Named Entities

Historically, tailoring language processing systems to specific domains and languages for which they were not originally built has required a great deal of effort. Recent advances in corpus-based manual and …...

Named entities form the major components in a document. When we catch the fundamental entities, we can understand a document to some degree. This paper employs different types of information …...

This paper presents a set of algorithms for distinguishing personal names with mul- tiple real referents in text, based on little or no supervision. The approach utilizes an unsupervised clustering …...

In this paper, the search engine Intuition is described. It allows the user to navigate through the documents retrieved with a given query. Several “browse help” functions are provided by …...

We present a new approach to relation ex- traction that requires only a handful of train- ing examples. Given a few pairs of named entities known to exhibit or not …...

Experiments carried out within evaluation initiatives for information retrieval have been building a substantial resource for further detailed research. In this study, we present a comprehensive analysis of the data …...

. The automatic extraction of biographical information from business news is a com- plex task. This approach deals with the characterization of the so called biographical relations by means of …...

Most existing information retrieval (IR) systems do not take much advantage of natural language processing (NLP) tech- niques due to the complexity and limited observed effectiveness of applying NLP to …...

AbstractIn this paper, we present a multimodal parallel text-image corpus, and propose an image annotation method that exploits the textual information associated with images. Our corpus contains news articles composed …...

We investigate how the automatic identi- fication of noun compounds and named entities can contribute to keyphrase ex- traction and we also show how previously identified noun compounds affect named …...

AbstractNamed Entities (NEs) that occur in natural language text are important especially due to the advent of social media, and they play a critical role in the development of many …...

AbstractIn this paper, we present HunOr, the first multi-domain Hungarian―Russian parallel corpus. Some of the corpus texts have been manually aligned and split into sentences, besides, named entities also have …...

AbstractThe aim of this paper is to present a system for semantic text annotation called Inforex. Inforex is a web-based system designed for managing and annotating text corpora on the …...

Document revision histories are a useful and abundant source of data for natural language processing, but selecting relevant data for the task at hand is not trivial. In this paper …...

AbstractIn the last decades, a wide range of automatic metrics that use linguistic knowledge has been developed. Some of them are based on lexical information, such as METEOR; others rely …...

AbstractSentiment analysis, or opinion mining, is the process of extracting sentiment from documents or sentences, where the expressed sentiment is typically categorized as positive, negative, or neutral. Many different techniques …...

Many current Natural Language Process- ing [NLP] techniques work well assum- ing a large context of text as input data. However they become ineffective when applied to short texts such …...

The Semantic Textual Similarity (STS) task examines semantic similarity at a sentence- level. We explored three representations of semantics (implicit or explicit): named enti- ties, semantic vectors, and structured vectorial …...

Information in visually rich formats such as PDF and HTML is often conveyed by a combination of textual and visual features. In particular, genres such as marketing flyers and info-graphics …...

In this paper, we describe our English- Hindi and Hindi-English statistical sys- tems submitted to the WMT14 shared task. The core components of our translation systems are phrase based (Hindi-English) …...

AbstractThe task of Relation Extraction from texts is one of the main challenges in the area of Information Extraction, considering the required linguistic knowledge and the sophistication of the language …...

AbstractWe describe a corpus of consumer health questions annotated with named entities. The corpus consists of 1548 de-identified questions about diseases and drugs, written in English. We defined 15 broad …...

AbstractText comparison is an interesting though hard task, with many applications in Natural Language Processing. This work introduces a new text-similarity measure, which employs named-entities’ information extracted from the texts …...

Named Entities (NEs) are a very important part of a sentence and their treatment is a potentially useful preprocessing step for Statistical Machine Translation (SMT). Improper translation of NE lapse …...

AbstractRapid progress has been made towards question answering (QA) systems that can extract answers from text. Existing neural approaches make use of expensive bi-directional attention mechanisms or score all possible …...

AbstractToday’s massive news streams demand the automate analysis which is provided by various online news explorers. However, most of them do not provide sentiment analysis. The main problem of sentiment …...

AbstractAnalogy completion via vector arithmetic has become a common means of demonstrating the compositionality of word embeddings. Previous work have shown that this strategy works more reliably for certain types …...

AbstractIn the last few years, the increasing availability of large corpora spanning several time periods has opened new opportunities for the diachronic analysis of language. This type of analysis can …...

AbstractMultiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single …...

AbstractMany Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources. While this is easy for humans, the present …...

AbstractThe paper presents the Bulgarian MARCELL corpus, part of a recently developed multilingual corpus representing the national legislation in seven European countries and the NLP pipeline that turns the web …...

AbstractUnsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches, …...

AbstractAutomatic translation systems are known to struggle with rare words. Among these, named entities (NEs) and domain-specific terms are crucial, since errors in their translation can lead to severe meaning …...

AbstractA sufficient amount of annotated data is usually required to fine-tune pre-trained language models for downstream tasks. Unfortunately, attaining labeled data can be costly, especially for multiple language varieties and …...

AbstractMost question answering tasks focuses on predicting concrete answers, e.g., named entities. These tasks can be normally achieved by understanding the contexts without additional information required. In Reading Comprehension of …...

AbstractIn dialogue analysis, characterising named entities in the domain of interest is relevant in order to understand how people are making use of them for argumentation purposes. The movie recommendation …...

AbstractThe paper describes the Bulgarian Event Corpus (BEC). The annotation scheme is based on CIDOC-CRM ontology and on the English Framenet, adjusted for our task. It includes two main layers: …...

AbstractWe identify hard problems for neural machine translation models by analyzing progressively higher-scoring translations generated by letting models cheat to various degrees. If a system cheats and still gets something …...

AbstractNamed Entity Recognition (NER) is a subtask of Natural Language Processing (NLP) that involves identifying and categorizing named entities. The result annotation makes unstructured natural texts applicable for other NLP …...