This survey covers fifteen years of research in the Named Entity Recognition and Classification (NERC) field, from 1991 to 2006. We report observations about languages, named entity types, domains and textual genres studied in the literature. From the start, NERC systems have been developed using hand-made rules, but now machine learning techniques are widely used. These techniques are surveyed along with other critical aspects of NERC such as features and evaluation methods. Features are word-level, dictionary-level and corpus-level representations of words in a document. Evaluation techniques, ranging from intuitive exact match to very complex matching techniques with adjustable cost of errors, are an indisputable key to progress.

A survey of named entity recognition and classification

In this paper we provide a description of TimeML, a rich specification language for event and temporal expressions in natural language text, developed in the context of the AQUAINT program on Question Answering Systems. Unlik em ost previous work on event annotation, TimeML capture st hree distinct phenomena in temporal markup: (1) it systematically anchors event predicates to a broad range of temporally denotating expressions; (2) it orders event expressions in text relative to one another, both intrasententially and in discourse; and (3) it allows for a delayed (underspecified) interpretation of partially determined temporal expressions. We demonstrate the expressiveness of TimeML for a broad range of syntactic and semantic contexts, including aspectual predication, modal subordination, and an initial treatment of lexical and constructional causation in text.

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TimeML: Robust Specification of Event and Temporal Expressions in Text

Tweets are the most up-to-date and inclusive stream of in- formation and commentary on current events, but they are also fragmented and noisy, motivating the need for systems that can extract, aggregate and categorize important events. Previous work on extracting structured representations of events has focused largely on newswire text; Twitter's unique characteristics present new challenges and opportunities for open-domain event extraction. This paper describes TwiCal-- the first open-domain event-extraction and categorization system for Twitter. We demonstrate that accurately extracting an open-domain calendar of significant events from Twitter is indeed feasible. In addition, we present a novel approach for discovering important event categories and classifying extracted events based on latent variable models. By leveraging large volumes of unlabeled data, our approach achieves a 14% increase in maximum F1 over a supervised baseline. A continuously updating demonstration of our system can be viewed at http://statuscalendar.com; Our NLP tools are available at http://github.com/aritter/ twitter_nlp.

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Open domain event extraction from twitter

We describe SUTIME, a temporal tagger for recognizing and normalizing temporal expressions in English text. SUTIME is available as part of the Stanford CoreNLP pipeline and can be used to annotate documents with temporal information. It is a deterministic rule-based system designed for extensibility. Testing on the TempEval-2 evaluation corpus shows that this system outperforms state-of-the-art techniques.

/pdf/sutime-a-library-for-recognizing-and-normalizing-time-53w0y4a0ke.pdf

SUTime: A library for recognizing and normalizing time expressions

This paper reviews research on automatic summarising in the last decade. This work has grown, stimulated by technology and by evaluation programmes. The paper uses several frameworks to organise the review, for summarising itself, for the factors affecting summarising, for systems, and for evaluation.

The review examines the evaluation strategies applied to summarising, the issues they raise, and the major programmes. It considers the input, purpose and output factors investigated in recent summarising research, and discusses the classes of strategy, extractive and non-extractive, that have been explored, illustrating the range of systems built.

The conclusions drawn are that automatic summarisation has made valuable progress, with useful applications, better evaluation, and more task understanding. But summarising systems are still poorly motivated in relation to the factors affecting them, and evaluation needs taking much further to engage with the purposes summaries are intended to serve and the contexts in which they are used.

/pdf/automatic-summarising-the-state-of-the-art-2gd3hob6kg.pdf

Automatic summarising: The state of the art

We introduce an annotation scheme for temporal expressions, and describe a method for resolving temporal expressions in print and broadcast news. The system, which is based on both hand-crafted and machine-learnt rules, achieves an 83.2% accuracy (F-measure) against hand-annotated data. Some initial steps towards tagging event chronologies are also described.

/pdf/robust-temporal-processing-of-news-56y45xq1xd.pdf

Robust temporal processing of news

The views, opinions, and/or findings contained in this report are those of the MITRE Corporation and should not be construed as an official Government position, policy, or decision, unless designated by other documentation.

/pdf/2003-standard-for-the-annotation-of-temporal-expressions-16h0jr87m5.pdf

2003 Standard for the Annotation of Temporal Expressions

/pdf/2005-standard-for-the-annotation-of-temporal-expressions-3acmzr4pt2.pdf

2005 Standard for the Annotation of Temporal Expressions

This paper introduces a set of guidelines for annotating time expressions with a canonicalized representation of the times they refer to, and describes methods for extracting such time expressions from multiple languages.

/pdf/a-multilingual-approach-to-annotating-and-extracting-5f2gvppw9t.pdf

George Wilson

Papers

Robust temporal processing of news

2003 Standard for the Annotation of Temporal Expressions

2005 Standard for the Annotation of Temporal Expressions

A multilingual approach to annotating and extracting temporal information

A Multilingual Approach to Annotating and Extracting Temporal Information.