Source: https://patents.google.com/patent/US9594831B2/en
Timestamp: 2018-05-26 10:52:12
Document Index: 800112332

Matched Legal Cases: ['Application No. 13757813', 'Application No. 13757813', 'Application No. 13', 'Application No. 201380044316', 'Application No. 201380013249', 'Application No. 13757813', 'Application No. 201380013249', 'Application No. 201380013249', 'Application No. 201380044316']

US9594831B2 - Targeted disambiguation of named entities - Google Patents
Targeted disambiguation of named entities Download PDF
US9594831B2
US9594831B2 US13531493 US201213531493A US9594831B2 US 9594831 B2 US9594831 B2 US 9594831B2 US 13531493 US13531493 US 13531493 US 201213531493 A US201213531493 A US 201213531493A US 9594831 B2 US9594831 B2 US 9594831B2
US13531493
US20130346421A1 (en )
A targeted disambiguation system is described herein which determines true mentions of a list of named entities in a collection of documents. The list of named entities is homogenous in the sense that the entities pertain to the same subject matter domain. The system determines the true mentions by leveraging the homogeneity in the list, and, more specifically by applying a context similarity hypothesis, a co-mention hypothesis, and an interdependency hypothesis. In one implementation, the system executes its analysis using a graph-based model. The system can operate without the existence of additional information regarding the entities in the list; nevertheless, if such information is available, the system can integrate it into its analysis.
In some applications, it is desirable to identify occurrences of a named entity in a set of documents. A named entity often corresponds to a proper noun, e.g., referring to a name of a person, organization, location, product, event, etc. This task may be challenging, however, because a named entity may correspond to a string having two or more meanings (i.e., a homograph). For example, assume the goal is to identify documents which contain reference to Apple® computers. Some of the documents may use the word “apple” in the context of fruit, rather than computers.
One known way to address this problem is via a content-matching technique. This technique entails identifying the context in which a document mentions a string corresponding to the named entity in question, e.g., the word “apple.” The technique then compares this context information with a-priori reference information associated with the named entity, such as an online encyclopedia entry corresponding to Apple® computers. If there is a match between the context information and the reference information, the technique can conclude that the mention of “apple” in the document likely corresponds to Apple® computers.
This approach, however, is not fully satisfactory. One drawback is that many named entities have no counterpart reference documents that provide authoritative information regarding the named entities.
Described herein is a targeted disambiguation system for determining true mentions of a list of named entities in a collection of documents. In one implementation, the system operates by receiving a list of named entities. The named entities homogenously pertain to a same subject matter domain. The system then determines a set of candidate mentions. Each candidate mention corresponds to an occurrence of a string in a collection of documents that corresponds to a named entity in the list. The system then identifies true mentions within the set of candidate mentions. Each true mention corresponds to a valid occurrence of a named entity in the collection of documents. Generally stated, the system identifies the true mentions by leveraging the homogeneity in the list of named entities—namely, the fact that the entities pertain to the same subject matter domain.
According one illustrative feature, the system can identify the true mentions without the aid of reference documents. In this sense, the named entities, which are the targets of the analysis performed by the system, may be considered ad-hoc in nature.
According to another illustrative feature, the system can identify the true mentions by applying three hypotheses described in detail herein: (a) a context similarity hypothesis; (b) a co-occurrence hypothesis; and (c) an interdependency hypotheses.
According to another illustrative feature, the system can identify the true mentions by constructing a graph, and then generating ranking scores using the graph. The graph expresses information which represents the three hypotheses mentioned above.
According to another illustrative feature, the system can integrate the use of additional knowledge about the named entities, if available (although this is not required). The system can perform this task by adding virtual nodes to the graph; the virtual nodes correspond to reference documents associated with the named entities.
FIG. 1 shows a correlation between three named entities in a list and four documents.
FIG. 2 shows a targeted disambiguation system (TDS) which determines true mentions of named entities in a collection of documents.
FIG. 3 is a flowchart that describes, in a high-level form, one manner of operation of the TDS of FIG. 2.
FIG. 4 is a flowchart that describes, in greater detail, one manner of operation of the TDS of FIG. 2.
FIG. 5 is a sample of a graph that may be constructed by the TDS of FIG. 2.
FIG. 6 is a sample of another graph that may be constructed by the targeted disambiguation system of FIG. 2; here, the graph includes at least one virtual node associated with additional information pertaining to a named entity.
FIG. 7 is a flowchart that describes another manner of operation of the TDS of FIG. 2; in this case, the TDS takes into account additional information that is available regarding the named entities.
FIG. 8 depicts a first computer-related implementation of the functionality shown in the foregoing drawings.
FIG. 9 depicts a second computer-related implementation of the functionality shown in the foregoing drawings.
FIG. 10 shows a yet more detailed computer-related implementation of the functionality shown in the foregoing drawings.
This disclosure is organized as follows. Section A describes an overview of a targeted disambiguation system (TDS) that identifies true mentions of named entities in a collection of documents. Section B describes functionality for creating a graph used by the TDS. Section C describes functionality for computing raw context similarity scores that are used to construct the graph. Section D describes functionality for computing co-occurrence scores which are also used to construct the graph. Section E describes functionality for computing weights that are assigned to edges in the graph. Section F describes functionality for computing ranking scores based on the graph. Section G describes functionality for leveraging additional information in the task of identifying true mentions, if available. And Section H describes illustrative computing functionality for implementing any of the operations set forth in the preceding sections.
As a preliminary matter, some of the figures describe concepts in the context of one or more structural components, variously referred to as functionality, modules, features, elements, etc. The various components shown in the figures can be implemented in any manner by any physical and tangible mechanisms, for instance, by software, hardware (e.g., chip-implemented logic functionality), firmware, etc., and/or any combination thereof. In one case, the illustrated separation of various components in the figures into distinct units may reflect the use of corresponding distinct physical and tangible components in an actual implementation. Alternatively, or in addition, any single component illustrated in the figures may be implemented by plural actual physical components. Alternatively, or in addition, the depiction of any two or more separate components in the figures may reflect different functions performed by a single actual physical component. Section H, to be discussed in turn, provides additional details regarding illustrative physical implementations of the functions shown in the figures.
A targeted disambiguation system (TDS) is described herein which determines true mentions of a list of named entities within a collection of documents D. To begin with, this section sets forth the meaning of terms used in this description.
A named entity. A named entity refers to any subject matter that is a target of interest, including a person, a location, an organization, a product, an event, and so on. In many cases, a named entity refers to a proper noun, but named entities are not limited to proper nouns.
A subject matter domain. A subject matter domain pertains to a field associated with a named entity. For example, without limitation, one list of named entities may pertain to the field of computer software companies. Another list of named entities may pertain to shoes produced by a particular manufacturer. Another list of named entities may pertain to locations within a particular region of the world, and so on. The subject matter domain need not conform to an accepted classification in any classification scheme (although, in some cases, it may). In this sense, the subject matter domain may be considered ad-hoc in nature.
A document. A document refers to any textual information that conveys any meaning in any environment. In some environments, a document may refer to a text document containing one or more pages (although the document may also contain other types of media content, such as images, etc.). Alternatively, or in addition, a document may refer to a web page or a web site. Alternatively, or in addition, a document may pertain to a message of any type, such as an IM message, a Facebook message, a Twitter message, an SMS message, etc. Alternatively, or in addition, a document may refer to a record in a database, and so on.
A candidate mention. A candidate mention refers to the occurrence of a string associated with a named entity within a document. For example, a candidate mention of the computer software company “Apple” may correspond to the string “apple” within a document. A candidate mention is formally identified by a paring of a named entity ei, and a document dj that contains the string associated with the named entity. In other words, the pair is denoted as (ei, dj). A candidate mention is qualified as being a “candidate” because it may or may not be a true mention of the named entity. To facilitate description, a candidate mention may also be referred to as simply the occurrence of a named entity ei in the document di (that is, without making explicit reference to a string associated with ei).
A string. A string si refers to a series of one or more characters associated with a named entity. The string may also refer to one or more other concepts besides the named entity. A string may include one or more words.
A true mention. A true mention corresponds to a candidate mention that is a valid occurrence of a named entity in a document. For example, a document that uses the word “apple” when discussing the company Apple Inc., corresponds to a true mention of the named entity “Apple.”
A false mention. A false mention corresponds to a candidate mention that is not a valid occurrence of a named entity in a document. For example, a document that uses the word “apple” when discussing the fruit “apple” is a false mention of the named entity “Apple” (presuming that the named entity “Apple” refers to the computer software company Apple Inc.).
An occurrence. An occurrence refers to a single candidate mention of a named entity in a document. A document may contain zero, one, two, or more occurrences of any named entity. A candidate mention (ei, dj) means that the document dj contains at least one occurrence of ei, although it may actually include any number of occurrences of ei.
Context. The context refers to the circumstances in which a candidate mention appears in a document. In one implementation, a context may correspond to other words in the document, such as, without limitation, the z words preceding the candidate mention and the z words following the candidate mention.
FIG. 1 provides a simplified example of the operation of the TDS. From a high-level perspective, the TDS accepts a list of named entities pertaining to any subject matter domain. In the merely illustrative case of FIG. 1, the TDS accepts a list that includes at least three entities pertaining to computer software companies. The first named entity (e1) has the fictional company name of “Microship.” The second named entity (e2) has the fictional company name of “Grape.” The third entity has the fictional company name “GP solutions.” The purpose of the TDS is to identify true mentions of these three named entities within a corpus of documents. FIG. 1 shows merely four representative documents in the corpus of documents (d1, d2, d3, and d4).
More specifically, consider document d1. This document appears to be discussing a product produced by the company “Microship,” and therefore likely corresponds to a true mention of “Microship.” The document d2 mentions both “Microship” and “Grape” in the context of computer software, and therefore likely includes true mentions of both “Microship” and “Grape.” The document d3, by contrast, uses the word “grape” in the context fruit, and is therefore likely a false mention of the company “Grape.” Similarly, the document d4 uses the string “GP solutions” in the context of a medical environment, where “GP” likely refers to “general practitioner,” rather than the computer software company named “GP Solutions.”
The TDS can automatically generate the above conclusion by leveraging the homogeneity of the list of named entities—namely, the fact that all of the named entities belong to the same subject matter domain. More specifically, the TDS identifies true mentions by applying three hypotheses. Each of the three hypotheses pertains to a different observation which depends on the homogeneity of the list of named entities.
Context similarity. A first hypothesis posits that a context between two true mentions is more similar than between two false mentions, across two distinct named entities in the list. The four documents shown in FIG. 1 illustrate this premise. For example, the contexts of d1 and d2 share the contextual words “operating system” and “graphical user interface.” This stems from the fact that “Microship” and “Grape” both identify computer software companies, and therefore documents that are truly directed to these companies can be expected to discuss similar types of products. By contrast, d3 pertains to fruit, while d4 pertains to a medical environment. Since these concepts are dissimilar, the contextual words in d3 (“growers,” “frost,” “harvest,” etc.) can be expected to be a poor match for the contextual words in d4 (“hospital,” “patient,” etc.).
Further, the context between two true mentions can generally be expected to be more similar than between a true mention and a false mention. The example of FIG. 1 also supports this observation. For example, the contextual words in d1 and d2 are not found in either d3 and d4, and vice versa.
Note, however, that there can be similar context between false mentions associated with a single named entity. For example, there may be several false mentions for the named entity “Grape” that pertain to fruit. These false mentions may very well have similar context, e.g., by mentioning words like “vine,” “harvest,” etc. The TDS takes this issue into account in a manner set forth in greater detail below.
Co-mention. A second hypothesis posits that, if plural named entities have candidate mentions in a same document, there is an elevated likelihood that these candidate mentions correspond to true mentions. For example, document d2 mentions both “Microship” and “Grape,” strings corresponding to two named entities from the list. Based on the second hypothesis, it is therefore likely that d2 contains two true mentions.
Interdependency. A third hypothesis posits that, if a particular candidate mention has similar context with one or more true mentions, there is an elevated likelihood that the particular candidate mention is also a true mention. For example, assume that it is established that document d2 is a true mention of the named entity “Microship,” e.g., based on the co-mention hypothesis and/or based on other evidence. Further assume that it is established that document d2 has a similar context to document d1. This means that the candidate mention in document d1 is also likely to be a true mention. In other words, this hypothesis posits that true mentions will propagate their positive relevance to related candidate mentions.
In one implementation, the TDS can pick out the true mentions from the set of candidate mentions without a-priori knowledge of the subject matter domain to which the entities pertain.
Further, the TDS can establish the true mentions without relying on any additional knowledge regarding the named entities. This feature is helpful because many named entities may have no counterpart reference information. For example, consider a list that identifies fifty sneaker brands produced by a particular manufacturer. It is quite likely that reference information will not be available for at least some of the sneaker brands in the list, e.g., due to lack of widespread interest in these brands among the general public and/or any other reason. Entities that lack reference information may be regarded as ad-hoc entities. Indeed, these ad-hoc entities need not even appear in any dictionaries, directories, ontologies, knowledge bases, etc.
Nevertheless, suppose that additional knowledge exists pertaining to at least some of the named entities in the list. For example, suppose that a reference document exists which provides authoritative information regarding the first entity, Microship. The TDS can integrate this knowledge into its determination of true mentions. Section G provides additional information regarding this aspect of the TDS.
Advancing to FIG. 2, this figure shows one implementation of a targeted disambiguation system (TDS) 200. The TDS 200 is said to be “targeted” in the sense that the system attempts to find true mentions regarding an established set of named entities. This is in contrast to some systems which identify any named entities within a group of documents (e.g., by clustering), without targeting a-priori entities of interest.
The TDS 200 can include (or can be conceptualized as including) a number of modules that perform different functions. Each of these modules will be described below in turn. Later sections will provide additional information regard individual modules shown in FIG. 2.
To begin with, an input module 202 receives a list of named entities from any source, pertaining to any subject matter domain. For example, the input module 202 can receive a list of named entities that are manually input by a user. Alternatively, or in addition, the input module 202 can extract the list of named entities from a pre-existing table, database, and/or some other source (or sources). The input module 202 can then store the list of named entities in a data store 204.
A mention determination module 206 determines occurrences of strings associated with the named entities within a collection of documents. The occurrences correspond to candidate mentions because it is not yet resolved whether they are true or false mentions. The mention determination module 206 can perform its function using any search functionality, such as by relying on an inverted index provided by a search engine or a database retrieval engine to find strings within documents.
The mention determination module 206 can also optionally expand each named entity in the list to a group of equivalent terms associated with the named entity (such as synonyms). For example, the mention determination module 206 may expand the named entity “GP Solutions” to its full name “Great Plains Solutions.” The mention determination module 206 can perform this operation using any expansion resources, such as a thesaurus dictionary, an acronym dictionary, a stemming analysis module, etc. As a whole, this expansion operation yields an expanded list of named entities. The mention determination module 206 can then determine candidate mentions for each named entity in the expanded list of entities. However, to facilitate explanation, it will henceforth be assumed that the mention determination module 206 only finds candidate mentions for the strings in the list of named entities, in their given form.
The documents can be provided in a data store 208. The data store 208 can pertain to information provided at a single site or information distributed over plural sites. For example, in the latter case, the documents may pertain to documents provided in various repositories that are accessible via a wide area network, such as the Internet.
