDL and associated textual documents found by a crawler. …...

AbstractThis paper presented an overview of Chinese bi-character words’ morphological types, and proposed a set of features for machine learning approaches to predict these types based on composite characters’ information. …...

Ambiguity of entity mentions and con- cept references is a challenge to mining text beyond surface-level keywords. We describe an effective method of disambi- guating surface forms and resolving them …...

AbstractIn the past, we have succesfully used machine learning approaches for sentiment analysis. In the course of those experiments, we observed that our machine learning method, although able to cope …...

. We proposed a method of using machine learning with various features for the recognition of Japanese notational variants. We increased 0.06 at the F-measure by specific features using existing …...

Traditional computational approaches to re- ferring expression generation operate in a de- liberate manner, choosing the attributes to be included on the basis of their ability to dis- tinguish the …...

Named entity recognition (NER) has been extensively studied for the names of genes and gene products but there are few proposed solutions for phenotypes. Phenotype terms are expected to play …...

Extracting biomedical named entities is one of the major challenges in automatic processing of biomedical literature. This paper proposes a machine learning approach for finding phenotype names in text. Features …...

AbstractThe current study works at the interface of theoretical and computational linguistics to explore the semantic properties of an particle verbs, i.e., German particle verbs with the particle an. Based …...

A machine learning mechanism is learned from human annotations in order to perform preference ranking. The mechanism operates on a sentence level and ranks the alternative machine translations of each …...

This paper presents the first systematic study of the coreference resolution problem in a general inference-based discourse processing framework. Employing the mode of inference called weighted abduction, we propose a …...

This paper presents a supervised machine learning approach to incrementally learn and segment affixes using generic background knowledge. We used Prolog script to split affixes from the Amharic word for …...

Scientific publications contain many references to method terminologies used during scientific experiments. New terms are constantly created within the research community, especially in the biomedical domain where thousands of papers …...

Online content analysis employs algorithmic methods to identify entities in unstructured text. Both machine learning and knowledge-base approaches lie at the foundation of contemporary named entities extraction systems. However, the …...

. Several research efforts tackled medical entity recognition from texts. However, to our knowledge, there are no comparative studies for the following approaches: (i) the extraction of noun phrases in …...

A respelling is an alternative spelling of a word in the same writing system, intended to clarify pronunciation. We introduce the task of automatic generation of a respelling from the …...

Automatically identifying implicit discourse relations using annotated data and raw cor- pora This paper presents a system for identifying « implicit » discourse relations (that is, relations that are not …...

With 19%–28% of Internet users partici- pating in online health discussions, it be- came imperative to be able to detect and analyze posted personal health informa- tion (PHI). In this …...

Learning a hierarchy of specialized pairwise models for coreference resolution This paper proposes a new method for significantly improving the performance of pairwise coreference models. Given a set of indicators, …...

Traditional relation extraction predicts rela- tions within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, …...

. In this paper, we test two distinct approaches to chunk transcribed oral data, trying to minimize the phases of manual correction. First, we use an existing chunker, learned from …...

This work is, to our knowledge, a first attempt at a machine learning approach to cross-lingual coreference resolution, i.e. coreference resolution (CR) performed on a bitext. Focusing on CR of …...

In this paper, we present a machine learning approach for word sense alignment (WSA) which combines distances between senses in the graph representations of lexical-semantic resources with gloss similarities. In …...

. This paper presents the first experiments on French in automatic identification of implicit discourse relations (i.e. relations that lack an overt connective). Our systems exploit hand-labeled implicit examples, along …...

AbstractNative Language Identification (NLI) is a task aimed at determining the native language (L1) of learners of second language (L2) on the basis of their written texts. To date, research …...

This paper presents a machine learning approach to sarcasm detection on Twitter in two lan- guages – English and Czech. Although there has been some research in sarcasm detection in …...

Sentiment analysis has become an important classification task because a large amount of user-generated content is published over the Internet. Sentiment lexicons have been used successfully to classify the sentiment …...

Natural language processing and text analy- sis methods offer the potential of uncovering hidden associations from large amounts of un- processed texts. The SemEval-2015 Analy- sis of Clinical Text task …...

Taxonomy plays an important role in many applications by organizing domain knowl- edge into a hierarchy of is-a relations be- tween terms. Previous works on the taxo- nomic relation identification …...

Social media represents a rich source of up- to-date information about events such as in- cidents. The sheer amount of available infor- mation makes machine learning approaches a necessity for …...

This paper presents a method for cleaning and evaluating parallel corpora using word alignments and machine learning algorithms. It is based on the assumption that parallel sentences have many word …...

