Introduction | (2012) presented a system called TwiCal which extracts an open-domain calendar of significant events represented by a 4-tuple set including a named entity , event phrase, calendar date, and event type from Twitter. |
Introduction | Here, event elements include named entities such as person, company, organization, date/time, location, and the relations among them. |
Methodology | Events extracted in our proposed framework are represented as a 4-tuple (y, d, l, k), where 3/ stands for a non-location named entity , d for a date, I for a location, and k for an event-related keyword. |
Methodology | Tweets are preprocessed by time expression recognition, named entity recognition, POS tagging and stemming. |
Methodology | Named Entity Recognition. |
Abstract | First, named entities are linked to candidate Wikipedia pages by a generic annotation engine. |
Abstract | Then, the algorithm re—ranks candidate links according to mutual relations between all the named entities found in the document. |
Experiments and Results | In this task, mentions of entities found in a document collection must be linked to entities in a reference KB, or to new named entities discovered in the collection. |
Experiments and Results | We observe that the complete algorithm (co-references, named entity labels and MDP) provides the best results on PER NE links. |
Introduction | The Entity Linking (EL) task consists in linking name mentions of named entities (NEs) found in a document to their corresponding entities in a reference Knowledge Base (KB). |
Introduction | Various approaches have been proposed to solve the named entity disambiguation (NED) problem. |
Introduction | In the context of the Named Entity Recognition (NER) task, such resources can be generic and generative. |
Proposed Algorithm | 3.2 Named Entity Label Correction |
Abstract | Named Entity Disambiguation (NED) refers to the task of mapping different named entity mentions in running text to their correct interpretations in a specific knowledge base (KB). |
Introduction | Named entities (NEs) have received much attention over the last two decades (Nadeau and Sekine, 2007), mostly focused on recognizing the boundaries of textual NE mentions and classifying them as, e.g., Person, Organization or Location. |
Introduction | Named Entity Disambiguation |
Related Work | The second line of approach is collective named entity disambiguation (CNED), where all mentions of entities in the document are disambiguated jointly. |
Solution Graph | cos: The cosine similarity between the named entity textual mention and the KB entry title. |
Solution Graph | Named Entity Selection: The simplest approach is to select the highest ranked entity in the list for each mention mi according to equation 5, where R could refer to Rm or R5. |
Solution Graph | Our results show that Page-Rank in conjunction with re-ranking by initial confidence score can be used as an effective approach to collectively disambiguate named entity textual mentions in a document. |
Concept Identification | o NER: 1 if the named entity tagger marked the span as an entity, 0 otherwise. |
Concept Identification | clex also has a set of rules for generating concept fragments for named entities and time expressions. |
Concept Identification | It generates a concept fragment for any entity recognized by the named entity tagger, as well as for any word sequence matching a regular expression for a time expression. |
Notation and Overview | Each stage is a discriminatively-trained linear structured predictor with rich features that make use of part-of-speech tagging, named entity tagging, and dependency parsing. |
Training | 0 Input: X, a sentence annotated with named entities (person, organization, location, mis-ciscellaneous) from the Illinois Named Entity Tagger (Ratinov and Roth, 2009), and part-of-speech tags and basic dependencies from the Stanford Parser (Klein and Manning, 2003; de Mameffe et al., 2006). |
Training | l. ( Named Entity ) Applies to name concepts and their opn children. |
Training | (Fuzzy Named Entity ) Applies to name concepts and their opn children. |
Experiments | We assume that attribute values should be either name entities or terms following @ and #. |
Experiments | Name entities are extracted using Ritter et al.’s NER system (2011). |
Experiments | Consecutive tokens with the same named entity tag are chunked (Mintz et al., 2009). |
Model | 0 Token-level: for each token t E 6, word identity, word shape, part of speech tags, name entity tags. |
Abstract | We conduct experiments on named entity recognition data and find that our approach can provide a significant improvement over the standard model when annotation errors are present. |
Experiments | We now consider the problem of named entity recognition (NER) to evaluate how our model performs in a large-scale prediction task. |
Experiments | In traditional NER, the goal is to determine whether each word is a person, organization, location, or not a named entity (‘other’). |
Experiments | (This task does not trivially reduce to finding the capitalized words, as the model must distinguish between people and other named entities like organizations). |