The mention determination module 206 can store the candidate mentions in a data store 210. As stated above, each candidate mention will be referred to herein as a pairing of a particular entity (e.g., entity ei) and a particular document (e.g., document dj)—that is, (ei, dj), meaning that dj contains at least one occurrence of a string associated with ei. The complete set of candidate mentions is referred to as R.
A true mention determination module 212 operates on the candidate mentions in the data store 210 to pick out the true mentions from the false mentions. Within that functionality, a context similarity determination module (CSDM) 214 determines a context similarity score for each pair of candidate mentions (providing that the pair satisfies the criterion set forth below). The context similarity score describes a degree to which a first context associated with a first candidate mention matches a second context associated with a second candidate mention. The CSDM 214 can store the context similarity scores in a data store 216. Section C provides additional information regarding the computations performed by the CSDM 214.
A weight determination module 218 computes weights based, in part, on the context similarity scores. The weight determination module 218 can store the weights that it computes in a data store 220. Section E provides additional information regarding the computations performed by the weight determination module 218.
A co-occurrence determination module 222 determines a co-occurrence score for each candidate mention in the set of candidate mentions. The co-occurrence score quantities an extent to which the document associated with the candidate mention includes two or more strings associated with different named entities from the list of named entities. The co-occurrence score can also be regarded as a “prior” score because it provides a-priori information regarding the likelihood that a candidate mention is a true mention. The co-occurrence determination module 222 can store the co-occurrence scores in a data store 224. Section D provides additional information regarding the computations performed by the co-occurrence determination module 222.
A graph construction module 226 constructs a graph data structure (“graph”) that includes nodes associated with the set of candidate mentions. Further, the graph construction module 226 assigns a co-occurrence score (calculated by the co-occurrence determination module 222) to each node. Further, the graph construction module 226 generates an edge between each pair of nodes, and assigns a weight to that edge (as calculated by the weight determination module 218). Finally, the graph construction module 226 assigns a to-be-determined ranking score to each node. The graph construction module 226 can store the graph that it generates in a data store 228. Section B provides further information regarding the construction of the graph.
A graph solving module 230 applies a solving technique on the graph to determine the unknown ranking scores associated with the nodes in the graph. These ranking scores quantity the extent to which the candidate mentions associated with the nodes can be considered true mentions. The graph solving module 230 can store its results in a data store 232. Section F provides further information regarding one manner which can be used to determine the ranking scores.
An application 234 can make use of the ranking scores provided by the graph solving module 230. To cite one example, an enterprise may wish to perform a search over a corpus of documents to extract additional information regarding a list of named entities that appear in the table. The enterprise can then add some of the discovered information to the table. In another example, an enterprise may wish to identify and extract comments made by users regarding products made and sold by the enterprise. For example, the comments may appear in a blogs, Twitter messages, IM messages, etc.
FIG. 2 also indicates that the input module 202 can optionally receive one or more reference documents which provide additional information regarding the named entities in the list. The graph construction module 226 then represents these reference documents in its graph as virtual nodes. Section G provides additional information regarding the manner in which the TDS 200 can take into account additional information, when it is available (although the TDS 200 can perform its analysis without the additional information).
FIG. 3 shows a procedure 300 that describes, in a high-level form, one manner of operation of the TDS 200 of FIG. 2. Later sections will provide additional information regarding the operations performed in this flowchart.
In block 302, the TDS 200 receives and stores a list of named entities pertaining to any subject matter domain. In block 304, the TDS 200 determines and stores a set of candidate mentions. Each candidate mention corresponds to an occurrence of at least one string associated with a named entity in a document. In block 306, the TDS 200 identifies and stores true mentions of the named entities within the set of documents. It performs this task by leveraging the homogeneity in the list of named entities. More specifically, the TDS 200 can determine the true mentions by applying the three hypotheses described above: (a) the context similarity hypothesis; (b) the co-mention hypothesis; and (c) the interdependence hypothesis. In block 308, the TDS 200 outputs the true mentions determined in block 308.
FIG. 4 is a procedure 400 that describes additional detail regarding block 306 of FIG. 3. Again, later sections will provide additional information regarding the operations performed in this flowchart. The TDS 200 need not perform the operations in procedure 400 in the order listed in FIG. 4.
In block 402, the TDS 200 creates nodes in the graph corresponding to the candidate mentions. In block 404, the TDS 200 determines context similarity scores between pairs of candidate mentions. In block 406, the TDS 200 determines weights between nodes in the graph, based, in part, on the context similarity scores provided in block 404. In block 408, the TDS 200 assigns the weights computed in block 406 to the edges of the graph. In block 410, the TDS 200 determines co-occurrence scores associated with the candidate mentions. In block 412, the TDS 200 assigns the co-occurrence scores to the nodes in the graph. In block 414, the TDS 200 applies a solving technique to derive ranking scores. Each ranking score is associated with a node in the graph.
B. Generating the Graph
FIG. 5 is a small sample of a graph that may be constructed by the TDS 200 of FIG. 2. This graph corresponds to some of the relationships depicted in FIG. 1. That is the graph shows the relationships between two entities (e1 and e2) and three documents (d1, d2, and d3); for simplicity, the graph omits the relationship between e3 and d4.
The graph construction module 226 assigns a node to each candidate mention. Generally, a node indicates that there is at least one occurrence of a string associated with a named entity ei in a document dj. That is, although a document may contain multiple occurrences of a string for a particular named entity, to simplify analysis, the graph construction module 226 only assigns a single representative node to the pairing of entity ei and document dj. Hence, a first node (e1, d1) represents at last one occurrence of a string associated with e1 in d1. A second node (e1, d2) represents at least one occurrence of a string associated with e1 in d2. A third node (e2, d2) represents at least one occurrence of a string associated with e2 in d2. A fourth node (e2, d3) represents at last one occurrence of a string associated with e2 in d3.
To simplify analysis, the graph construction module 226 assumes that the plural occurrences of a string in a document (if present) refer to the same concept. That is, a document which mentions “apple” twice is unlikely to refer to “apple” both in the context of a computer company and in the context of a fruit. More specifically, while this case is possible in relatively rare circumstances, the graph construction module 226 ignores it to simplify its analysis.
The graph construction module 226 also assigns raw co-occurrence scores to each node. As summarized above, a co-occurrence score describes an extent to which a document associated with a candidate mention contains strings associated with two or more named entities. More specifically, the graph associates raw co-occurrence scores π11, π12, π22, and π23 with nodes (e1, d1), (e1, d2), (e2, d2), and (e2, d3), respectively. As will be clarified below, the graph construction module 226 then normalizes the raw co-occurrence scores to derive a final co-occurrence score vector p, comprising individual scores p11, p12, p22, and p23 (not shown in FIG. 5).
The graph construction module 226 also establishes edges which connect each respective pair of candidate mentions. That is, an edge connects a first candidate mention (ei, dj) to a second candidate mention (ei′, dj′). The graph construction module 226 then assigns a raw context similarity score μij,i′j′ to that edge. As described above, the raw context similarity score describes an extent to which the context of the candidate mention (ei, dj) is similar to the context of the candidate mention (ei′,dj′).
Note, however, that the graph construction module 226 does not assign a raw context similarity score to any edge that connects nodes associated with the same entity, e.g., in which ei=ei′. The graph construction module 226 omits a score in this circumstance to address a biasing effect that may be produced by a large number of false mentions associated with a particular named entity. For example, consider the example in which a list of named entities contains the names of well-known computer scientists, one of which is “Michael Jordan.” The set of candidate mentions may include a few candidate mentions for the scientist “Michael Jordan,” but it may also include a relatively large number of candidate mentions for the basketball player “Michael Jordan.” Based on the interdependency hypothesis, this large number of false mentions can spread through the graph and bias the calculation of ranking scores. The graph construction module 226 can prevent this from happening by omitting the raw context similarity scores for links between candidate mentions that pertain to the same entity. In doing so, the TDS 200 is leveraging the observation that it is more reliable for a candidate mention to be deemed true if it has similar context with mentions of many different entities, rather than with many mentions of the same named entity.
The graph construction module 226 also assigns weights w11,12, w11,22, w11,23, w12,23, w22,12, and w22,23, (not shown in FIG. 5) to respective edges. The weights collectively form a weight vector w. In some cases, a weight includes a part that is based on a corresponding raw context similarity score. Each weight also addresses normalization and smoothing considerations. Section E provides additional information regarding the computation of weights.
Finally, the graph construction module 226 assigns a to-be-determined ranking score rij to each node. As said, once the graph has been “solved,” the ranking score rij will provide a measure which reflects an extent to which the candidate mention associated with the node is a true mention.
In one illustrative implementation, the ranking score rij for entity ei and document dj is given by:
r ij = λ ⁢ ⁢ p ij + ( 1 - λ ) ⁢ ∑ i ′ , j ′ ⁢ ⁢ w ij , i ′ ⁢ j ′ ⁢ r i ′ ⁢ j ′ , ( 1 )
where λ is a constant between 0 and 1, pij is based on a co-occurrence score associated with the candidate mention (ei, dj), entity ei′ and document dj′ correspond to another candidate mention (ei′,dj′), wij,i′j′ is a weight assigned to an edge between candidate mention (ei, dj) and candidate mention (ei′,dj′), and ri′,j′ is a ranking score associated with the candidate mention (ei′,dj′). The weight wij,i′j′ will depend on the raw context similarity score μij,i′j′, so long as ei≠ei′. Collectively, the ranking scores form a vector r.
The above equation includes component parts which leverage the three hypotheses described in Section A. Namely, the context similarity hypothesis is addressed by the presence of the weight vector w, the co-occurrence hypothesis is addressed by the presence of the co-occurrence vector p, and the interdependency hypothesis is addressed by the product of w and r.
Also note that Equation (1) has two parts. A first part depends on the prior likelihood information expressed by the co-occurrence vector p. The second part takes into account the influence of related candidate mentions. The constant λ determines the importance of the first part of the equation relative to the second part.
C. Computing Context Similarity Scores
The raw context similarity score μij,i′j′ measures the degree to which the context for candidate mention (ei, dj) agrees with the context for candidate mention (ei′,dj′). The context similarity determination module (CSDM) 214 can calculate this score in various ways. First, the CSDM 214 can define the context of each candidate mention as the z words preceding a string associated with a named entity in question, and the z words following the string. For example, in document d1 of FIG. 1, the context may include the prior phrase “The new operating system of,” and the subsequent phrase, “boasts a graphical user interface.” Other implementations can define the context in other ways, such as by defining the context as an entire sentence, entire paragraph, entire record, or entire document in which a string associated with a named entity appears.
Recall that a single document may include plural occurrences of a string associated with a named entity. For example, the document d2 of FIG. 2 includes two occurrences of the string “Microship.” In one implementation, the CSDM 214 computes the context similarity score by considering each pairing of the xth occurrence of a string si for entity ei in document dj with the yth occurrence of a string si′ for entity ei′ in document dj′. For example, if there are three occurrences of string si in document dj and three occurrences of string si′ in document dj′, the CSDM 214 will perform nine separate context comparisons. Each context comparison may be denoted as θijx,i′j′y.
In one case, μij,i′j′ comprises the average of the individual context comparisons:
μ ij , i ′ ⁢ j ′ = average ⁢ x , y ⁢ θ ijx , i ′ ⁢ j ′ ⁢ y . ( 2 )
In alternative cases, the CSDM 214 can compute μij,i′j′ based on a min function, max function, median function, or any combination of these functions.
The CSDM 214 can likewise use different measures to compute each individual context comparison θijx,i′j′y. In one case, the CSDM 214 can form term-frequency inverse-document-frequency (tf-idf) vectors associated with the two contexts for comparison. The CSDM 214 can then compare the two vectors using the cosine similarity function. The cosine similarity function may be expressed as:
similarity ⁢ ⁢ measure = X · Y  X  ⁢  Y  , ( 3 )
where X and Y represent the input vectors to be compared. More specifically, the CSDM 214 can normalize the input vectors (associated with the contexts), so that each vector has a length l. After normalization, the CSDM 214 can remove noisy and indiscriminative words by respectively omitting words with very low document frequency and very high document frequency. Normalization allows the CSDM 214 to compute the similarity (using the cosine similarity technique) by simply performing a dot product.
D. Computing Co-Occurrence Scores
As stated above, each raw co-occurrence score πij measures the extent to which a document dj associated with the candidate mention (ei, dj) contains strings associated with two or more named entities in the list. In one approach, the co-occurrence module 222 can compute πij by counting the number of different strings (associated with respective named entities) in an entire document dj. In another approach, the co-occurrence module 222 can compute πij by counting the different strings that appear in the context portions of the document dj, rather than the entire document.
Once the vector π is computed, the normalized co-occurrence vector p can be obtained based on the computation: pij=πij/Σi,jπij. As noted above, the graph construction module 226 assigns the normalized co-occurrence scores pij to the nodes in the graph.
E. Computing Weights
In one implementation, the weight determination module 218 computes the weights based on the following equations:
w i ′ ⁢ j ′ , ij = { z ij k , if ⁢ ⁢ i = i ′ μ i ′ ⁢ j ′ , ij V i ⁢ Z + z ij k , otherwise , ( 4 )
z ij = 1 - Σ i ′ ≠ i ⁢ Σ j ′ ⁢ μ i ′ ⁢ j ′ , ij V i ⁢ Z , and ( 5 ) Z = max i , j ⁢ Σ i ′ ≠ i ⁢ Σ j ′ ⁢ μ i ′ ⁢ j ′ , ij V i . ( 6 )
In these equations, Vi is the number of documents that have candidate mentions of ei in the document collection, and k is the total number of candidate mentions in the graph. In other words, Vi=|{dj|(ei,dj)εR}|, and k=|R|.
Note that, if ei≠ei′, then the weighting term in Equation (4) has two components. The first part corresponds to a normalized context similarity score. The second component corresponds to a smoothing term. If ei=ei′, however, the weighting term only includes the smoothing term. This formalizes the notion set forth above, whereby the TDS 200 does not take into account the raw context similarity score between two nodes if ei=ei′. This prevents biasing that may occur in situations in which there are a large number of false mentions associated with a named entity.
As to the normalization performed by Equation (4), note that the denominator of the first component, ViZ, has the effect limiting the total contribution from candidate mentions associated with an individual named entity. This normalizing operation is another way to reduce bias in the ranking operation.
As to the smoothing term, the TDS 200 may, in some cases, select only a short text window for context similarity computation. Because of this, the context similarity score between many pairs of candidate mentions could be zero or close to zero. The smoothing term performs smoothing of the propagation weight to address this issue.
More specifically, zij and Z are constants used for smoothing. Zij controls the weight of the smoothing term 1/k. It is negatively correlated with the overall context similarity of (ei, dj) and other mentions. Z is a constant that represents the maximum overall context similarity of one mention with other mentions. If the overall context similarity of the one mention with other mentions is high (close to Z), the smoothing term will be small in order to avoid significantly deviating the final weight from the similarity score.
F. Solving the Graph
The relationship of Equation (1) can be rewritten as r=Mr, where r is the ranking score vector and M is a Markov matrix that is stochastic, irreducible, and aperiodic. Different known techniques can be used to solve this type of formulation, such as the power method.
