Index of papers in Proc. ACL that mention
  • entity types
Jiang, Jing
A multitask transfer learning solution
Entity type features: We hypothesize that the entity types and subtypes of the relation arguments are also more likely to be associated with specific relation types.
A multitask transfer learning solution
We refer to the set of features that contain the entity type or subtype of an argument as “arg-NE” features.
A multitask transfer learning solution
4.4 Imposing entity type constraints
Conclusions and future work
In this paper, we applied multitask transfer learning to solve a weakly-supervised relation extraction problem, leveraging both labeled instances of auxiliary relation types and human knowledge including hypotheses on feature generality and entity type constraints.
Conclusions and future work
We also leveraged additional human knowledge about the target relation type in the form of entity type constraints.
Conclusions and future work
Experiment results on the ACE 2004 data show that the multitask transfer learning method achieves the best performance when we combine human guidance with automatic general feature selection, followed by imposing the entity type constraints.
Experiments
Finally, TL-NE builds on top of TL-comb and uses the entity type constraints to refine the predictions.
Introduction
ditional human knowledge about the entity type constraints on the relation arguments, which can usually be derived from the definition of a relation type.
Task definition
For example, we may be given the entity type restrictions on the two relation arguments.
Task definition
Nodes that represent the arguments are also labeled with the entity type , subtype and mention type as defined by ACE.
entity types is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Chan, Yee Seng and Roth, Dan
Experiments
ACE-2004 defines 7 coarse-grained entity types , each of which are then refined into 43 fine-
Experiments
grained entity types .
Experiments
Using the ACE data annotated with mentions and predefined entity types, we build a fine-grained mention entity typing (MET) classifier to disambiguate between 44 labels (43 fine-grained and a null label to indicate not a mention).
Introduction
We describe our mention entity typing system in Section 4 and features for the RE system in Section 5.
Mention Extraction System
These mention candidates are then fed to our mention entity typing (MET) classifier for type prediction (more details in Section 6.3).
Relation Extraction Framework
Abbreviations: Lm: predicted entity label for mention m using the mention entity typing (MET) classifier described in Section 4; PM ET: prediction probability according to the MET classifier; 75: used for thresholding.
Relation Extraction Framework
In (Roth and Yih, 2007), the authors used entity types to constrain the (first dimensional) relation types allowed among them.
Relation Extraction System
Due to space limitations, we refer the reader to our prior work (Chan and Roth, 2010) for the lexical, structural, mention-level, entity type , and dependency features.
Syntactico-Semantic Structures
Table 2: Features used in our mention entity typing (MET) system.
Syntactico-Semantic Structures
and whether they satisfy certain semantic entity type constraints.
entity types is mentioned in 13 sentences in this paper.
Topics mentioned in this paper:
Li, Qi and Ji, Heng
Algorithm 3.1 The Model
First, the algorithm enumerates all possible segments (i.e., subse-quences) of ac ending at the current token with various entity types .
Algorithm 3.1 The Model
:c-axis and y-axis represent the input sentence and entity types , respectively.
Algorithm 3.1 The Model
The rectangles denote segments with entity types , among which the shaded ones are three competing hypotheses ending at “1,400”.
Background
ACE defined 7 main entity types including Person (PER), Organization (ORG),
Features
The entity segments of 3) can be expressed as a list of triples (61, ..., em), where each segment 6, = (ui, 2),, 75,-) is a triple of start index ui, end index 21,-, and entity type 25,-.
Features
Gazetteer features Entity type of each segment based on matching a number of gazetteers including persons, countries, cities and organizations.
Features
Coreference consistency Coreferential entity mentions should be assigned the same entity type .
Introduction
This problem has been artificially broken down into several components such as entity mention boundary identification, entity type classification and relation extraction.
entity types is mentioned in 17 sentences in this paper.
Topics mentioned in this paper:
Pantel, Patrick and Lin, Thomas and Gamon, Michael
Abstract
We predict entity type distributions in Web search queries via probabilistic inference in graphical models that capture how entity-bearing queries are generated.
Abstract
We jointly model the interplay between latent user intents that govern queries and unobserved entity types , leveraging observed signals from query formulations and document clicks.
Abstract
We apply the models to resolve entity types in new queries and to assign prior type distributions over an existing knowledge base.
Introduction
There are, however, inherent problems in the entity repositories: (a) coverage: although coverage of head entity types is often reliable, the tail can be sparse; (b) noise: created by spammers, extraction
Introduction
We cast the task as performing probabilistic inference in a graphical model that captures how queries are generated, and then apply the model to contextually recognize entity types in new queries.