Experiments | For this purpose we used the Freebase Search API (Freebase, 2013a).A11 named entities 8 in a question were sent to this API, which returned a ranked list of relevant topics. |
Experiments | 8When no named entities are detected, we fall back to noun phrases. |
Graph Features | We simply apply a named entity recognizer to find the question topic. |
Graph Features | (special case) if a qtopic node was tagged as a named entity, then replace this node with its its named entity form, e.g., bieber —> qtopic=person; |
Detailed generative story | (c) If wdk is a named entity type (PERSON, PLACE, ORG, . |
Inference by Block Gibbs Sampling | Each context word and each named entity is associated with a latent topic. |
Inference by Block Gibbs Sampling | This process treats all topics as exchangeable, including those associated with named entities . |
Evaluation | In the absence of a sizable number of linguistically interesting terms (like wicked) that are known to be geographically variable, we consider the proxy of estimating the named entities evoked by specific terms in different geographical regions. |
Evaluation | As noted above, geographic terms like city provide one such example: in Massachusetts we expect the term city to be more strongly connected to grounded named entities like Boston than to other US cities. |
Introduction | This information enables learning models of word meaning that are sensitive to such factors, allowing us to distinguish, for example, between the usage of wicked in Massachusetts from the usage of that word elsewhere, and letting us better associate geographically grounded named entities (e.g, Boston) with their hypemyms (city) in their respective regions. |
Experiments | Three kinds of features, namely, lexical, syntactic and named entity tag features, were extracted from relation mentions. |
Introduction | As we cannot tell what kinds of features are effective in advance, we have to use NLP toolkits, such as Stanford CoreNLPIO, to extract a variety of textual features, e.g., named entity tags, part-of-speech tags and lexicalized dependency paths. |
Related Work | Other literatures (Takamatsu et al., 2012; Min et al., 2013; Zhang et al., 2013; Xu et al., 2013) addressed more specific issues, like how to construct the negative class in learning or how to adopt more information, such as name entity tags, to improve the performance. |
Background | It works well for named entities . |
Related Work | Word embeddings have been successfully applied in many applications, such as in sentiment analysis (Socher et al., 2011b), paraphrase detection (Socher et al., 2011a), chunking, and named entity recognition (Turian et al., 2010; Collobert et al., 2011). |
Results and Analysis 5.1 Varying the Amount of Clusters | The red paths refer to the relations between the named entity and its hypernyms extracted using the web mining method (Fu et al., 2013). |
Abstract | Moreover, the success of joint bilingual learning may lend itself to many inherent multilingual NLP tasks such as POS tagging (Yarowsky and Ngai, 2001), name entity recognition (Yarowsky et al., 2001), sentiment analysis (Wan, 2009), and semantic role labeling (Sebastian and Lapata, 2009) etc. |
Abstract | It has been successfully applied to many NLP applications, such as POS tagging (Engelson and Dagan, 1996; Ringger et al., 2007), word sense disambiguation (Chan and Ng, 2007; Zhu and Hovy, 2007), sentiment detection (Brew et al., 2010; Li et al., 2012), syntactical parsing (Hwa, 2004; Osborne and Baldridge, 2004), and named entity recognition (Shen et al., 2004; Tomanek et al., 2007; Tomanek and Hahn, 2009) etc. |
Abstract | named entity and syntactic parse tree). |
Introduction | We present a new dataset of Twitter messages that use FactBank predicates (e.g., claim, say, insist) to scope the claims of named entity sources. |
Modeling factuality judgments | 0 Source: represented by the named entity or username in the source field (see Figure 4) Journalist: represented by their Twitter ID Claim: represented by a bag-of-words vector from the claim field (Figure 4) |
Text data | 0 Finally, we restrict consideration to tweets in which the source contains a named entity or twitter usemame. |
Data | We also map all named entities (e.g., “downtown” and “28X”) to their semantic types (resp. |
Data | We map named entities to their semantic types, apply stemming, and remove stop words.3 The corpus we use contains approximately 2, 000 dialogue sessions or 80, 000 conversation utterances. |
Latent Structure in Dialogues | 3We used regular expression to map named entities , and Porter stemmer in NLTK to stem all tokens. |
Baselines | > Basic features: the named entity and its type in the focus candidate; relative position of the focus candidate to the negative expression (before or after). |
Baselines | Along with negation focus annotation, this corpus also contains other annotations, such as POS tag, named entity , chunk, constituent tree, dependency tree, and semantic role. |
Baselines | > Named Entity Recognizer: We employ the Stanford NER5 (Finkel et al., 2005) to obtain named entities . |