More specifically, the power method entails iteratively solving the equation:
r m + 1 = Mr m  Mr m  . ( 7 )
The iteration starts with an initial vector r0. In one case, r0 has a value of 0.0 for each rij. The iteration continues until the change in score becomes smaller than a specified threshold ε. In one case, the computing functionality used to perform the iterative solving technique uses distributed parallel processing resources. These resources may be provided by local computing functionality and/or by remote computing functionality.
Consider the following simplified case, which modifies the example of FIG. 1 in the following manner. Assume that (e1, d1) and (e2, d3) are false mentions, and the other two mentions are true. Further assume that the context similarity score between true mentions is 0.8, and all others is 0.2. Further assume λ=0 (meaning that no entity co-mention prior is used). Under these conditions, Equation (1) becomes:
( r 11 r 12 r 22 r 23 ) = ( 0.15 0.00 0.20 0.35 0.15 0.00 0.80 0.35 0.35 0.80 0.00 0.15 0.35 0.20 0.00 0.15 ) ⁢ ( r 11 r 12 r 22 r 23 ) .
The solution is (r11, r12, r22, r23)=(0.4, 1.0, 1.0, 0.4). Note that the scores have been normalized so that the largest score is 1.0.
G. Leveraging Additional Knowledge
In the above description, the TDS 200 computes the ranking score vector r without taking into consideration any additional information pertaining to the entities. Nevertheless, in some cases, additional information may be available for one or more of the entities in the list. The additional information can take various forms. For example, the additional information for an entity may correspond to a document of any type that imparts information regarding the entity ei, such as an online encyclopedia article pertaining to the entity ei. In another case, the additional information may correspond to attributes pertaining to the entity ei obtained from any source or combination of sources, such as an online catalog or a database. For example, consider a named entity that corresponds to the name of a book. The attributes for this entity may correspond to author name, publisher, publication date, genre, etc.
The additional information for an entity ei can be expressed as a reference document ai. In the case that the additional information corresponds to a pre-existing source document, that source document serves as the reference document. In the case in which the additional information corresponds to a set of attributes, the TDS 200 can concatenate the attributes to form a pseudo-document; that pseudo-document then serves as the reference document.
The graph construction module 226 can then add a virtual node to the graph corresponding to the reference document ai. For example, assume that a reference document a1 exists for the first entity e1 shown in FIG. 1. As shown in FIG. 6, the graph construction module 226 can add a virtual node (e1, a1) to the graph. Further, the graph construction module 226 assigns a high prior score π0 to the virtual node, indicative of the fact that the information imparted by this reference source is known to have strong relevance to the entity e1.
Then, the graph construction module 226 links the virtual node to every other candidate mention in the graph. The graph construction module 226 then assigns a candidate-node-to-virtual-node score to each edge that links to the virtual node. In a first case, assume that the edge links nodes associated with the same entity, i.e., ei=ei′; here, the graph construction module 226 will use the raw context similarity score μij,i′j′ as the context-node-to-virtual-node score for the edge. In a second case, assume that the edge links nodes associated with different entities, i.e., ei≠ei′; here, the graph construction module 226 will use βμij,i′j′ as the context-node-to-virtual-node score for the edge.
The adjustment parameter β reflects an extent to which the reference document a1 is generally applicable to all of the named entities in the list of named entities. For example, suppose that the reference document a1 provides only idiosyncratic information about the company “Microship,” e.g., pertaining to some unusual product that this company produces which is out-of-character with the typical products produced by this kind of company. In this case, the parameter β would be low. The adjustment parameter β therefore controls the manner in which the relevance of the reference document a1 propagates through the graph. In one implementation, a human analyst can provide the parameter β for each reference document. Alternatively, or in addition, the TDS 200 can use an automated mechanism to determine the parameter β, such as by using a trained classifier.
The graph construction module 226 also assigns a to-be-determined ranking score r10 to the node associated with the reference document a1. This node receives a ranking score to provide a vehicle for propagating its relevance through the graph, rather than to assign a final ranking score to the reference document a1 (which is not of interest). More specifically, note that, by virtue of the interdependency hypothesis, the evidence established by the reference document can influence the ranking scores associated with candidate mentions that do not pertain to the reference document. The parameter β helps prevent this influence from spreading to entities that are not related to the reference document.
Note that, so as not to unduly complicate the figure, FIG. 6 does not label the edges between pairs of candidate mentions with their raw context similarity scores. Further, FIG. 6 does not label the candidate mention nodes with their raw co-occurrence scores. Those omitted scores (which are explicitly shown in FIG. 5) nevertheless also apply to case of FIG. 6. The weights assigned to the edges in FIG. 6 are computed in the same manner described above, e.g., using Equations (4)-(6).
To simplify explanation, it was assumed above that there is, at most, one reference document per named entity. But the TDS 200 can also accommodate the case in which a single named entity has two or more separate reference documents. For instance, the graph construction module 226 can add a separate virtual node for each reference document associated with a named entity.
FIG. 7 shows a procedure which summarizes the manner in which the TDS 200 can take into account the presence of additional information. In block 702, the TDS 200 creates nodes in the graph corresponding to the candidate mentions (this is the same as block 402 of FIG. 4). In block 704, the TDS 200 adds one or more virtual nodes to the graph associated with additional information regarding the named entities, if it exists. In block 706, the TDS 200 determines context similarity scores between pairs of candidate mentions (this is the same as block 404 of FIG. 4). In block 708, the TDS 200 determines mention-node-to-virtual-node similarity scores between respective pairs of mentions nodes and virtual nodes. The remainder of the procedure 600 conforms to blocks 406-414 of FIG. 4.
The TDS 200 can incorporate additional information into its analysis in additional ways. For example, assume that a-priori knowledge exists regarding the similarity of two or more named entities in the list of named entities. For example, assume that two or more entities pertain to a subcategory within the general subject matter domain of the list. The TDS 200 can address this situation by assigning an entity-to-entity similarity score δii′ between each entity ei and entity ei′ that reflects the degree of similarity between the entities. The TDS 200 can then assign a modified raw context similarity score to each edge corresponding to δii′μij,i′j′. This has the effect of reducing the propagation of relevancy over an edge if the two respective entities that are associated with the edge are known to be dissimilar.
H. Illustrative Computing Functionality
FIG. 8 shows a first physical implementation of the functionality shown in the foregoing drawings. In this case, all aspects of the TDS 200 described above are implemented by stand-alone local computing functionality 802, in conjunction with one or more data stores 804. FIG. 8 illustrates this point by indicating that the local computing functionality 802 includes local TDS functionality 806.
For example, the local TDS functionality 806 may correspond to a standalone utility, or a function that is integrated with some other application. In this case, the user may use the local computing functionality 802 to submit a list of named entities to the local TDS functionality 806. The local TDS functionality 806 can return an indication of true mentions of the named entities in the list. The local TDS functionality 806 can mine these true mentions from any local and/or remote repository(ies) of documents.
The local computing functionality 802 can correspond to any type of computing device, such as a personal computing device, a computer work station, a lap top computing device, a netbook-type computing device, a tablet computing device, a booklet computing device, a mobile telephone device, a personal digital assistant device, a game console device, a portable game device, a set-top box device, and so on, or any combination thereof.
FIG. 9 shows a second physical implementation of the functionality shown in the foregoing drawings. In this case, local computing functionality 902 (and associated data stores 904) are coupled to the remote computing functionality 906 (and associated data stores 908) via a communication conduit 910. Here, any aspect of the TDS 200 can be distributed between the local and remote computing functionalities (902, 906) in any manner. FIG. 9 conveys this point by showing some TDS functionality 912 provided by the local computing functionality 902 and some TDS functionality 914 provided by the remote computing functionality 906.
In one case, for instance, all of the processing performed by the TDS 200 can be implemented by the remote TDS functionality 914, e.g., as a service available to various end users, enterprises, etc. That is, the user may use the local computing functionality 902 to submit a list of named entities to the remote TDS functionality 914. The remote TDS functionality 914 can return an indication of true mentions of the named entities in the list. The remote TDS functionality 914 can mine these true mentions from any local or remote repository(ies) of documents.
The local computing functionality 902 of FIG. 9 can be implemented using any of the computing devices mentioned above with respect to FIG. 8. The remote computing functionality 906 can be implemented by one or more server computing devices. The communication conduit 910 can be implemented by a local area network, a wide area network (e.g., the Internet), or combination thereof.
FIG. 10 sets forth a yet more detailed depiction of computing functionality 1000 that can be used to implement any aspect of the functions described above. For example, the computing functionality 1000 can be used to implement any aspect of the TDS 200 of FIG. 2, e.g., as provided by the embodiment of FIG. 8, the embodiment of FIG. 9, or any other embodiment. In all cases, the computing functionality 1000 represents one or more physical and tangible processing mechanisms.
The computing functionality 1000 can include volatile and non-volatile memory, such as RAM 1002 and ROM 1004, as well as one or more processing devices 1006 (e.g., one or more CPUs, and/or one or more GPUs, etc.). The computing functionality 1000 also optionally includes various media devices 1008, such as a hard disk module, an optical disk module, and so forth. The computing functionality 1000 can perform various operations identified above when the processing device(s) 1006 executes instructions that are maintained by memory (e.g., RAM 1002, ROM 1004, or elsewhere).
More generally, instructions and other information can be stored on any computer readable medium 1010, including, but not limited to, static memory storage devices, magnetic storage devices, optical storage devices, and so on. The term computer readable medium also encompasses plural storage devices. In all cases, the computer readable medium 1010 represents some form of physical and tangible entity.
The computing functionality 1000 also includes an input/output module 1012 for receiving various inputs (via input modules 1014), and for providing various outputs (via output modules). One particular output mechanism may include a presentation module 1016 and an associated graphical user interface (GUI) 1018. The computing functionality 1000 can also include one or more network interfaces 1020 for exchanging data with other devices via one or more communication conduits 1022. One or more communication buses 1024 communicatively couple the above-described components together.
The communication conduit(s) 1022 can be implemented in any manner, e.g., by a local area network, a wide area network (e.g., the Internet), etc., or any combination thereof. The communication conduit(s) 1022 can include any combination of hardwired links, wireless links, routers, gateway functionality, name servers, etc., governed by any protocol or combination of protocols.
Alternatively, or in addition, any of the functions described in the preceding sections can be performed, at least in part, by one or more hardware logic components. For example, the computing functionality 1000 can be implemented as one or more of: Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.
1. A method implemented by one or more computer processing devices, the method comprising:
receiving and storing a list of multiple different named entities, the multiple different named entities homogenously pertaining to a particular subject matter domain;
determining and storing a set of candidate mentions of the multiple different named entities, each candidate mention being an occurrence of a corresponding named entity in the list of multiple different named entities, the set of candidate mentions including true mentions and false mentions occurring in a collection of documents;
identifying particular candidate mentions as the true mentions within the set of candidate mentions by leveraging homogeneity in the list of multiple different named entities, each true mention corresponding to a valid occurrence of an individual named entity in the collection of documents, the identifying including assigning scores to individual candidate mentions of the set of candidate mentions and identifying the particular candidate mentions as the true mentions using the scores; and
outputting the true mentions.
2. The method of claim 1, wherein the method identifies the true mentions without a-priori knowledge of the particular subject matter domain associated with the list of multiple different named entities.
3. The method of claim 1, wherein the set of candidate mentions contains:
at least one occurrence of an individual named entity from the list that is an individual true mention; and
at least one false mention that is a homograph of the individual named entity.
4. The method of claim 1, wherein the scores include context similarity scores, and said identifying comprises:
identifying contexts of the individual candidate mentions of the set of candidate mentions, the contexts corresponding to at least two distinct named entities in the list;
differentiating some contexts that are more similar and other contexts that are less similar;
assigning the context similarity scores to the individual candidate mentions based on the differentiating; and
identifying the particular candidate mentions as the true mentions based on the context similarity scores.
5. The method of claim 1, wherein the scores include co-occurrence scores, and said identifying comprises:
identifying strings associated with plural respective named entities in the list of multiple different named entities that appear as the individual candidate mentions in a particular document;
assigning the co-occurrence scores to the individual candidate mentions based on an extent to which the particular document includes two or more of the strings; and
identifying the particular candidate mentions associated with the two or more of the strings as some of the true mentions based on the co-occurrence scores.
ascertaining that a first individual candidate mention has a similar context with a first individual true mention; and
in response to the ascertaining, identifying the first individual candidate mention as a second individual true mention.
7. The method of claim 1, wherein said identifying also uses counterpart reference information pertaining to at least one named entity in the list of multiple different named entities to identify the true mentions.
8. The method of claim 1, wherein said identifying comprises:
constructing a graph, where:
each node in the graph relates to an associated candidate mention of a particular named entity from the list of multiple different named entities in a particular document,
each node in the graph is associated with a co-occurrence score that identifies an extent to which the particular document includes strings associated with plural respective named entities from the list of multiple different named entities,
each edge in the graph connects one node, associated with one of the candidate mentions, with another node, associated with another one of the candidate mentions, and
at least some edges are associated with context similarity scores that identify contextual similarity between corresponding candidate mentions; and
determining ranking scores, each ranking score identifying an extent to which the associated candidate mention associated with each node is considered one of the true mentions.
9. The method of claim 8, wherein a ranking score ri,j associated with a candidate mention (ei, dj) of entity ei in document dj is given by:
r i , j = λ ⁢ ⁢ p ij + ( 1 - λ ) ⁢ ∑ i ′ , j ′ ⁢ ⁢ w ij , i ′ ⁢ j ′ ⁢ r i ′ ⁢ j ′ ,
where λ is a constant,
pi,j is based on a particular co-occurrence score associated with the candidate mention (ei, dj),
entity ei′ and document dj′ correspond to another candidate mention (ei′, dj′),
wij,i′j′ is a weight assigned to an edge between the candidate mention (ei, dj) and the another candidate mention (ei′, dj′), and
ri′,j′ is another ranking score associated with the another candidate mention (ei′, dj′).
10. The method of claim 8, further comprising omitting consideration of a particular context similarity score between a first candidate mention (ei, dj), associated with an entity ei in document dj, and a second candidate mention (ei′,dj′), associated with an entity ei′ and document dj′, providing that ei=ei′.
creating at least one virtual node associated with an entity ei, the at least one virtual node corresponding to counterpart reference information about the entity ei; and
linking the at least one virtual node to the graph.
12. The method of claim 11, further comprising associating a mention-node-to-virtual-node similarity score to individual edges between the at least one virtual node and a node corresponding to a candidate mention (ei′, dj′), associated with entity ei′ and document dj′,
the mention-node-to-virtual-node similarity score being an associated unmodified context similarity score μij,i′j′, providing that ei=ei′,
the mention-node-to-virtual-node similarity score being an associated context similarity μij,i′j′, score modified by an adjustment parameter β, providing that ei≠ei′, and
wherein the adjustment parameter β describes another extent to which the counterpart reference information applies to the particular subject matter domain.