AbstractIn this paper we describe our work in building an online tool for manually annotating texts in any spoken language with SignWriting in any sign language. The existence of such …...

AbstractMachine learning-based methods have obtained great progress on emotion classification. However, in most previous studies, the models are learned based on a single corpus which often suffers from insufficient labeled …...

This paper tries to demonstrate our exploratory efforts in tackling with the “high accuracy-low quantity” problem of human word sense annotation task in Chinese, and ultimately reach the goal 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): …...

AbstractManual text annotation is an essential part of Big Text analytics. Although annotators work with limited parts of data sets, their results are extrapolated by automated text classification and affect …...

A significant concern in processing natural language data is the often unclear legal status of the input and output data/resources. In this paper, we investigate this problem by discussing a …...

AbstractThe recently increased focus on misinformation has stimulated research in fact checking, the task of assessing the truthfulness of a claim. Research in automating this task has been conducted in …...

AbstractIn this position paper, we argue that building operational automated scoring systems is a task that has disciplinary complexity above and beyond standard competitive shared tasks which usually involve applying …...

AbstractThe use of machine learning for NLP generally requires resources for training. Tasks performed in a low-resource language usually rely on labeled data in another, typically resource-rich, language. However, there …...

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 …...

AbstractThis paper presents an approach for prediction of results for sport events. Usually the sport forecasting approaches are based on structured data. We test the hypothesis that the sports results …...

AbstractSocial media plays a great role in news dissemination which includes good and bad news. However, studies show that news, in general, has a significant impact on our mental stature …...

AbstractNatural language has recently been explored as a new medium of supervision for training machine learning models. Here, we explore learning classification tasks using language in a conversational setting – …...

AbstractIn this paper we present the first work on the automated scoring of mindreading ability in middle childhood and early adolescence. We create MIND-CA, a new corpus of 11,311 question-answer …...

AbstractThis paper reports on a structured evaluation of feature-based Machine Learning algorithms for selecting the form of a referring expression in discourse context. Based on this evaluation, we selected seven …...

AbstractThe spread of biased news and its consumption by the readers has become a considerable issue. Researchers from multiple domains including social science and media studies have made efforts to …...

AbstractDaily scenes are complex in the real world due to occlusion, undesired lighting conditions, etc. Although humans handle those complicated environments well, they evoke challenges for machine learning systems to …...

AbstractAcademia and industry have developed machine learning and natural language processing models to detect online hate speech automatically. However, most of these existing methods adopt a supervised approach that heavily …...

AbstractHoping for a large number of clicks and potentially high social shares, journalists of various news media outlets publish sensationalist headlines on social media. These headlines lure the readers to …...

AbstractThis paper presents the Multilingual Artificial Intelligence Agent Assistant (MAIA), a project led by Unbabel with the collaboration of CMU, INESC-ID and IT Lisbon. MAIA will employ cutting-edge machine learning …...

AbstractAnalyzing the geographic movement of humans, animals, and other phenomena is a growing field of research. This research has benefited urban planning, logistics, animal migration understanding, and much more. Typically, …...

AbstractSocial media platforms such as Twitter have become a breeding ground for unverified information or rumors. These rumors can threaten people’s health, endanger the economy, and affect the stability of …...

AbstractDeep learning-based Chinese zero pronoun resolution model has achieved better performance than traditional machine learning-based model. However, the existing work related to Chinese zero pronoun resolution has not yet well …...

AbstractPlatforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts. But this process does not typically provide feedback to …...

The COVID-19 pandemic has had a significant impact on human lives globally. As a result, it is unsurprising that it has influenced hate speech and other sorts of abusive language …...

AbstractMachine learning methods for financial document analysis have been focusing mainly on the textual part. However, the numerical parts of these documents are also rich in information content. In order …...

AbstractNatural language understanding (NLU) tasks are typically defined by creating an annotated dataset in which each utterance is encountered once. Such data does not resemble real-world natural language interactions in …...

AbstractClinical data annotation has been one of the major obstacles for applying machine learning approaches in clinical NLP. Open-source tools such as NegBio and CheXpert are usually designed on data …...

AbstractSuccessful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge. Such texts …...

AbstractThis paper describes a resource of Wikipedias in 31 languages categorized into Extended Named Entity (ENE), which has 219 fine-grained NE categories. We first categorized 920 K Japanese Wikipedia pages …...

AbstractModel explanations are crucial for the transparent, safe, and trustworthy deployment of machine learning models. The SHapley Additive exPlanations (SHAP) framework is considered by many to be a gold standard …...