Introduction
We show that jointly modeling user intent and entity type significantly outperforms the current state of the art on the task of entity type resolution in queries.
Joint Model of Types and User Intents
We theorize that search queries are governed by a latent user intent, which in turn influences the entity types , the choice of query words, and the clicked hosts.
Related Work
entity type with latent user intents, and by incorporating click signals.
entity types is mentioned in 19 sentences in this paper.
Topics mentioned in this paper:
Yao, Limin and Riedel, Sebastian and McCallum, Andrew
Experiments
Local+Type This system adds entity type features to the previous system.
Experiments
This allows us to compare performance of using global features against entity type features.
Experiments
To determine entity types , we link named entities to Wikipedia pages using the Wikifier (Rati-nov et al., 2011) package and extract categories from the Wikipedia page.
Introduction
fine-grained entity types of two arguments, to handle polysemy.
Introduction
However, such fine grained entity types come at a high cost.
Introduction
It is difficult to discover a high-quality set of fine-grained entity types due to unknown criteria for developing such a set.
entity types is mentioned in 21 sentences in this paper.
Topics mentioned in this paper:
Chen, Yanping and Zheng, Qinghua and Zhang, Wei
Discussion
Except in Row 8 and Row 11, when two head nouns of entity pair were combined as semantic pair and when POS tag were combined with the entity type , the performances are decreased.
Feature Construction
head noun, entity type , subtype, CLASS, LDC-TYPE, etc.)
Feature Construction
In our experiment, the entity type , subtype and the head noun are used.
Feature Construction
All the employed features are simply classified into five categories: Entity Type and Subtype, Head Noun, Position Feature, POS Tag and Omni-word Feature.
entity types is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Takamatsu, Shingo and Sato, Issei and Nakagawa, Hiroshi
Experiments
documents 1,303,000 entity pairs 2,017,000 (matched to Freebase) 129,000 (with entity types ) 913,000 frequent patterns 3,084 relations 24
Experiments
In Experiment 1, since we needed entity types for patterns, we restricted ourselves to entities matched with Freebase, which also provides entity types for entities.
Experiments
Entity types are omitted in patterns.
Related Work
(2011) applied a rule-based method to the problem by using popular entity types and keywords for each relation.
Wrong Label Reduction
We define a pattern as the entity types of an entity pair2 as well as the sequence of words on the path of the dependency parse tree from the first entity to the second one.
Wrong Label Reduction
2If we use a standard named entity tagger, the entity types are Person, Location, and Organization.
entity types is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Ben-Ami, Zvi and Feldman, Ronen and Rosenfeld, Binyamin
Entity Relevance
The examples we are interested in are in the medical domain and deal with three main entity types : PERSON, DRUG, and DISEASE, where PERSON is restricted to known physicians.
Entity Relevance
While each of the entity types can be the target of a sentiment expression, the more interesting questions in this domain involve multiple entities, specifically, DRUG + DISEASE ("how effective is this drug for this disease?
Experiments
In the Financial corpus, COMPANIES are used as target entities and in the medical corpus, DISEASES, DRUGS and PERSONS are the entity types that are used as target entities.
Introduction
Another layer that we'd like to add concerns the interaction of different entity types during SA.
Introduction
In a typical situation, there is only one entity type which is the target for SA.
Introduction
In such cases, clearly distinguishing between the relevancy of target and non-target entities types is not essential.
Relevance Algorithms
(2010), working in the 'ignore relevance' mode, which (1) finds and labels all entities of the target type(s); (2) resolves all corefer-ences for the target entity type (s); (3) finds and labels all sentiment expressions, regardless of their relevance; and (4) provides dependency parses for all sentences in the corpus.
entity types is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Tomanek, Katrin and Hahn, Udo and Lohmann, Steffen and Ziegler, Jürgen
Experimental Design
In this task, a human annotator has to decide for each word whether or not it belongs to one of the entity types of interest.
Experimental Design
Annotation of these entity types in newspaper articles is admittedly fairly easy.
Experimental Design
Moreover, the limited number of entity types reduced the amount of participants’ training prior to the actual experiment, and positively affected the design and handling of the experimental apparatus (see below).
entity types is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Hovy, Dirk
Abstract
We investigate a largely unsupervised approach to learning interpretable, domain-specific entity types from unlabeled text.