13. A hardware computer readable storage medium storing computer readable instructions, the computer readable instructions providing a targeted disambiguation system when executed by one or more processing devices, the computer readable instructions comprising:
logic configured to receive and store a list of multiple different named entities, the multiple different named entities homogenously pertaining to a particular subject matter domain;
logic configured to determine and store a set of candidate mentions, each candidate mention corresponding to an occurrence, in a collection of documents, of an individual string that represents a corresponding named entity in the list of multiple different named entities;
logic configured to identify contexts of the candidate mentions in the collection of documents;
logic configured to determine context similarity scores between pairs of the candidate mentions;
logic configured to determine co-occurrence scores for the candidate mentions, each co-occurrence score identifying an extent to which a particular document includes strings representing plural respective named entities from the list of multiple different named entities; and
logic configured to derive ranking scores, each ranking score identifying an extent to which a corresponding candidate mention is considered a true mention corresponding to a valid occurrence and meaning of a particular named entity in the collection of documents.
14. The hardware computer readable storage medium of claim 13, the computer readable instructions further comprising:
logic configured to create nodes in a graph corresponding to the candidate mentions;
logic configured to determine weights between the nodes in the graph based, in part, on the context similarity scores;
logic configured to assign the weights to edges in the graph;
logic configured to assign the co-occurrence scores to the nodes in the graph; and
logic configured to derive the ranking scores by solving the graph, the ranking scores being associated with the nodes in the graph.
15. The hardware computer readable storage medium of claim 14, the computer readable instructions further comprising:
logic configured to create at least one virtual node, the at least one virtual node associated with an entity ei, the at least one virtual node conveying counterpart reference information about the entity ei; and
logic configured to link the at least one virtual node to the graph.
16. The hardware computer readable storage medium of claim 15, the computer readable instructions further comprising logic configured to associate a mention-node-to-virtual-node similarity score to each edge between the at least one virtual node and a node corresponding to a candidate mention (ei′, dj′), associated with entity ei′ and document dj′,
the mention-node-to-virtual-node similarity score being an associated context similarity score μij,i′j′, modified by an adjustment parameter β, providing that ei≠ei′, and
where the adjustment parameter β describes an extent to which the counterpart reference information applies to the particular subject matter domain.
a storage device storing computer-executable instructions which, when executed by the processing device, cause the processing device to:
receive and store a list of multiple different named entities, the multiple different named entities in the list pertaining to a particular subject matter domain,
determine and store a set of candidate mentions, each candidate mention being a string representing a corresponding named entity in the list of multiple different named entities, the set of candidate mentions including true mentions and false mentions occurring in a collection of documents, the false mentions corresponding to different meanings of the multiple different named entities that do not pertain to the particular subject matter domain,
assign scores to individual candidate mentions of the set of candidate mentions, and
distinguish between the true mentions and the false mentions within the set of candidate mentions based on the scores.
18. The system of claim 17, wherein the instructions further cause the processing device to assign the scores based at least in part on context similarity between contexts of particular candidate mentions corresponding to at least two distinct named entities in the list.
19. The system of claim 17, wherein the instructions further cause the processing device to assign the scores based at least in part on co-occurrence of two or more strings representing plural respective named entities in the list of multiple different named entities, the two or more strings appearing in a particular document.
20. The system of claim 17, wherein the instructions further cause the processing device to assign the scores based at least in part on a particular candidate mention having a similar context with one or more of the true mentions.
US13531493 2012-06-22 2012-06-22 Targeted disambiguation of named entities Active US9594831B2 (en)
US13531493 US9594831B2 (en) 2012-06-22 2012-06-22 Targeted disambiguation of named entities
US20130346421A1 true US20130346421A1 (en) 2013-12-26
US9594831B2 true US9594831B2 (en) 2017-03-14
ID=49775311
US13531493 Active US9594831B2 (en) 2012-06-22 2012-06-22 Targeted disambiguation of named entities
US (1) US9594831B2 (en)
US20150254329A1 (en) * 2014-03-06 2015-09-10 Tata Consultancy Services Limited Entity resolution from documents
US9798829B1 (en) * 2013-10-22 2017-10-24 Google Inc. Data graph interface
FR3016712A1 (en) * 2014-01-21 2015-07-24 Electricite De France Method of identifying a given as relevant or irrelevant
CN104881398B (en) * 2014-08-29 2018-03-30 北京大学 English literature of China issued by the agency of information extraction
CN104933152A (en) * 2015-06-24 2015-09-23 北京京东尚科信息技术有限公司 Named entity recognition method and device
US9710544B1 (en) * 2016-05-19 2017-07-18 Quid, Inc. Pivoting from a graph of semantic similarity of documents to a derivative graph of relationships between entities mentioned in the documents
WO1997040452A1 (en) 1996-04-23 1997-10-30 Language Engineering Corporation Automated natural language translation
US6289301B1 (en) 1996-11-08 2001-09-11 The Research Foundation Of State University Of New York System and methods for frame-based augmentative communication using pre-defined lexical slots
US20010042080A1 (en) 2000-05-10 2001-11-15 Ross Gary E. Augmentation system for documentation
WO2002029627A2 (en) 2000-10-06 2002-04-11 Polar Extreme Research Limited An improved system for storing and retrieving data
US20020103793A1 (en) 2000-08-02 2002-08-01 Daphne Koller Method and apparatus for learning probabilistic relational models having attribute and link uncertainty and for performing selectivity estimation using probabilistic relational models
US20020123882A1 (en) 2000-12-29 2002-09-05 Yunus Mohammed Compressed lexicon and method and apparatus for creating and accessing the lexicon
US20020169755A1 (en) 2001-05-09 2002-11-14 Framroze Bomi Patel System and method for the storage, searching, and retrieval of chemical names in a relational database
US20020178005A1 (en) 2001-04-18 2002-11-28 Rutgers, The State University Of New Jersey System and method for adaptive language understanding by computers
US6711577B1 (en) 2000-10-09 2004-03-23 Battelle Memorial Institute Data mining and visualization techniques
US20050021324A1 (en) 2003-07-25 2005-01-27 Brants Thorsten H. Systems and methods for new event detection
US6876963B1 (en) 1999-09-24 2005-04-05 International Business Machines Corporation Machine translation method and apparatus capable of automatically switching dictionaries
US20050086592A1 (en) 2003-10-15 2005-04-21 Livia Polanyi Systems and methods for hybrid text summarization
US20050114322A1 (en) 1998-09-27 2005-05-26 Infobit, Ltd. Apparatus and Method fopr Search and Retrieval of Documents
US20050216444A1 (en) 2004-03-25 2005-09-29 Ritter Gerd M Relationship-based searching
US20060089927A1 (en) 2004-10-27 2006-04-27 Tildy, Llc Indexing and querying engines and methods of indexing and querying
US7080068B2 (en) 2000-06-28 2006-07-18 Thomas Leitermann Automatic search method
US20060173674A1 (en) 2000-04-25 2006-08-03 Microsoft Corporation Language model sharing
US20060195421A1 (en) 2005-02-25 2006-08-31 International Business Machines Corporation System and method of generating string-based search expressions using templates
US20060206306A1 (en) 2005-02-09 2006-09-14 Microsoft Corporation Text mining apparatus and associated methods
WO2006123918A1 (en) 2005-05-20 2006-11-23 Nhn Corporation Query matching system and method, and computer readable recording medium recording program for implementing the method
US20070038663A1 (en) 2005-08-11 2007-02-15 Marc Colando Systems and methods for extracting and adapting data
US20070100823A1 (en) 2005-10-21 2007-05-03 Inmon Data Systems, Inc. Techniques for manipulating unstructured data using synonyms and alternate spellings prior to recasting as structured data
US7254530B2 (en) 2001-09-26 2007-08-07 The Trustees Of Columbia University In The City Of New York System and method of generating dictionary entries
US20070192085A1 (en) 2006-02-15 2007-08-16 Xerox Corporation Natural language processing for developing queries
US20070233656A1 (en) 2006-03-31 2007-10-04 Bunescu Razvan C Disambiguation of Named Entities
US20070239742A1 (en) 2006-04-06 2007-10-11 Oracle International Corporation Determining data elements in heterogeneous schema definitions for possible mapping
US7293003B2 (en) 2002-03-21 2007-11-06 Sun Microsystems, Inc. System and method for ranking objects by likelihood of possessing a property
US20080021898A1 (en) 2006-07-20 2008-01-24 Accenture Global Services Gmbh Universal data relationship inference engine
US20080087725A1 (en) 2006-10-11 2008-04-17 Qing Liu Fixture based Item Locator System
US20080091660A1 (en) 2006-10-16 2008-04-17 Electronics And Telecommunications Research Institue System and method for searching information using synonyms
US20080109416A1 (en) 2006-11-06 2008-05-08 Williams Frank J Method of searching and retrieving synonyms, similarities and other relevant information
US20080147618A1 (en) 2005-02-25 2008-06-19 Volker Bauche Method and Computer Unit for Determining Computer Service Names
US20080270116A1 (en) 2007-04-24 2008-10-30 Namrata Godbole Large-Scale Sentiment Analysis
US20080266148A1 (en) 2007-04-30 2008-10-30 Jen-Te Chen Decoding Method Utilizing Temporally Ambiguous Code and Apparatus using the same
US20080275837A1 (en) 2007-05-01 2008-11-06 Lambov Branimir Z Method and system for approximate string matching
WO2008141583A1 (en) 2007-05-22 2008-11-27 Beijing Sogou Technology Development Co., Ltd. Character input method, input system and method for updating word lexicon
US20080313607A1 (en) 2007-06-15 2008-12-18 Microsoft Corporation Unified input stack
CN101354707A (en) 2007-07-26 2009-01-28 国际商业机器公司 Method and apparatus for customizing model entity expression based on expression regulation
US20090222434A1 (en) 2008-03-03 2009-09-03 Oracle International Corporation Inclusion of metadata in indexed composite document
US20090282012A1 (en) * 2008-05-05 2009-11-12 Microsoft Corporation Leveraging cross-document context to label entity
US20090327223A1 (en) 2008-06-26 2009-12-31 Microsoft Corporation Query-driven web portals
US20100004925A1 (en) 2008-07-03 2010-01-07 Xerox Corporation Clique based clustering for named entity recognition system
US20100005086A1 (en) 2008-07-03 2010-01-07 Google Inc. Resource locator suggestions from input character sequence
US20100082657A1 (en) 2008-09-23 2010-04-01 Microsoft Corporation Generating synonyms based on query log data
US20100106677A1 (en) 2004-03-09 2010-04-29 Gozoom.Com, Inc. Email analysis using fuzzy matching of text
CN101256462B (en) 2007-02-28 2010-06-23 北京三星通信技术研究有限公司;三星电子株式会社 Hand-written input method and apparatus based on complete mixing association storeroom
CN101785000A (en) 2007-06-25 2010-07-21 谷歌股份有限公司 Word probability determination
US7778817B1 (en) 2000-09-30 2010-08-17 Intel Corporation Method and apparatus for determining text passage similarity
US20100250598A1 (en) 2009-03-30 2010-09-30 Falk Brauer Graph based re-composition of document fragments for name entity recognition under exploitation of enterprise databases
US20100293179A1 (en) 2009-05-14 2010-11-18 Microsoft Corporation Identifying synonyms of entities using web search
US20100313258A1 (en) 2009-06-04 2010-12-09 Microsoft Corporation Identifying synonyms of entities using a document collection
US7877343B2 (en) 2007-04-02 2011-01-25 University Of Washington Through Its Center For Commercialization Open information extraction from the Web
US20110071965A1 (en) 2009-09-24 2011-03-24 Yahoo! Inc. System and method for cross domain learning for data augmentation
US20110093479A1 (en) 2009-10-19 2011-04-21 Vexigo, Ltd. System and method for use of semantic understanding in storage, searching and providing of data or other content information
US20110106807A1 (en) 2009-10-30 2011-05-05 Janya, Inc Systems and methods for information integration through context-based entity disambiguation
US20110125776A1 (en) 2009-11-24 2011-05-26 International Business Machines Corporation Service Oriented Architecture Enterprise Service Bus With Advanced Virtualization
US7958489B2 (en) 2007-04-12 2011-06-07 Microsoft Corporation Out of band data augmentation
US20110196670A1 (en) 2010-02-09 2011-08-11 Siemens Corporation Indexing content at semantic level
US20110202874A1 (en) 2005-09-14 2011-08-18 Jorey Ramer Mobile search service instant activation
US20110225133A1 (en) 2010-03-09 2011-09-15 Microsoft Corporation Metadata-aware search engine
US20110282856A1 (en) 2010-05-14 2011-11-17 Microsoft Corporation Identifying entity synonyms
US20110302179A1 (en) * 2010-06-07 2011-12-08 Microsoft Corporation Using Context to Extract Entities from a Document Collection
US20110307485A1 (en) * 2010-06-10 2011-12-15 Microsoft Corporation Extracting topically related keywords from related documents
US20110314006A1 (en) 2008-05-01 2011-12-22 Primal Fusion Inc. Methods and apparatus for searching of content using semantic synthesis
US20110320548A1 (en) 2010-06-16 2011-12-29 Sony Ericsson Mobile Communications Ab User-based semantic metadata for text messages
US20110316772A1 (en) 2009-03-19 2011-12-29 Google Inc. Input method editor
CN102306144A (en) 2011-07-18 2012-01-04 南京邮电大学 Terms disambiguation method based on semantic dictionary
CN102314452A (en) 2010-06-30 2012-01-11 北京搜狗科技发展有限公司 Method for navigation through input method platform and system
US20120011115A1 (en) 2010-07-09 2012-01-12 Jayant Madhavan Table search using recovered semantic information
US20120042022A1 (en) 2010-02-17 2012-02-16 Wright State University Methods and systems for analysis of real-time user-generated text messages
US20120065963A1 (en) 2007-09-18 2012-03-15 At&T Intellectual Property I, Lp System And Method Of Generating Responses To Text-Based Messages
US20120150838A1 (en) 2010-12-08 2012-06-14 Microsoft Corporation Automated database generation for answering fact lookup queries
CN102609407A (en) 2012-02-16 2012-07-25 复旦大学 Fine-grained semantic detection method of harmful text contents in network
US20120191642A1 (en) 2011-01-25 2012-07-26 Muthian George User defined function classification in analytical data processing systems
US20120259890A1 (en) 2002-05-08 2012-10-11 International Business Machines Corporation Knowledge-based data mining system
US20130018894A1 (en) 2011-07-11 2013-01-17 Lexxe Pty Ltd. System and method of sentiment data generation
US20130159277A1 (en) 2011-12-14 2013-06-20 Microsoft Corporation Target based indexing of micro-blog content
US20130166573A1 (en) 2011-12-27 2013-06-27 Business Objects Software Ltd. Managing Business Objects Data Sources
US20130232129A1 (en) 2012-03-05 2013-09-05 Microsoft Corporation Robust discovery of entity synonyms using query logs
US20130238621A1 (en) 2012-03-06 2013-09-12 Microsoft Corporation Entity Augmentation Service from Latent Relational Data
US20130246322A1 (en) 2012-03-15 2013-09-19 Cept Systems Gmbh Methods, Apparatus and Products for Semantic Processing of Text
US20130290390A1 (en) 2010-11-03 2013-10-31 Sk Telecom Co Ltd Method and device for inputting equation
US20130325436A1 (en) 2012-05-29 2013-12-05 Wright State University Large Scale Distributed Syntactic, Semantic and Lexical Language Models
US20130346464A1 (en) 2012-06-20 2013-12-26 Microsoft Corporation Data Services for Enterprises Leveraging Search System Data Assets
US20140058722A1 (en) 2012-08-24 2014-02-27 Microsoft Corporation Word Detection and Domain Dictionary Recommendation
US8996356B1 (en) 2012-04-10 2015-03-31 Google Inc. Techniques for predictive input method editors
US20150121290A1 (en) 2012-06-29 2015-04-30 Microsoft Corporation Semantic Lexicon-Based Input Method Editor
US20150199332A1 (en) 2012-07-20 2015-07-16 Mu Li Browsing history language model for input method editor
US20120117078A1 (en) 2000-07-06 2012-05-10 Streamsage, Inc. Method and System for Indexing and Searching Timed Media Information Based Upon Relevant Intervals
US7346490B2 (en) 2000-09-29 2008-03-18 Axonwave Software Inc. Method and system for describing and identifying concepts in natural language text for information retrieval and processing
US20110161080A1 (en) 2009-12-23 2011-06-30 Google Inc. Speech to Text Conversion
US20110153325A1 (en) 2009-12-23 2011-06-23 Google Inc. Multi-Modal Input on an Electronic Device
CN102193970A (en) 2010-03-09 2011-09-21 微软公司 Metadata-aware search engine
WO2013133985A1 (en) 2012-03-06 2013-09-12 Microsoft Corporation Entity augmentation service from latent relational data
US20160012036A1 (en) 2012-08-24 2016-01-14 Microsoft Technology Licensing, Llc Word detection and domain dictionary recommendation
Non-Patent Citations (323)
"Advisory Action", From U.S. Appl. No. 12/235,635, Mailed Feb. 6, 2012.
"Final Office Action and Examiner Initiated Interview Summary", From U.S. Appl. No. 12/465,832, Mailed Oct. 21, 2013.
"Final Office Action", From U.S. Appl. No. 12/235,635, Mailed Oct. 25, 2011.
"Final Office Action", From U.S. Appl. No. 12/779,964 , Mailed Aug. 27, 2012.
"Final Office Action", From U.S. Appl. No. 12/779,964 , Mailed Jun. 3, 2014.
"Final Office Action", From U.S. Appl. No. 13/413,179, Mailed Dec. 5, 2013.
"Foundations of Statistical Natural Language Processing," retrieved at <<http://n1p.stanford.eduifsnip/>>, retrieved on Jul. 4, 2012, companion website to the book: Foundations of Statistical Natural Language Processing, Manning, et al., MIT Press, Jun. 18, 1999, 2 pages.
"Foundations of Statistical Natural Language Processing," retrieved at >, retrieved on Jul. 4, 2012, companion website to the book: Foundations of Statistical Natural Language Processing, Manning, et al., MIT Press, Jun. 18, 1999, 2 pages.
"Microsoft Research Tech Fest 2012: Projects," retrieved at <<http://research.microsoft.com/en-us/events/techfest2012/projects.aspx>>, retrieved on Apr. 10, 2012, Microsoft Corporation, Redmond, WA, 7 pages.
"Non-Final Office Action", From U.S. Appl. No. 12/235,635, Mailed Feb. 16, 2011.
"Non-Final Office Action", From U.S. Appl. No. 12/235,635, Mailed Nov. 18, 2013.
"Non-Final Office Action", From U.S. Appl. No. 12/465,832, Mailed Jun. 13, 2013.
"Non-Final Office Action", From U.S. Appl. No. 12/779,964 , Mailed Feb. 14, 2012.
"Non-Final Office Action", From U.S. Appl. No. 12/779,964 , Mailed Nov. 18, 2013.
"Non-Final Office Action", From U.S. Appl. No. 13/413,179, Mailed Apr. 3, 2013.
"Non-Final Office Action", From U.S. Appl. No. 13/413,179, Mailed Jan. 15, 2015.
"Non-Final Office Action", From U.S. Appl. No. 13/527,601, Mailed Jan. 15, 2015.
"Non-Final Office Action," From U.S. Appl. No. 131487,260, Mailed Mar. 7, 2013.
"Notice of Allowance" from U.S. Appl. No. 12/235,635, Mailed Aug. 29, 2014.
"Notice of Allowance" from U.S. Appl. No. 12/235,635, Mailed Dec. 11, 2014.
"Notice of Allowance" from U.S. Appl. No. 12/235,635, Mailed May 23, 2014.
"PageRank," retrieved at <<http://en.wikipedia.org/wiki/PageRank>>, Wikipedia article, retrieved on Jun. 22, 2012, 14 pages.
"PageRank," retrieved at >, Wikipedia article, retrieved on Jun. 22, 2012, 14 pages.
"Preliminary Report on Patentability", From PCT Application No. PCT/US2013/027203, Mailed Sep. 9, 2014.
"Response to the Apr. 10, 2012 Non-Final Office Action," From U.S. Appl. No. 12/465,832, Filed Jul. 10, 2012.
"Response to the Apr. 3, 2013 Non-Final Office Action," From U.S. Appl. No. 13/413,179, Filed Sep. 3, 2013.
"Response to the Aug. 27, 2012 Final Office Action," From U.S. Appl. No. 12/779,964, Filed Nov. 1, 2012.
"Response to the Dec. 5, 2013 Non-Final Office Action," From U.S. Appl. No. 13/413,179, Filed Jun. 5, 2014.
"Response to the Feb. 14, 2012 Non-Final Office Action," From U.S. Appl. No. 12/779,964, Filed Jun. 11, 2012.
"Response to the Feb. 16, 2011 Non-Final Office Action," From U.S. Appl. No. 12/235,635, Filed Jul. 18, 2011.
"Response to the Feb. 27, 2012 Final Office Action," From U.S. Appl. No. 12/478,120, Filed Jun. 27, 2012.
"Response to the Jun. 13, 2013 Non-Final Office Action," From U.S. Appl. No. 121465,832, Filed Jul. 30, 2012.
"Response to the Jun. 3, 2014 Final Office Action," From U.S. Appl. No. 12/779,964, Filed Oct. 2, 2014.
"Response to the Mar. 7, 2013 Non-Final Office Action," From U.S. Appl. No. 13/487,260, Filed Aug. 6, 2013.
"Response to the May 23, 2011 Non-Final Office Action," From U.S. Appl. No. 12/465,832, Filed Aug. 16, 2011.
"Response to the Nov. 18, 2013 Non-Final Office Action," From U.S. Appl. No. 12/235,635, Filed Feb. 17, 2014.
"Response to the Nov. 18, 2013 Non-Final Office Action," From U.S. Appl. No. 12/779,964, Filed: Feb. 18, 2014.
"Response to the Nov. 3, 2011 Non-Final Office Action," From U.S. Appl. No. 12/478,120, Filed Feb. 2, 2012.
"Response to the Oct. 25, 2011 Final Office Action," From U.S. Appl. No. 12/235,635, Filed Mar. 25, 2012.
"Response to the Oct. 7, 2011 Final Office Action," From U.S. Appl. No. 121465,832, Filed Dec. 22, 2011.
"Response to the Sep. 19, 2012 Final Office Action," From U.S. Appl. No. 12/465,832, Filed Dec. 18, 2012.
"Supplemental Response", From U.S. Appl. No. 12/235,635, Filed Mar. 11, 2014.
Advisory Action, Examiner-Initiated Interview Summary and After Final Consideration Program Decision mailed May 31, 2016 from U.S. Appl. No. 13/527,601, 5 pages.
Agrawal et al., "Mining Association Rules between Sets of Items in Large Databases," Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, May 1993, 10 pages.
Agrawal et al., "Scalable Ad-hoc Entity Extraction from Text Collections," Proceedings of the VLDB Endowment VLDB Endowment, vol. 1, Issue 1, Aug. 2008, pp. 945-957, 13 pages.
Agrawal, "Mining Association Rules Between Sets of Items in Large Databases," retrieved at <<http://rakesh.agrawal-family.com/papers/sigmod93assoc.pdf>>, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, May 1993, 10 pages.
Agrawal, "Mining Association Rules Between Sets of Items in Large Databases," retrieved at >, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, May 1993, 10 pages.
Agrawal, et al., "Exploiting web search engines to search structured databases," retrieved at <<http://acm.org>>, Proceedings of the 18th International Conference on World Wide Web, Apr. 2009, pp. 501-510.
Aho, et al., "Efficient String Matching: An Aid to Bibliographic Search," retrieved at <<http://www.win.tue.n1/˜watson/2R0801opdracht/p333-aho-corasick.pdf>>, Communications of the ACM CACM, vol. 18, Issue 6, Jun. 1975, pp. 333-340.
Aho, et al., "Efficient String Matching: An Aid to Bibliographic Search," retrieved at >, Communications of the ACM CACM, vol. 18, Issue 6, Jun. 1975, pp. 333-340.
Amadeo, Ron, "2016 Google Tracker: Everything Google is working for the next year", retrieved at <<http://arstechnica.com/gadgets/2016/01/2016-google-tracker-everything-google-is-working-on-for-the-new-year/>>, on Aug. 26, 2016, published Jan. 8, 2016, 5 pages.
Amendment and Response filed Apr. 14, 2015 to Notice of Allowance mailed Jan. 14, 2015 from U.S. Appl. No. 13/594,473, 10 pages.
Amendment and Response filed Aug. 12, 2015 to Non-Final Office Action mailed May 12, 2015 from U.S. Appl. No. 13/594,473, 10 pages.
Amendment and Response filed Dec. 11, 2014 to Non-Final Office Action mailed Sep. 11, 2014 from U.S. Appl. No. 13/594,473, 13 pages.
Amendment/Response filed Apr. 15, 2015 to the Non-Final Office Action mailed Jan. 15, 2015 to from U.S. Appl. No. 13/413,179, 23 pages.
Ananthanarayanan et al., "Rule Based Synonyms for Entity Extraction from Noisy Text", Proceedings of the Second Workshop on Analytics for Noisy Unstructured Text Data, pp. 31-38, 2008.
Appelt et al., "Introduction to Information Extraction Technology", Proceedings of the International Joint Conference on Artificial Intelligence Tutorial, 1999.
Apple, "Chinese Input Method: Use the Pinyin-Simplified Input Source", retrieved at https://support.apple.com/kb/PH22620?locale=en-US>>, on Aug. 26, 2016, published Sep. 30, 2015, 4 pages.
Apple, IOS Developer Library, "Managing the Keyboard", retrieved at https://developer.apple.com/library/prerelease/content/documentation/StringsTextFonts/Conceptual/TextAndWebiPhone0S/KeyboardManagement/KeyboardManagement.html>>, on Aug. 26, 2016, published Mar. 10, 2014, 9 pages.
Applicant Initiated Interview mailed Mar. 21, 2014 from U.S. Appl. No. 12/779,964, 3 pages.
Applicant Initiated Interview mailed Oct. 30, 2012 from U.S. Appl. No. 12/779,964, 3 pages.
Applicant-Initiated Interview Summary mailed Nov. 4, 2015 from U.S. Appl. No. 13/527,601, 4 pages.
Arasu et al., "Learning String Transformations from Examples", Proceedings of the Publication of Very Large Database Endowment, pp. 2(1):514-525, 2009.
Arasu et al., "Transformation-Based Framework for Record Matching" Proceedings of the 24th IEEE International Conference on Data Engineering, pp. 40-49, 2008.
Arasu, et al., "Efficient Exact Set-Similarity Joins," retrieved at <<http://www.vldb.org/conf/20061p918-arasu.pdf>>, Proceedings of the 32nd International Conference on Very Large Data Bases, Sep. 2006, pp. 918-929.
Arasu, et al., "Efficient Exact Set-Similarity Joins," retrieved at >, Proceedings of the 32nd International Conference on Very Large Data Bases, Sep. 2006, pp. 918-929.
Arasu, et al., "PageRank Computation and the Structure of the Web: Experiments and Algorithms," retrieved at <<http://www2002.org/CDROM/poster/173.pdf>>, Proceedings of the Eleventh International World Wide Web Conference, 2002, 5 pages.
Arasu, et al., "PageRank Computation and the Structure of the Web: Experiments and Algorithms," retrieved at >, Proceedings of the Eleventh International World Wide Web Conference, 2002, 5 pages.
Argawal, et al., "Scalable Ad-hoc Entity Extraction from Text Collections," retrieved at <<http://www.cs.uwaterloo.cal˜ilyas/CS848F08/papers/agrawal2008.pdf>>, Proceedings of the VLDB Endowment VLDB Endowment, vol., Issue 1, Aug. 2008 pp. 945-957.
Argawal, et al., "Scalable Ad-hoc Entity Extraction from Text Collections," retrieved at >, Proceedings of the VLDB Endowment VLDB Endowment, vol., Issue 1, Aug. 2008 pp. 945-957.
Artiles, et al., "WePS-3 Evaluation Campaign: Overview of the Web People Search Clustering and Attribute Extraction Tasks," retrieved at <<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.174.3094&rep=rep1&type=pdf>>, Proceedings of CLEF, 2010, 15 pages.
Artiles, et al., "WePS-3 Evaluation Campaign: Overview of the Web People Search Clustering and Attribute Extraction Tasks," retrieved at >, Proceedings of CLEF, 2010, 15 pages.
Aymerich et al., "Automatic Extraction of Entries for a Machine Translation Dictionary Using Bitexts," Machine Translation Summit XI, Sep. 10, 2007, 7 pages.
Baeza-Yates, et al., "Extracting Semantic Relations from Query Logs", Yahoo! Research, KDD'07, Aug. 2007, San Jose, California, USA, 10 pages.
Bahmani, et al., "Fast Personalized Pagerank on Mapreduce", in SIGMOD, Jun. 12-16, 2011, Athens, Greece, 12 pages.
Banko et al., "Open Information Extraction from the Web", Commun. ACM 51, 12 (Dec. 2008), 68-74.
Baroni, et al., "Using cooccurrence statistics and the web to discover synonyms in a technical language," retrieved at <<http://clic.cimec.unitn.il/marco/publications/Irec2004/syn-lrec-2004.pdf>>, Proceedings of the LREC 2004, 2004, 4 pages.
Bellahsene et al., "Schema Matching and Mapping," Springer, 2011.
Berlin, et al., "TupleRank: Ranking Discovered Content in Virtual Databases", In Proceedings of 6th International Conference on Next Generation Information Technologies and Systems, Jul. 4-6, 2006, 15 pages.
Bernstein, et al., "Generic Schema Matching, Ten Years Later". In VLBD Endowment, vol. 4, No. 11, 2011, 7 pages.
Bhattacharya, et al., "Collective Entity Resolution in Relational Data," retrieved at <<http://linqs.cs.umd.edu/basilic/web/Publications/2007/bhattacharya:tkdd07/bhattacharya-tkdd.pdf>>, ACM Transactions on Knowledge Discovery from Data, vol. 1, No. 1, 2007, 35 pages.
Bhattacharya, et al., "Collective Entity Resolution in Relational Data," retrieved at >, ACM Transactions on Knowledge Discovery from Data, vol. 1, No. 1, 2007, 35 pages.
Bohn, Christian, "Extracting Named Entities and Synonyms from Wikipedia for use in News Search," retrieved at <<http://daimidi.ntnu.no/masteroppgaver/IME/IDI/2008/4290/masteroppgave.pdf>>, Master of Science in Computer Science, Norwegian University of Science and Technology, Department of Computer and Information Science, Jun. 2008, 95 pages.
Bohn, Christian, "Extracting Named Entities and Synonyms from Wikipedia for use in News Search," retrieved at >, Master of Science in Computer Science, Norwegian University of Science and Technology, Department of Computer and Information Science, Jun. 2008, 95 pages.
Booth et al., "Query Sentences as Semantic (Sub) Networks," 2009 IEEE International Conference on Semantic Computing, 2009, 6 pages.
Brin, et al., "The Anatomy of a Large-Scale Hypertextual Web Search Engine," retrieved at <<http://www.cs.panam.edu/˜creilly/courses/CSCI6175-F11/papers/Brin-1998.pdf>>, Proceedings of the Seventh International Conference on World Wide Web 7, 1998, 20 pages.
Brin, et al., "The Anatomy of a Large-Scale Hypertextual Web Search Engine," retrieved at >, Proceedings of the Seventh International Conference on World Wide Web 7, 1998, 20 pages.
Bunescu, et al., "Using Encyclopedic Knowledge for Named Entity Disambiguation," retrieved at <<http://www.cs.utexas.edu/˜ml/papers/encyc-eacl-06.pdf>>, Proceeding of the 11th Conference of the European Chapter of the Association of Computational Linguistics, 2006, 8 pages.
Bunescu, et al., "Using Encyclopedic Knowledge for Named Entity Disambiguation," retrieved at >, Proceeding of the 11th Conference of the European Chapter of the Association of Computational Linguistics, 2006, 8 pages.
Cafarella, et al., "Data Integration for the Relational Web". VLDB, Aug. 24-28, 2009, Lyon, France, 12 pages.
Cafarella, et al., "Uncovering the Relational Web", in WebDB, Jun. 13, 2008, Vancouver, Canada, 6 pages.
Cafarella, et al., "Webtables: Exploring the Power of Tables on the Web", PVLDB, 2008, 12 pages.
Chaiken et al., "Scope: Easy and Efficient Parallel Processing of Massive Data Sets", Proceedings of Very Large Database Endowment, pp. 1(2):1265-1276, 2008.
Chakaravarthy, et al., "Efficiently Linking Text Documents with Relevant Structured Information," retrieved at <<http://www.vldb.org/conf/2006/p667-chakaravarthy.pdf>>, VLDB '06, Proceedings of the 32nd International Conference on Very Large Data Bases, 2006, pp. 667-678.
Chakaravarthy, et al., "Efficiently Linking Text Documents with Relevant Structured Information," retrieved at >, VLDB '06, Proceedings of the 32nd International Conference on Very Large Data Bases, 2006, pp. 667-678.
Chakrabarti, et al., "An Efficient Filter for Approximate Membership Checking," retrieved at <<http://acm.org>>, Proceedings of the 2008 ACM Sigmod International Conference on Management of Data, Jun. 2008, pp. 805-817.
Chakrabarti, et al., "An Efficient Filter for Approximate Membership Checking," retrieved at >, Proceedings of the 2008 ACM Sigmod International Conference on Management of Data, Jun. 2008, pp. 805-817.
Chandel, et al., "Efficient Balch Top-k Search for Dictionary-based Entity Recognition", Retrieved at <<http://www.il.iilb.ac.In/-sunila/papers/icde06b.pdf>>, Proceedings of the IEEE 22nd International Conference on Data Engineering (ICDE '06), Apr. 3-7, 2006, Atlanta, Georgia, 10 pages.
Chandel, et al., "Efficient Balch Top-k Search for Dictionary-based Entity Recognition", Retrieved at >, Proceedings of the IEEE 22nd International Conference on Data Engineering (ICDE '06), Apr. 3-7, 2006, Atlanta, Georgia, 10 pages.
Chang, et al., "Structured Databases on the Web: Observations and Implications," accessed at <<http://eagle.cs.uiuc.edu/pubs/20041dwsurvey-sigmodrecord-chIpz-aug04.pdf>>, ACM SIGMOD Record Archive, vol. 33, Issue 3, 2004, 10 pages.
Chang, et al., "Structured Databases on the Web: Observations and Implications," accessed at >, ACM SIGMOD Record Archive, vol. 33, Issue 3, 2004, 10 pages.
Chaudhuri et al., "Mining the Web to Facilitate Fast and Accurate Approximate Match," Proceedings of WWW2009, Apr. 20-24, 2009, Madrid, Spain, 10 pages.
Chaudhuri, et al., "A Primitive Operator for Similarity Joins in Data Cleaning," retrieved at <<http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1617373&isnumber=33902>>, Proceedings of the 22nd International Conference on Data Engineering (ICDE 2006), 2006, 12 pages.
Chaudhuri, et al., "A Primitive Operator for Similarity Joins in Data Cleaning," retrieved at >, Proceedings of the 22nd International Conference on Data Engineering (ICDE 2006), 2006, 12 pages.
Chaudhuri, et al., "Exploiting Web Search to Generate Synonyms for Entities," retrieved at <<http://www2009.org/proceedings/pdf/p151.pdf>>, Proceedings of the 18th International Conference on World Wide Web, April2009, pp. 151-160.
Chaudhuri, et al., "Mining Document Collections to Facilitate Accurate Approximate Entity Matching," retrieved at <<http://www.vldb.org/pvldb/2/vIdb09-315.pdf>>, Proceedings of the VLBD Endowment, vol. 2, No. 1, Aug. 2009, 12 pages.
Chaudhuri, et al., "Robust and Efficient Fuzzy Match for Online Data Cleaning," retrieved at <<http://research.microsoft.com/pubs/75996/bm-sigmod03.pdf>>, SIGMOD '03, Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data , Jun. 9, 2003, 12 pages.
Chaudhuri, et al., "Robust and Efficient Fuzzy Match for Online Data Cleaning," retrieved at >, SIGMOD '03, Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data , Jun. 9, 2003, 12 pages.
Chen, et al., "A Query Substitution-Search Result Refinement Approach for Long Query Web Searches," retrieved at <<http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5286069>>, IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies (WI-IAT), 2009, pp. 245-251.
Chen, et al., "A Query Substitution-Search Result Refinement Approach for Long Query Web Searches," retrieved at >, IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies (WI-IAT), 2009, pp. 245-251.
Cheng et al., "Robust Discovery of Entity Synonyms Using Query Logs," U.S. Appl. No. 61/606,481, filed Mar. 5, 2012, 61 pages.
Cheng, et al., "Entity Synonyms for Structured Web Search," retrieved at <<http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5963679>>, IEEE Transactions on Knowledge and Data Engineering, No. 99, Jul. 2011, pp. 1-15.
Cheng, et al., "Entity Synonyms for Structured Web Search," retrieved at >, IEEE Transactions on Knowledge and Data Engineering, No. 99, Jul. 2011, pp. 1-15.
Cheng, et al., "EntityRank: Searching Entities Directly and Holistically," retrieved at <<pubs/2007/entityrank-vldb07-cyc-jul07.pdf>>, Proceedings of the 33rd International Conference on Very Large Data Bases, Sep. 2007, 12 pages.
Cheng, et al., "Fuzzy Matching of Web Queries to Structured Data," retrieved at <<http://ieeexplore.ieee.org>>, 2010 IEEE 26th International Conference on Data Engineering (ICDE), Mar. 2010, pp. 713-716.
Cheng, et al., "Fuzzy Matching of Web Queries to Structured Data," retrieved at >, 2010 IEEE 26th International Conference on Data Engineering (ICDE), Mar. 2010, pp. 713-716.
Cheng, et al., U.S. Appl. No. 13/487,260, "Robust Discovery of Entity Synonyms Using Query Logs," filed Jun. 4, 2012, 61 pages.
Cheng, et al., U.S. Appl. No. 13/527,601, "Data Services for Enterprises Leveraging Search System Data Assets," filed on Jun. 20, 2012, 58 pages.
Chirita, et al., "PTAG: Large Scale Automatic Generation of Personalized Annotation TAGs for the Web," retrieved at <<http://acm.org>>, Proceedings of the 16th International Conference on World Wide Web, May 2007, pp. 845-854.
Chklovski, et al., "VERBOCEAN: Mining the Web for Fine-Grained Semantic Verb Relations," accessed at <<http:l/acl.ldc.upenn.edu/ac12004/emnIp/pdf/Chklovski.pdf>>, Proceedings of EMNLP 2004, 2004, 8 pages.
Chklovski, et al., "VERBOCEAN: Mining the Web for Fine-Grained Semantic Verb Relations," accessed at >, Proceedings of EMNLP 2004, 2004, 8 pages.
Cohen, et al., "Exploiting Dictionaries in Named Entity Extraction: Combining Semi-Markov Extraction Processes and Data Integration Methods," retrieved on at <<http://www.cs.cmu.edu/-wcohen/postscript/kdd-04-csmm.pdf>>, Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data mining, Aug. 2004, 10 pages.
Cohen, et al., "Learning to Match and Cluster Large High-Dimensional Data Sets for Data Integration", In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Jul. 23-26, 2002, 6 pages.
Cohen, et al., "XSEarch: A Semantic Search Engine for XML," accessed at <<http://www.vldb.org/conf/2003/papers/S03P02.pdf>>, Proceedings of the 29th VLDB Conference, 2003, 12 pages.
Cohen, et al., "XSEarch: A Semantic Search Engine for XML," accessed at >, Proceedings of the 29th VLDB Conference, 2003, 12 pages.
Corrected Notice of Allowability mailed Dec. 22, 2016 from U.S. Appl. No. 12/779,964, 6 pages.
Craswell, et al., "Random Walks on the Click Graph," accessed at <<http://research.microsoft.com/users/nickcil pubs/craswell-sigir07.pdf, Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2007, 8 pages.
Cucerzan, Silviu, "Large-Scale Named Entity Disambiguation Based on Wikipedia Data," retrieved at <<http://acl.Idc.upenn.edu/D/D07/D07-1074.pdf>>, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language, 2007, pp. 708-716.
Cucerzan, Silviu, "Large-Scale Named Entity Disambiguation Based on Wikipedia Data," retrieved at >, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language, 2007, pp. 708-716.
Dagan et al., "Contextual Word Similarity and Estimation from Sparse Data", Computer Speech and Language, 9:123-152, 1993.
Dean et al., "MapReduce: Simplified Data Processing on Large Clusters", Communications of the ACM-50th Anniversary Edition, vol. 51 Issue 1, pp. 107-113, Jan. 2008.
Dill, et al., "SemTag and Seeker: Bootstrapping the Semantic Web Via Automated Semantic Annotation," retrieved at <<http://what.csc.villanova.edu/~cassel/9010SemanticWeb/SemTag%20and%20Seeker%20Bootstrapping%20the%20semantic%20web%20via%20automated%20semantic%20annotation.pdf>>, Proceedings of the 12th International Conference on World Wide Web, 2003, 9 pages.
Dill, et al., "SemTag and Seeker: Bootstrapping the Semantic Web Via Automated Semantic Annotation," retrieved at <<http://what.csc.villanova.edu/˜cassel/9010SemanticWeb/SemTag%20and%20Seeker%20Bootstrapping%20the%20semantic%20web%20via%20automated%20semantic%20annotation.pdf>>, Proceedings of the 12th International Conference on World Wide Web, 2003, 9 pages.
Distributional hypothesis, retrieved at <<http://en.wikipedia.org/wiki/Distributional-hypothesis>>, retrieved on Mar. 1, 2012, Wikipedia online encyclopedia excerpt, 2 pages.
Doan, et al., "Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach", in ACM Sigmod, May 21-24, 2001, 12 pages.
Dong, et al., "Reference Reconciliation in Complex Information Spaces," retrieved at <<http://db.cs.washington.edu/.semex/reconciliation-sigmod.pdf>>, Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, 2005, 12 pages.
Eadiccio, Lisa, "10 Ways Texting on Your iPhone is About to Change Forever", Time, retrieved at http://time.com/4367056/apple-imessage-wwdc-ios-10-iphone/>>, on Aug. 26 ,2016, published Jun. 14, 2016, 5 pages.
Egenhofer et al., "Determining Semantic Similarity among Entity Classes from Difference Ontologies," IEEE Transactions on Knowledge and Data Engineering, vol. 15, No. 2, Mar. 1, 2003, pp. 442-456, 16 pages.
Elsayed, et al., "Pairwise Document Similarity in Large Collections with Mapreduce", In ACL, Jun. 2008, 4 pages.
Enterprise software, retrieved at http://en.wikipedia.org/wiki/Enterprise-software, retrieved on Jun. 19, 2012, Wikipedia article, 3 pages.
European Search Report mailed Mar. 4, 2015 from European Patent Application No. 13757813.4, 3 pages.
Examination Report mailed Mar. 11, 2015 from European Patent Application No. 13757813.4, 4 pages.
Examiner Initiated Interview mailed Mar. 24, 2015 from U.S. Appl. No. 12/779,964, 2 pages.
facebook/draft-js, retrieved at <<https://github.com/facebook/draft-js.git>>, on Aug. 26, 2016, 3 pages.
Feldman et al., "Self-supervised Relation Extraction from the Web", F. Esposito et al. (Eds.): ISMIS 2006, LNAI 4203, pp. 755-764, 2006.
Final Office Action mailed Dec. 5, 2013 from U.S. Appl. No. 13/413,179, 31 pages.
Final Office Action mailed Jun. 13, 2016 from U.S. Appl. No. 13/635,274, 28 pages.
Final Office Action mailed Mar. 11, 2016 from U.S. Patent Application No. 13/527,602, 5 pp.. (MS336388.01).
Final Office Action mailed Oct. 26, 2016 from U.S. Appl. No. 14/864,430, 33 pages.
Final Office Action of U.S. Appl. No. 12/465,832, mailed on Oct. 7, 2011, Surajit Chaudhuri, Identifying Synonyms of Entities Using Web Search, 13 pages.
First Office Action mailed Jun. 22, 2016 from China Patent Application No. 201380044316.4, 9 pages.
Fuxman, et al., "Using the Wisdom of the Crowds for Keyword Generation," accessed at <<http:l/ acm.org>>, Proceedings of the 17th International Conference on World Wide Web, 2008, pp. 61-70.
Fuxman, et al., "Using the Wisdom of the Crowds for Keyword Generation," accessed at >, Proceedings of the 17th International Conference on World Wide Web, 2008, pp. 61-70.
Gale, et al., "One Sense Per Discourse," retrieved at <<http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=8EA215CD07B134CA243A22FF6DDA2871?doi=10.1.1.178.2723&rep=rep1&type=pdf>>, Proceedings of the Workshop on Speech and Natural Language, 1992, pp. 233-237.
Ganjam, et al., " InfoGather: Entity Augmentation and Attribute Discovery by Holistic Matching with Web Tables", SIGMOD '12, May 20-24, 2012, Scottsdale AZ, 12 pages.
Ganjam, et al., U.S. Appl. No. 13/413,179, "Entity Augmentation Service from Latent Relational Data," filed on Mar. 6, 2012, 54 pages.
Ganti, et al., "Entity Catergorization Over Large Document Collections," retrieved at <<http://acm.org>>, Proceedings.Of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2008, pp. 274-282.
Gentile, et al., "Graph-based Semantic Relatedness for Named Entity Disambiguation," retrieved at <<http://staffwww.dcs.shef.ac.uk/people/J.Iria/iria-s3t09.pdf>>, Proceedings of the 1st International Conference on Software, Services and Semantic Technologies (S3T), Oct. 2009, 8 pages.
Gentile, et al., "Graph-based Semantic Relatedness for Named Entity Disambiguation," retrieved at >, Proceedings of the 1st International Conference on Software, Services and Semantic Technologies (S3T), Oct. 2009, 8 pages.
Ghani et al., "Text Mining for Product Attribute Extraction", ACM SIGKDD Explorations Newsletter, vol. 8, Issue 1, Jun. 1, 2006, pp. 41-48.
Ghani, et al., "Text Mining for Product Attribute Extraction", SIGKDD Explorations, vol. 8, Issue 1, Jun. 2006, 8 pages.
Gligorov, et al., "Using Google Distance to Weight Approximate Ontology Matches," retrieved at <<http://www.cs.vu.nl/˜frankh/postscript/BNAIC07-WWW07.pdf>>, Proceedings of the 16th International Conference on World Wide Web, 2007, 2 pages.
Goggle Play, "WeChat-Android Apps on Google Plan", retrieved at <<https://play.google.com/store/apps/details? id=com.tencent.mm&referrer=utm-source%3Dwechat.com%26utm-medium%3Ddesktop>>, on Nov. 7, 2016, 3 pages.
Google Input Tools, "Supported Languages", retrieved at <<https://www.google.com/inputtools/help/languages.html>>, on Aug. 26, 2016, 5 pages.
Google Input Tools-Home, retrieved at <<http://web.archive.org/web/20120704053911/http://google.com/inputtools/>>, on Jul. 4, 2012, 2 pages.
Gooi, et al., "Cross-Document Coreference on a Large Scale Corpus," retrieved at <<http://acl.idc.upenn.edu/hlt-naacI2004/main/pdf/177-Paper.pdf>>, in HLT-NAACL, 2004, 8 pages.
Gooi, et al., "Cross-Document Coreference on a Large Scale Corpus," retrieved at >, in HLT-NAACL, 2004, 8 pages.
Graupmann. Jens. "Concept-Based Search on Semi-Structured Data Exploiting Mined Semantic Relations," accessed at <<http://www.springerlink.com/content/p7fw8dk70v2x8w4a/fulltext.pdf>>, EDBT 2004 Workshops, LNCS 3268, Eds. W. Lindner, et al., Springer-Verlag, Berlin Heidelberg, 2004, pp. 34-43.
Graupmann. Jens. "Concept-Based Search on Semi-Structured Data Exploiting Mined Semantic Relations," accessed at >, EDBT 2004 Workshops, LNCS 3268, Eds. W. Lindner, et al., Springer-Verlag, Berlin Heidelberg, 2004, pp. 34-43.
Guo, et al., "Named Entity Recognition in Query," retrieved at <<http://acm.org>>, Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2009, pp. 267-274.
Guo, et al., "Named Entity Recognition in Query," retrieved at >, Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2009, pp. 267-274.
Gupta, et al., "Answering Table Augmentation Queries from Unstructured Lists on the Web", Proc. VLDB Endowment, Aug. 24-28, 2009, Lyon France, 12 pages.
Han et al., "Mining Frequent Patterns without Candidate Generation" Proceedings of the 2000 ACM SIGMOD international Conference on Management of Data, pp. 1-12, 2000.
Han, et al., "Collective Entity Linking in Web Text: A Graph-Based Method," retrieved at <<http://www.nlpr.ia.ac.cn/2011papers/gjhy/gh133.pdf>>, Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2011, pp. 765-774.
Han, et al., "Collective Entity Linking in Web Text: A Graph-Based Method," retrieved at >, Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2011, pp. 765-774.
Han, et al., "Data Mining: Concepts and Techniques," retrieved on at <<http://www.iriit.edu/˜dagr/DataMiningCourse/Spring2001/BookNotes/4lang.pdf>>, slide presentation, Intelligent Database Systems Research Lab, School of Computing Science, Simon Fraser University, Canada, Jun. 17, 2001, 5 pages.
Han, et al., "Data Mining: Concepts and Techniques," retrieved on at >, slide presentation, Intelligent Database Systems Research Lab, School of Computing Science, Simon Fraser University, Canada, Jun. 17, 2001, 5 pages.
Han, et al., "Named Entity Disambiguation by Leveraging Wikipedia Semantic Knowledge," retrieved at <<http://avss2012.org/cip/ZhaoJunPublications/paper/CIKM2009.NED.pdf>>, Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2009, 10 pages.
Han, et al., "Named Entity Disambiguation by Leveraging Wikipedia Semantic Knowledge," retrieved at >, Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2009, 10 pages.
Haveliwala, et al., "Topic-Sensitive Pagerank", in WWW 2002, May 7-11, 2002, Honolulu, Hawaii, 10 pages.
He, et al., "Seisa: Set Expansion by Iterative Similarity Aggregation", in WWW, Mar. 28-Apr. 1, 2011, Hyderabad, India, 10 pages.
He, et al., "Statistical Schema Matching Across Web Query Interfaces", in SIGMOD 2003, Jun. 9-12, 2003, San Diego, CA, 12 pages.
Hipp, et al., "Algorithms for Association Rule Mining- A General Survey and Comparison", ACM SIGKDD Explorations Newsletter, vol. 2, Issue 1, Jun., 2000, pp. 58-64.
Hoffart, et al., "Robust Disambiguation of Named Entities in Text," retrieved at <<http://aclweb.org/anthology-new/D/D11/D11-1072.pdf>>, Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Jul. 2011, pp. 782-792.
Hoffart, et al., "Robust Disambiguation of Named Entities in Text," retrieved at >, Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Jul. 2011, pp. 782-792.
Hu, Wen-Chu, "ApproxSeek: Web Document Search Using Approximate Matching," retrieved at http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=F776964F00B448D5445A84C3528F0E83? doi=10.1.1.44.8602&rep=rep1Mype=pdf>>, Proceedings of the Fifth International Conference on Computer Science and Informatics, Sep. 1999, 5 pages.
Intemational Preliminary Report on Patentability mailed Jan. 8, 2015 from PCT Patent Application No. PCT/CN2012/077888, 6 pages.
Intemational Search Report and Written Opinion mailed Apr. 4, 2013 from PCT Patent Application No. PCT/CN2012/077888, 7 pages.
International Preliminary Report on Patentability and Written Opinion mailed Feb. 24, 2015 from PCT Patent Application No. PCT/US2013/055500, 9 pages.
International Preliminary Report on Patentability mailed Jan. 8, 2015 from PCT Patent Application No. PCT/CN2012/077888, 6 pages.
International Search Report and Written Opinion mailed Apr. 4, 2013 from PCT Patent Application No. PCT/CN2012/077888, 7 pages.
International Search Report and Written Opinion mailed Feb. 13, 2014 from PCT Patent Application No. PCT/US2013/055500, 12 pages.
International Search Report and Written Opinion, PCT/US2013/027203, mailed Jun. 26, 2013, 10 pages.
Isard, et al., "Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks," retrieved at <<http://research.microsoft.com/pubs/63785/eurosys07.pdf>>, EuroSys 2007, 2007, 14 pages.
Isard, et al., "Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks," retrieved at >, EuroSys 2007, 2007, 14 pages.
Jain, et al., "Domain-Independent Entity Extraction from Web Search Query Logs," retrieved at <<http://www.marcopennacchiotti.com/pro/publications/WWVV-2011-2.pdf>>, Proceedings of the 20th International Conference Companion on World Wide Web, Mar. 28, 2011, pp. 63-64.
Jain, et al., "Domain-Independent Entity Extraction from Web Search Query Logs," retrieved at >, Proceedings of the 20th International Conference Companion on World Wide Web, Mar. 28, 2011, pp. 63-64.
Jiang et al., "On the Development of Text Input Method-Lessons Learned," retrieved at <<http://arxiv.org.ftp.arxiv/papers/0704/0704.3665.pdf>>, on Apr. 2007, 10 pages.
Jones, et al., "Generating Query Substitutions," retrieved at <<http://acm.org>>, Proceedings of the 15th International Conference on World Wide Web, 2006, pp. 387-396.
Jones, et al., "Generating Query Substitutions," retrieved at >, Proceedings of the 15th International Conference on World Wide Web, 2006, pp. 387-396.
Kasliwal, et al., "Text Mining in Biomedical Literature", Retrieved at <<http://www.cse.iilb.ac.ini-sourabh/seminar/ final/seminar-report>>, Retrieved at least as early as Mar. 9, 2009, Department of Computer Science and Engineering, Indian Institute of Technology, Bombay, India, 27 pages.
Kasliwal, et al., "Text Mining in Biomedical Literature", Retrieved at >, Retrieved at least as early as Mar. 9, 2009, Department of Computer Science and Engineering, Indian Institute of Technology, Bombay, India, 27 pages.
Kim, et al., "A comparison of collocation-based similarity measures in query expansion," Information Processing and Management, No. 35, 1999, pp. 19-30.
Klapaftis, et al., "Google & WordNet Based Word Sense Disambiguation", Proceedings of the 22nd ICML Workshop on Learning & Extending Ontologies, Bonn, Germany, 2005, 5 pages.
Koudas, et al., "Record Linkage: Similarity Measures and Algorithms," retrieved at <<http://disi.unitn.it/˜p2p/RelatedWork/Matching/aj-recordLinkage-06.pdf>>, presentation dated Sep. 23, 2006, 130 pages.
Koudas, et al., "Record Linkage: Similarity Measures and Algorithms," retrieved at >, presentation dated Sep. 23, 2006, 130 pages.
Kowalski, et al., "Information Storage and Retrieval Systems", 2002 Kluwer Academic Publishers, pp. 1-36.
Kulkarni, et al., "Collective Annotation of Wikipedia Entities in Web Text," retrieved at <<http://www.cc.gatech.edu/˜zha/CSE8801/query-annotation/p457-kulkarni.pdf>>, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, 2009, pp. 457-465.
Kulkarni, et al., "Collective Annotation of Wikipedia Entities in Web Text," retrieved at >, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, 2009, pp. 457-465.
Lafferty et al., "Conditional Random Fields: Probalistic Models for Segmenting and Labeling Sequence Data", Proceedings of the Eighteenth International Conference on Machine Learning, pp. 282-289, 2001.
Limaye, et al., "Annotating and Searching Web Tables Using Entities, Types and Relationships", VLDB Endowment, vol. 3, No. 1, 2010, 10 pages.
Lin, Dekang, "Automatic Retrieval and Clustering of Similar Words", Proceedings of the 17th international Conference on Computational Linguistics, vol. 2, pp. 768-774, 1998.
Loser et al., "Augementing Tables by Self-Supervised Web Search", M. Castellanos, U. Dayal, and V. Markl (Eds.): BIRTE 2010, LNBIP 84, pp. 84-99, 2011.
Madhavan, et al., "Corpus-based Schema Matching", 21st International Conference on Data Engineering, Apr. 5-8, 2005, 12 pages.
Madhavan, et al., "Generic Schema Matching with Cupid", 27th VLDB Conference, 2001, Roma, Italy, 10 pages.
Malekian, et al., "Optimizing Query Rewrites for Keyword-Based Advertising," accessed at <<http:l/ acm.org>>, Proceedings of the 9th ACM Conference on Electronic Commerce, Jul. 2008, pp. 10-19.
Malekian, et al., "Optimizing Query Rewrites for Keyword-Based Advertising," accessed at >, Proceedings of the 9th ACM Conference on Electronic Commerce, Jul. 2008, pp. 10-19.
Mann, et al., "Unsupervised Personal Name Disambiguation," retrieved at <<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.10.7097&rep=rep1&type=pdf>>, Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, vol. 4, 2003, 8 pages.
Mann, et al., "Unsupervised Personal Name Disambiguation," retrieved at >, Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, vol. 4, 2003, 8 pages.
Manning et al., Foundations of Statistical Natural Language Processing, the MIT Press, 1999, 717 pages.
Manning et al., Foundations of Statistical Natural Language Processing, the MIT Press, 1999.
MDGB, "Enabling Pinyin input on your computer", retrieved at <<https://www.facebook.com/notes/mdbg/enabling-pinyin-input-on-your-computer/419757796861/>>, on Aug. 26, 2016, published Sep. 4, 2010, 2 pages.
Mei, et al., "Query Suggestion Using Hitting Time," retrieved at <<http://ACM.org>>, Proceedings of the 17th ACM Conference on Information and Knowledge Management, Oct. 2008, pp. 469-477.
Mei, et al., "Query Suggestion Using Hitting Time," retrieved at >, Proceedings of the 17th ACM Conference on Information and Knowledge Management, Oct. 2008, pp. 469-477.
Michelson et al., "Mining Heterogeneous Transformations for Record Linkage", IIWeb, pp. 68-73, AAAI Press, 2007.
Mihalcea, et al., "Wikify! Linking Documents to Encyclopedic Knowledge," retrieved at <<http://www.cse.unt.edu/˜rada/papers/mihalcea.cikm07.pdf>>, Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, 2007, 9 pages.
Mihalcea, et al., "Wikify! Linking Documents to Encyclopedic Knowledge," retrieved at >, Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, 2007, 9 pages.
Mihalcea, Rada, "Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling," retrieved at <<http://www.aclweb.org/anthology-new/H/H05/H05-1052.pdf>>, Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), Oct. 2005, pp. 411-418.
Mihalcea, Rada, "Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling," retrieved at >, Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), Oct. 2005, pp. 411-418.
Miller, George A., "Wordnet: A Lexical Database for English," accessed at <<http://acm.org>>, Communications of the ACM, vol. 38, No. 11, Nov., 1995, pp. 39-41.
Miller, George A., "Wordnet: A Lexical Database for English," accessed at >, Communications of the ACM, vol. 38, No. 11, Nov., 1995, pp. 39-41.
Milne, et al., "Learning to Link with Wikipedia," retrieved at <<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.148.3617&rep=rep1&type=pdf>>, Proceedings of the 17th ACM Conference on Information and Knowledge Management, 2008, 10 pages.
Milne, et al., "Learning to Link with Wikipedia," retrieved at >, Proceedings of the 17th ACM Conference on Information and Knowledge Management, 2008, 10 pages.
Minjuan, et al., "Pseudo-Relevance Feedback Driven for XML Query Expansion," retrieved at <<http://www.aicit.org/jcit/ppl/JCIT0509-15.pdf>>, Journal of Convergence Information Technology, vol. 5, No. 9, Nov. 2010, pp. 146-156.
Minjuan, et al., "Pseudo-Relevance Feedback Driven for XML Query Expansion," retrieved at >, Journal of Convergence Information Technology, vol. 5, No. 9, Nov. 2010, pp. 146-156.
Mozur, Paul, "Seeking Access to Facebook in China, Zuckerberg Courts Risks", The New York Times, retrieved at <<http://www.nytimes.com/2016/03/21/business/seeking-access-to-facebook-in-china-zuckerberg-courts-risks.html>>, on Aug. 26, 2016, published Mar. 20, 2016, 3 pages.
Nadeau, et al., "Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity," retrieved at <<http://cogprints.org/5025/1/NRC-48727.pdf>>, Proceedings of 19th Conference of the Canadian Society for Computational Studies of Intelligence, Jun. 7, 2006, pp. 266-277.
Nadeau, et al., "Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity," retrieved at >, Proceedings of 19th Conference of the Canadian Society for Computational Studies of Intelligence, Jun. 7, 2006, pp. 266-277.
Naptali et al., "Integration of Topic Dependent Class Language Model and Cache-based Unigram Scaling", Interspeech, Feb. 26-27, 2010, 7 pages.
Navarro, Gonzalo, "A Guided Tour to Approximate String Matching," retrieved at <<http://ACM.org>>, ACM Computing Surveys, vol. 33, Issue 1, Mar. 2001, pp. 31-88.
Navarro, Gonzalo, "A Guided Tour to Approximate String Matching," retrieved at >, ACM Computing Surveys, vol. 33, Issue 1, Mar. 2001, pp. 31-88.
Nie et al., "Unknown Word Detection and Segmentation of Chinese using Statistical and heuristic Knowledge," Communications of Colips, 1995, vol. 5, No. 1-2, pp. 47-57, 11 pages.
Non-Final Office Action dated Apr. 5, 2016 from U.S Appl. No. 14/864,430, 15 pages.
Non-Final Office Action mailed Apr. 2, 2013 from U.S. Appl. No. 13/413,179, 30 pages.
Non-Final Office Action mailed Apr. 29, 2015 from U.S. Appl. No. 12/779,964, 26 pages.
Non-Final Office Action mailed Jul. 30, 2015 from U.S. Appl. No. 13/527,601, 12 pages.
Non-Final Office Action mailed May 12, 2015 from U.S. Appl. No. 13/594,473, 6 pages.
Non-Final Office Action mailed Oct. 13, 2016 from U.S. Appl. No. 13/635,274, 25 pages.
Non-Final Office Action mailed Sep. 11, 2014 from U.S. Appl. No. 13/594,473, 13 pages.
Non-Final Office Action mailed Sep. 28, 2015 from U.S Appl. No. 13/635,274, 21 pages.
Non-Final Office Action mailed Sep. 28, 2015 from U.S. Appl. No. 13/635,274, 21 pages.
Non-Final Office Action of U.S. Appl. No. 12/465,832, mailed on May 23, 2011, Surajit Chaudhuri, Identifying Synonyms of Entities Using Web Search, 19 pages.
Notice of Allowance and Examiner Initiated Interview mailed Jan. 14, 2015 from U.S. Appl. No. 13/594,473, 11 pages.
Notice of Allowance and Examiner Initiated Interview Summary mailed Jun. 17, 2015 from U.S. Appl. No. 13/413,179, 16 pages.
Notice of Allowance and Examiner Initiated Interview Summary mailed Nov. 6, 2015 from U.S. Appl. No. 12/779,964, 17 pages.
Notice of Allowance Dated Jan. 21, 2014 from U.S. Appl. No. 13/487,260, Filed Jun. 4, 2012, Inventor Tao Cheng.
Notice of Allowance mailed Apr. 7, 2015 from U.S. Appl. No. 12/235,635, 16 pages.
Notice of Allowance mailed Aug. 26, 2015 from U.S. Appl. No. 13/594,473, 8 pages.
Notice of Allowance Mailed Jun. 6, 2013 from U.S. Appl. No. 121478,120, 12 pages.
Notice of Allowance mailed May 22, 2015 from U.S. Appl. No. 12/235,635, 16 pages.
Notice of Allowance mailed Nov. 18, 2016 from U.S. Appl. No. 12/779,964, 20 pages.
Notice of Allowance mailed Oct. 17, 2013 from U.S. Appl. No. 13/487,260, 11 pages.
Notice on the First Office Action mailed Apr. 13, 2016 from China Patent Application No. 201380013249.X, 15 pages.
Office Action of U.S. Appl. No. 12/465,832, mailed on Apr. 10, 2012, Surajit Chaudhuri, Identifying Synonyms of Entities Using Web Search, 16 pages.
Office Action of U.S. Appl. No. 12/465,832, mailed on Sep. 19, 2012, Surajit Chaudhuri, Identifying Synonyms of Entities Using Web Search, 18 pages.
Office Action of U.S. Appl. No. 12/478,120, mailed on Feb. 27, 2012, Surajit Chaudhuri, "Identifying Synonyms of Entities Using a Document Collection", 18 pages.
Office Action of U.S. Appl. No. 12/478,120, mailed on Nov. 3, 2011, Surajit Chaudhuri, Identifying Synonyms of Entities Using a Document Collection, 8 pages.
Page, et al., "The Pagerank Citation Ranking: Bringing Order to the Web". Technical Report, Stanford InfoLab, 1998, 17 pages.
Pantel, et al., "Web-Scale Distributional Similarity and Entity Set Expansion," retrieved at <<http://www.aclweb.org/anthology/D/D091D09-1098.pdf>>, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Aug. 2009, pp. 938-947.
Pantel, et al., "Web-Scale Distributional Similarity and Entity Set Expansion," retrieved at >, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Aug. 2009, pp. 938-947.
Pasca, Marius, "Weakly-Supervised Discovery of Named Entities Using Web Search Queries," retrieved at <<http://www.acm.org>>, Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, Nov. 2007, pp. 683-690.
Pasquier. et al.. "Efficient Mining of Association Rules Using Closed Itemset Lattices". 1999 Elsevier Science Ltd., vol. 24, No. 1, pp. 25-46.
Peters, et al., "Folksonomy and Information Retrieval," retrieved at <<http://wwwalt.phil-fak.uni-duesseldorf.de/infowiss/admin/public-dateien/files/1/1194344432asist-am07.pdf>>, Proceedings of the 7oth ASIS&T Annual Meeting, vol. 44, 2007, 33 pages.
Rahm, et al., "A Survey of Approaches to Automatic Schema Matching", the VLDB Journal, 2001, 24 pages.
Requirement for Restriction/Election mailed Apr. 25, 2014 from U.S. Appl. No. 13/594,473, 6 pages.
Requirement for Restriction/Election mailed Sep. 12, 2011 from U.S. Appl. No. 12/478,120, 6 pages.
Response and After Final Consideration Program Request filed Apr. 27, 2016 to the Final Office Action mailed Mar. 11, 2016 from U.S. Appl. No. 13/527,602, 14 pages.
Response filed Apr. 14, 2015 to the Non-Final Office Action mailed Jan. 15, 2015 from U.S. Appl. No. 13/527,601, 12 pages.
Response filed Apr. 8, 2015 to Examination Report mailed Mar. 11, 2015 from European Patent Application No. 13757813.4, 17 pages.
Response filed Aug. 10, 2016 to the Final Office Action mailed Jun. 13, 2016 from U.S. Appl. No 13/635,274, 25 pages.
Response filed Aug. 25, 2014 to Requirement for Restriction/Election mailed Apr. 25, 2014 from U.S. Appl. No. 13/594,473, 10 pages.
Response filed Dec. 30, 2016 to the Non-Final Office Action mailed Oct. 13, 2016 from U.S Appl. No. 13/635,274, 18 pages.
Response filed Jan. 28, 2016 to Non-Final Office Action mailed Sep. 28, 2016 from U.S. Appl. No. 13/635,274, 24 pages.
Response filed Jul. 21, 2015 to Non-Final Office Action mailed Apr. 29, 2015 from U.S. Appl. No. 12/779,964, 11 pages.
Response filed Jun. 24, 2016 to Final Office Action mailed Mar. 11, 2016 from U.S. Appl. No. 13/527,601, 14 pages.
Response filed Jun. 5, 2014 to Final Office Action mailed Dec. 5, 2013 from U.S. Appl. No. 13/413,179, 19 pages.
Response filed Mar. 25, 2012 to Final Office Action mailed Oct. 25, 2011 from U.S. Appl. No. 12/235,635, 29 pages.
Response filed Nov. 30, 2015 to the Non-Final Office Action mailed Jul. 30, 2015 from U.S Appl. No. 13/527,601, 16 pages.
Response filed Oct. 12, 2011 to Requirement for Restriction/Election mailed Sep. 12, 2011 from U.S. Appl. No. 12/478,120, 4 pages.
Response filed Sep. 3, 2013 to Non-Final Office Action mailed Apr. 2, 2013 from U.S. Appl. No. 13/413,179, 15 pages.
Response to Restriction Requirement filed Aug. 27, 2015 from U.S Appl. No. 13/635,274, 9 pages.
Response to Restriction Requirement filed Aug. 27, 2015 from U.S. Appl. No. 13/635,274, 9 pages.
Restriction Requirement mailed Jul. 2, 2015 from U.S Appl. No. 13/635,274, 6 pages.
Rodriguez et al. "Determining Semantic Similarity Among Entity Classes from Different Ontologies", IEEE Transactions on Knowledge and Data Engineering, Mar./Apr. 2003, vol. 15, No. 2, pp. 442-456.
Salier-Hellendag, Isaac, "Facebook open sources rich text editor framework for Draft.js", retrieved at <<https://code.facebook.com/posts/1684092755205505/facebook-open-sources-rich-text-editor-framework-draft-js/>>, on Aug. 26, 2016, published Feb. 26, 2016, 10 pages.
Sander, Ed, "Demo of Weixin/WeChat", retrieved at <<https://www.youtube.com/watch?v=Avp10IZjDN8>>, on Aug. 26, 2016, published Apr. 18, 2013, 2 pages.
Sarawagi, Sunita, "Models and Indices for Integrating Unstructured Data with a Relational Database", In Proceedings of the Workshop on Knowledge Discovery in Inductive Databases, Sep. 20, 2004, 10 pages.
Sarkas, et al., "Structured Annotations of Web Queries," retrieved at <<http://acm.org>>, Proceedings of the 2010 International Conference on Management of Data, Jun. 2010, pp. 771-782.
Sarkas, et al., "Structured Annotations of Web Queries," retrieved at >, Proceedings of the 2010 International Conference on Management of Data, Jun. 2010, pp. 771-782.
Sarmento, et al., "An Approach to Web-scale Named-Entity Disambiguation," accessible at <<http://sigarra.up.pt/feup/publs-pesquisa.show-publ-file?pct-gdoc-id=68610.>>, Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition, 2009, 15 pages.
Schallehn, et al., "Efficient Similarity-based Operations for Data Integration", In Journal of Data & Knowledge Engineering, vol. 48, Issue 3, Aug. 12, 2003, 27 pages.
Schenkel, et al., "Efficient Top-k Querying over Social-Tagging Networks," retrieved at <<http://acm.org>>, Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2008, 8 pages.
Second Office Action mailed Dec. 1, 2016 from China Patent Application No. 201380013249.X, 7 pages.
Smeaton, et al., "Experiments on Incorporating Syntactic Processing of User Queries into a Document Retrieval Strategy," retrieved at <<http://acm.org, Proceedings of the 11th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1988, pp. 31-51.
Strube, et al., "Wikirelatel Computing Semantic Relatedness Using Wikipedia," accessed at <<http://www.dit.unitn.it/-p2p/RelatedWork!Matching/aaai06.pdf>>, AAAI'06, Proceedings of the 21st National Conference on Artificial intelligence, 2006, 6 pages.
Strube, et al., "Wikirelatel Computing Semantic Relatedness Using Wikipedia," accessed at >, AAAI'06, Proceedings of the 21st National Conference on Artificial intelligence, 2006, 6 pages.
Supplemental Amendment filed Mar. 11, 2014 from U.S. Appl. No. 12/235,635, 10 pages.
Tsoukalas, et al., "PLEDS: A Personalized Entity Detection System Based on Web Log Mining Techniques," retrieved at <<http://www2.fas.sfu.ca/pub/cs/techreports/2008/CMPT2008-06.pdf>>, WAIM, Proceedings of the Ninth International Conference on Web-Age information Management, Jul. 2008, pp. 1-23.
Turney, Peter D., accessed at <<http://cogprints.org/1796/1/ECML2001.ps>>, "Mining the Web for Synonyms: PMI- IR versus LSA on TOEFL," EMCL '01, Proceedings of the 12th European Conference on Machine Learning, LNCS, vol. 2167,2001, 12 pages.
Urbansky et al., "Entity Extractions from the Web with WebKnox", V. Snasel et al. (Eds.): Advances in Intelligent Web Mastering-2, AISC 67, pp. 209-218.
Venetis, et al., "Recovering Semantics of Tables on the Web", Proceedings of the VLBD Endowment, vol. 4, Issue 9, Jun. 2011, 10 pages.
Voluntary Amendment filed Feb. 28, 2015 from European Patent Application No. 201380013249.X, 8 pages.
Voluntary Amendment filed Jul. 1, 2015 from China Patent Application No. 201380044316.4, 10 pages.
Wang, et al., "Targeted Disambiguation of Ad-hoc, Homogeneous Sets of Named Entities," retrieved at <<http://acm.org>>, Proceedings of the 21st International Conference on World Wide Web, Apr. 2012, pp. 719-728.
Wang, et al., "Targeted Disambiguation of Ad-hoc, Homogeneous Sets of Named Entities," retrieved at >, Proceedings of the 21st International Conference on World Wide Web, Apr. 2012, pp. 719-728.
Watanabe, et al., "A Graph-based Approach to Named Entity Categorization in Wikipedia Using Conditional Random Fields," retrieved at <<http://www.aclweb.org/anthology-new/D/D07/D07-1068.pdf>>, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jun. 2007, pp. 649-657.
Watanabe, et al., "A Graph-based Approach to Named Entity Categorization in Wikipedia Using Conditional Random Fields," retrieved at >, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jun. 2007, pp. 649-657.
WeChat, captured by the Internet archive on Jan. 14, 2013, at <<https://web.archive.org/web/20130114231337/http://www.wechat.com/en/>>, 2 pages.
Weixin, "WeChat-Free messaging and calling app", retrieved at <<https://translate.google.com/translate?sl=auto&tl=en&js=y&prev=-t&hl=en&ie=UTF-8&u=http%3A%2F%2Fweixin.qq.com%2F&edit-text=&act=url>> (as translated by Google Translate), on Sep. 20, 2016, 1 page.
Wen, et al., "Clustering User Queries of a Search Engine," accessed at <<http://research.microsoft.com/users/jrwen/jrwen-files/publications/QC-WWW10.pdf>>, Proceedings of the 10th International Conference on World Wide Web, 2001, pp. 162-168.
Yakout, et al., "InfoGather: Entity Augmentation and Attribute Discovery by Holistic Matching with Web Tables," retrieved at <<http://acm.org>>, Proceedings of the 2012 International Conference on Management of Data, May 2012, pp. 97-108.
Yin, et al., "Facto: A Fact Lookup Engine Based on Web Tables", in WWW, Mar. 28-Apr. 1, 2011, Hyderabad, India, 10 pages.
Zhai. et al., "A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval," accessed at <<http://acm.org>>, Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2001, 9 pages.
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Radev et al. 2013 The ACL anthology network corpus
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Mayfield et al. 2009 Cross-Document Coreference Resolution: A Key Technology for Learning by Reading.
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