Index of papers in Proc. ACL 2011 that mention
  • named entity
LIU, Xiaohua and ZHANG, Shaodian and WEI, Furu and ZHOU, Ming
Abstract
The challenges of Named Entities Recognition (NER) for tweets lie in the insufficient information in a tweet and the unavailability of training data.
Experiments
We use the Twigg SDK 7 to crawl all tweets from April 20th 2010 to April 25th 2010, then drop non-English tweets and get about 11,371,389, from which 15,800 tweets are randomly sampled, and are then labeled by two independent annotators, so that the beginning and the end of each named entity are marked with <TYPE> and </TYPE>, respectively.
Introduction
Named Entities Recognition (NER) is generally understood as the task of identifying mentions of rigid designators from text belonging to named-entity types such as persons, organizations and locations (Nadeau and Sekine, 2007).
Related Work
(2010) use Amazons Mechanical Turk service 2 and CrowdFlower 3 to annotate named entities in tweets and train a CRF model to evaluate the effectiveness of human labeling.
Related Work
In contrast, our work aims to build a system that can automatically identify named entities in tweets.
Task Definition
Twitter users are interested in named entities , such
Task Definition
Figure 1: Portion of different types of named entities in tweets.
Task Definition
as person names, organization names and product names, as evidenced by the abundant named entities in tweets.
named entity is mentioned in 15 sentences in this paper.
Topics mentioned in this paper:
Chambers, Nathanael and Jurafsky, Dan
Information Extraction: Slot Filling
Finally, we filter extractions whose WordNet or named entity label does not match the learned slot’s type (e.g., a Location does not match a Person).
Learning Templates from Raw Text
We also tag the corpus with an NER system and allow patterns to include named entity types, e.g., ‘kidnaszERSON’.
Previous Work
Classifiers rely on the labeled examples’ surrounding context for features such as nearby tokens, document position, syntax, named entities , semantic classes, and discourse relations (Maslennikov and Chua, 2007).
Previous Work
(2006) integrate named entities into pattern learning (PERSON won) to approximate unknown semantic roles.
Previous Work
However, the limitations to their approach are that (l) redundant documents about specific events are required, (2) relations are binary, and (3) only slots with named entities are learned.
named entity is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Rüd, Stefan and Ciaramita, Massimiliano and Müller, Jens and Schütze, Hinrich
Abstract
We use search engine results to address a particularly difficult cross-domain language processing task, the adaptation of named entity recognition (NER) from news text to web queries.
Experimental data
As out-of-domain newswire evaluation data3 we use the development test data from the NIST 1999 IEER named entity corpus, a dataset of 50,000 tokens of New York Times (NYT) and Associated Press Weekly news.4 This corpus is annotated with person, location, organization, cardinal, duration, measure, and date labels.
Introduction
For example, a named entity (NE) recognizer trained on news text may tag the NE London in an out-of-domain web query like London Klondike gold rush as a location.
Piggyback features
The value of the feature URL-MI is the average difference between the MI of PER and the other named entities .
Piggyback features
The feature LEX-MI interprets words occurring before or after so as indicators of named entitihood .
named entity is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Bendersky, Michael and Croft, W. Bruce and Smith, David A.
Introduction
Automatic markup of textual documents with linguistic annotations such as part-of-speech tags, sentence constituents, named entities , or semantic roles is a common practice in natural language processing (NLP).
Joint Query Annotation
Many query annotations that are useful for IR can be represented using this simple form, including capitalization, POS tagging, phrase chunking, named entity recognition, and stopword indicators, to name just a few.
Related Work
These approaches have been shown to be successful for tasks such as parsing and named entity recognition in newswire data (Finkel and Manning, 2009) or semantic role labeling in the Penn Treebank and Brown corpus (Toutanova et al., 2008).
named entity is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Chan, Yee Seng and Roth, Dan
Mention Extraction System
NE tags We automatically annotate the sentences with named entity (NE) tags using the named entity tagger of (Ratinov and Roth, 2009).
Mention Extraction System
From a sentence, we gather the following as candidate mentions: all nouns and possessive pronouns, all named entities annotated by the the NE tagger (Ratinov and Roth, 2009), all base noun phrase (NP) chunks, all chunks satisfying the pattern: NP (PP NP)+, all NP constituents in the syntactic parse tree, and from each of these constituents, all substrings consisting of two or more words, provided the sub-strings do not start nor end on punctuation marks.
Syntactico-Semantic Structures
lw: last word in the mention; Bc(w): the brown cluster bit string representing w; NE: named entity
named entity is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Park, Souneil and Lee, Kyung Soon and Song, Junehwa
Disputant relation-based method
A named entity combined with a topic particle or a subject particle is identified as the subject of these quotes.
Disputant relation-based method
We detect the name of an organization, person, or country using the Korean Named Entity Recognizer (Lee et al.
Introduction
We observe that the method achieves acceptable performance for practical use with basic language resources and tools, i.e., Named Entity Recognizer (Lee et al.
named entity is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: