Table 5 further shows results on NER, the task of recovering the sets of named entity mentions pertaining to each target field.

Generally, parent-free relations in the hierarchy correspond to generic entities, realized as entity mentions in the text.

Figure 3 demonstrates the correct mapping of named entity mentions to tuples, as well as tuple unification, for the example shown in Figure 1.

In their model, slot-filling entities are first generated, and entity mentions are then realized in text.

In addition to proper nouns (named entity mentions ) that are considered in this work, they also account for nominal and pronominal noun mentions.

Compared with the extraction of tuples of entity mention pairs, template filling is associated with a more complex target relational schema.

Another feature encodes the size of the most semantically detailed named entity that maps to a field; for example, the most detailed entity mention of type stime in Figure l is “3:30”, comprising of two attribute values, namely hour and minutes.

We have experimented with features that encode the shortest distance between named entity mentions mapping to different fields (measured in terms of separating lines or sentences), based on the hypothesis that field values typically co-appear in the same segments of the document.

We present a statistical model for canonicalizing named entity mentions into a table whose rows represent entities and whose columns are attributes (or parts of attributes).

We collected named entity mentions from two corpora: political blogs and sports news.

Due to the large size of the corpora, we uniformly sampled a subset of documents for each corpus and ran the Stanford NER tagger (Finkel et al., 2005), which tagged named entities mentions as person, location, and organization.

Table 2 summarizes statistics for both datasets of named entity mentions .

We seek an algorithm that infers a set of real-world entities from mentions in a text, mapping each entity mention token to an entity, and discovers general categories of words used in names (e.g., titles and last names).

tributes through transductive learning from named entity mentions with a small number of seeds (see

As a result, the model discovers parts of names—(Mrs., Michelle, Obama)—while simultaneously performing coreference resolution for named entity mentions .

A target could be another discussant or an entity mentioned in the discussion.

The target of opinion can also be an entity mentioned in the discussion.

As stated above, a target could be another discussant or an entity mentioned in the discussion.

The target of attitude could be another discussant or an entity mentioned in the discussion.

tity recognition and noun phrase chunking to identify the entities mentioned in the discussion.

The attitude profile of a discussant contains an entry for every other discussant and an entry for every entity mentioned in the discission.

For any entity mention , two annotators independently annotate its canonical form.

This explains the cases where our system correctly links multiple entity mentions but fails to generate canonical forms.

However, named entity normalization (NEN) for tweets, which transforms named entities mentioned in tweets to their unambiguous canonical forms, has not been well studied.

Given a set of tweets, e. g., tweets within some period or related to some query, our task is: 1) To recognize each mention of entities of predefined types for each tweet; and 2) to restore each entity mention into its unambiguous canonical form.

Given each pair of entity mentions , decide whether they denote the same entity.

The corpus is preprocessed by identifying Freebase entity mentions , using an approach similar to (Milne and Witten, 2008), and parsing it with an inductive dependency parser (Nivre, 2006).

A different family of unsupervised methods for relation extraction is unsupervised semantic parsing, which aims at clustering entity mentions and relation surface forms, thus generating a semantic representation of the texts on which inference may be used.

2009; Hoffmann et al., 2011; Wang et al., 2011), or syntactic restrictions on the sentences and the entity mentions (Wu and Weld, 2010).