Abstract
It assumes that any common noun in a domain can function as potential entity type , and uses those nouns as hidden variables in a HMM.
Abstract
The results suggest that it is possible to learn domain-specific entity types from unlabeled data.
Conclusion
We evaluated an approach to learning domain-specific interpretable entity types from unlabeled data.
Introduction
(2011) proposed an approach that uses co-occurrence patterns to find entity type candidates, and then learns their applicability to relation arguments by using them as latent variables in a first-order HMM.
Introduction
0 the learned entity types can be used to predict selectional restrictions with high accuracy
entity types is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Alfonseca, Enrique and Pighin, Daniele and Garrido, Guillermo
Headline generation
(1): an MST is extracted from the entity pair 61, 62 (2); nodes are heuristically added to the MST to enforce grammaticality (3); entity types are recombined to generate the final patterns (4).
Headline generation
COMBINEENTITYTYPES: Finally, a distinct pattern is generated from each possible combination of entity type assignments for the participating entities.
Headline generation
While in many cases information about entity types would be sufficient to decide about the order of the entities in the generated sentences (e. g., “[person] married in [location]” for the entity set {ea 2 “Mr.
Related work
Chambers and Jurafsky (2009) present an unsupervised method for learning narrative schemas from news, i.e., coherent sets of events that involve specific entity types (semantic roles).
Related work
Similarly to them, we move from the assumptions that 1) utterances involving the same entity types within the same document (in our case, a collection of related documents) are likely describing aspects of the same event, and 2) meaningful representations of the underlying events can be learned by clustering these utterances in a principled way.
entity types is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Nakashole, Ndapandula and Mitchell, Tom M.
Fact Candidates
Entity Types .
Fact Candidates
We look up entity types in a knowledge
Fact Candidates
In particular, we use the NELL entity typing API (Carlson et al., 2010).
entity types is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Lassalle, Emmanuel and Denis, Pascal
Experiments
Depending on the document category, we found some variations as to which hierarchy was learned in each setting, but we noticed that parameters starting with right and left gramtypes often produced quite good hierarchies: for instance right gramtype —> left gramtype —> same sentence —> right named entity type .
Hierarchizing feature spaces
, 0 entity types .
Introduction
The main question we raise is, given a set of indicators (such as grammatical types, distance between two mentions, or named entity types ), how to best partition the pool of mention pair examples in order to best discriminate coreferential pairs from non coreferential ones.
System description
We used classical features that can be found in details in (Bengston and Roth, 2008) and (Rah-man and Ng, 2011): grammatical type and subtype of mentions, string match and substring, apposition and copula, distance (number of separating mentions/sentences/words), gender/number match, synonymy/hypemym and animacy (using WordNet), family name (based on lists), named entity types , syntactic features (gold parse) and anaphoricity detection.
System description
As indicators we used: left and right grammatical types and subtypes, entity types , a boolean indicating if the mentions are in the same sentence, and a very coarse histogram of distance in terms of sentences.
entity types is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Andrews, Nicholas and Eisner, Jason and Dredze, Mark
Detailed generative story
(c) If wdk is a named entity type (PERSON, PLACE, ORG, .
Detailed generative story
One could also make more specific versions of any feature by conjoining it with the entity type t.
Detailed generative story
More generally, the probability (2) may also be conditioned on other variables such as on the languages pi and sci—this leaves room for a transliteration model when 53.6 75 p.6—and on the entity type cut.
Generative Model of Coreference
However, any topic may generate an entity type , 6. g. PERSON, which is then replaced by a specific name: when PERSON is generated, the model chooses a previous mention of any person and copies it, perhaps mutating its name.1 Alternatively, the model may manufacture
entity types is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Raghavan, Sindhu and Mooney, Raymond and Ku, Hyeonseo
Experimental Evaluation
It consists of 57 entity types and 79 relations.
Experimental Evaluation
The entity types include Agent, PhysicalThing, Event, TimeLocation, Gender, and Group, each with several subtypes.
Experimental Evaluation
LIME learned several rules that had only entity types in their bodies.
Related Work
Unlike our system and others (Carlson et al., 2010; Doppa et al., 2010; Sorower et al., 2011) that use a predefined ontology, they automatically identify a set of entity types and relations using “open IE.” They use HOLMES (Schoenmackers et al., 2008), an inference engine based on MLNs (Domingos and Lowd, 2009) (an SRL approach that combines first-order logic and Markov networks) to infer additional facts.
entity types is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Liu, Xiaohua and Zhou, Ming and Zhou, Xiangyang and Fu, Zhongyang and Wei, Furu
Experiments
Table 3 reports the NER performance of our method for each entity type, from which we see that our system consistently yields better F1 on all entity types than S B R. We also see that our system boosts the F1 for ORGANIZATION most significantly, reflecting the fact that a large number of organizations that are incorrectly labeled as PERSON by S B R, are now correctly recognized by our method.
Experiments
Table 3: F1 (%) of NER on different entity types .
Our Method
To resolve NER, we assign a label to each word in a tweet, indicating both the boundary and entity type .
Our Method
tiflfil, respectively, refer to the same entity if and only if: 1) The two mentions share the same entity type ; 2) is a substring of film” or vise versa; ands) 23%.. = 1, z' = .27. andj = .71.--- .32, if 277%” exists.
entity types is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Nguyen, Minh Luan and Tsang, Ivor W. and Chai, Kian Ming A. and Chieu, Hai Leong
Experiments
YAGO is different from ACE 2004 in two aspects: there is less overlapping of topics, entity types and relation types between domains; and it has more relation mentions with 11 mentions per pair of entities on the average.
Problem Statement
Entity Features Entity types and entity mention types are very useful for relation extraction.
Problem Statement
use a subgraph in the relation instance graph (J iang and Zhai, 2007b) that contains only the node presenting the head word of the entity A, labeled with the entity type or entity mention types, to describe a single entity attribute.
Problem Statement
The nodes that represent the argument are also labeled with the entity type , subtype and mention type.
entity types is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Durrett, Greg and Hall, David and Klein, Dan
Models
Each mention 2' has been augmented with a single property node pi E {1, ..., The unary B factors encode prior knowledge about the setting of each pi; these factors may be hard (I will not refer to a plural entity), soft (such as a distribution over named entity types output by an NER tagger), or practically uniform (e. g. the last name Smith does not specify a particular gender).
Models
Suppose that we are using named entity type as an entity-level property.
Related Work
Their system could be extended to handle property information like we do, but our system has many other advantages, such as freedom from a pre-specified list of entity types , the ability to use multiple input clusterings, and discriminative projection of clusters.
entity types is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Szpektor, Idan and Dagan, Ido and Bar-Haim, Roy and Goldberger, Jacob
Contextual Preferences Models
We identify entity types using the default Lingpipe2 Named-Entity Recognizer (NER), which recognizes the types Location, Person and Organization.
Contextual Preferences Models
A variable j has a single preferred entity type in cpv;n(t)[j], the type of its instantiation in 75.
Experimental Settings
The Contextual Preferences for h were constructed manually: the named-entity types for cpvm(h) were set by adapting the entity types given in the guidelines to the types supported by the Ling-pipe NER (described in Section 3.2).
entity types is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
LIU, Xiaohua and ZHANG, Shaodian and WEI, Furu and ZHOU, Ming
Experiments
For the overall performance, we use the average Precision, Recall and F1, where the weight of each name entity type is proportional to the number of entities of that type.
Experiments
Tables 2-5 report the results on each entity type, indicating that our method consistently yields better results on all entity types .
Our Method
Following the common practice , we adopt a sequential labeling approach to jointly resolve these subtasks, i.e., for each word in the input tweet, a label is assigned to it, indicating both the boundary and entity type .
entity types is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Dubey, Amit
Model
c has entity type 6 (person, organization, etc.)
Model
Entity Type (x, e)
Model
The predicates we use involve matching strings (checking if two mentions share a head word or if they are exactly the same string), matching argreement features (if the gender, number or person of pairs of NPs are the same; especially important for pronouns), the distance between mentions, and if mentions have the same entity type (i.e.
entity types is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Sun, Le and Han, Xianpei
Introduction
Features about the entity information of arguments, including: a) #TP]-#TP2: the concat of the major entity types of arguments; b) #STI-#ST2: the concat of the sub entity types of arguments; c) #MT] -#MT2: the concat of the mention types of arguments.
Introduction
We capture the property of a node’s content using the following features: a) MB_#Num: The number of mentions contained in the phrase; b) MB_C_#Type: A feature indicates that the phrase contains a mention with major entity type #Type; c) M W_#Num: The number of words within the phrase.
Introduction
a) #RP_Arg]Head_#Arg] Type: a feature indicates the relative position of a phrase node with argument 1’s head phrase, where #RP is the relative position (one of match, cover, within, overlap, other), and #Arg] Type is the major entity type of argument 1.
entity types is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: