Microsoft Technology Licensing, LLC

Yunbo Cao

Hang Li

Chin-Yew Lin

Jun Xu

Jianfeng Gao

Xinying Song

Zhiyuan Chen

Dmitriy Meyerzon

Tie-Yan Liu

Ruihua Song

Shuming Shi

Pei Yue

Benjamin Daniel Martin

ZhaoHui Tang

Olivier Ribet

Described herein are techniques for extracting data records containing user-generated content from documents. The documents may be processed into document trees in which sub-trees represent the data records of the document. Domain constraints may be used to locate structured portions of the document tree. For example, anchor trees may be located as being sets of sibling sub-trees with similar tag paths that contain the domain constraints. The anchor trees may then be used to determine a record boundary (e.g., the start offset and length) of the data records. Finally, the data records may be extracted based on the anchor trees and the record boundaries.

Domain constraint path based data record extraction

This disclosure describes, in part, techniques for operating a search query user interface to allow seamless creating, editing and/or refining of a search query using various interactive functions. The techniques described herein may display a search query divided into segments. A selection of a segment of the search query may then be received. One or more alternatives to the selected segment may then be presented. Next, a selection of one of the presented alternative may be received. As a result, the search query may be altered using the selected alternative. Furthermore, the techniques described herein allow a user to operate on a search query using query substitution, expansion, association and/or history functions.

Search query user interface

Embodiments for a Mining Data Records based on Anchor Trees (MiBAT) process are disclosed. In accordance with at least one embodiment, the MiBAT process extracts data records containing user-generated content from web documents. The web document is processed into a Document Object Model (DOM) tree in which sub-trees of the DOM tree represent the data records of the web document. Domain constraints are used to locate structured portions of the DOM tree. Anchor trees are then located as being sets of sibling sub-trees which contain the domain constraints. The anchor trees are then used to determine a record boundary (i.e. the start offset and length) of the data records. Finally, the data records are extracted based on the anchor trees and the record boundaries.

Domain constraint based data record extraction

A two stage model identifies individuals having knowledge in a subject matter area relevant to a query. A relevance model receives a query and identifies documents, or other information, relevant to the query. A co-occurrence model identifies individuals, in the retrieved documents, related to the subject matter of the query. Individuals identified can be scored by combining scores from the relevance model and the co-occurrence model and output in a rank ordered list.

Two stage search

Some implementations disclosed herein provide techniques and arrangements to train a blocking scheme using both labeled data and unlabeled data. For example, training the blocking scheme may include iteratively: learning a conjunction, identifying first matches in the labeled data and the unlabeled data that are uncovered by the conjunction, and identifying second matches in the labeled data and the unlabeled data that are covered by the conjunction. The conjunction learned in each iteration may be combined using a disjunction. A search engine may use the search engine when searching for records that match an entity.

Record linkage based on a trained blocking scheme

A question search system provides a collection of questions having words for use in evaluating the utility of the questions based on a language model. The question search system calculates n-gram probabilities for words within the questions of the collection. The n-gram probability of a word for a sequence of n&#x2212;1 words indicates the probability of that word being next after that sequence in the collection of questions. The n-gram probabilities for the words of the collection represent the language model of the collection. The question search system calculates a language model utility score for each question within a collection that indicates the likelihood that a question is repeatedly asked by users. The question search system derives the language model utility score for a question from the n-gram probabilities of the words within that question.

Determining utility of a question

A method and system for determining the relevance of questions to a queried question based on topics and focuses of the questions is provided. A question search system provides a collection of questions with topics and focuses. Upon receiving a queried question, the question search system identifies a queried topic and queried focus of the queried question. The question search system generates a score indicating the relevance of a question of the collection to the queried question based on a language model of the topic of the question and a language model of the focus of the question.

Searching questions based on topic and focus

A method and system for presenting questions that are relevant to a queried question based on clusters of topics and clusters of focuses of the questions is provided. A question search system provides a collection of questions. Each question of the collection has an associated topic and focus. Upon receiving a queried question, the question search system identifies questions of the collection that may be relevant to the queried question and generates a score or ranking indicating relevance of the identified questions. The question search system clusters the identified questions into topic clusters of questions with similar topics. The question search system may also cluster the questions within each topic cluster into focus clusters of questions with similar focuses.

Clustering question search results based on topic and focus

In a question answering system, the system identifies a type of question input by a user. The system then generates answer summaries that summarize answers to the input question in a format that is determined based on the type of question asked by the user. The answer summaries are output, in the corresponding format, in answer to the input question.

Question type-sensitive answer summarization

A method of processing information is provided. The method includes collecting text strings of definition candidates from a data source. The definition candidates are ranked based on the text strings.

Ranking and accessing definitions of terms

A typed separable mixture model is used to mine associative relationships between sets of objects. Instead of modeling only one type of co-occurrence among the sets of objects, the typed separable mixture model can model multiple different types of co-occurrences among more than two sets of objects, and co-occurrences that exist in different contexts.

Mining latent associations of objects using a typed mixture model

A query and a factoid type selection are received from a user. An index of passages, indexed based on factoids, is accessed and passages that are related to the query, and that have the selected factoid type, are retrieved. The retrieved passages are ranked and provided to the user based on a calculated score, in rank order.

Training a ranking component

Factoid-based searching

A method of finding documents. A method of finding documents comprising, ranking documents according to relevance to form a ranked relevance list, ranking documents according to type to form a ranked type list, and interpolating the ranked relevance list and the ranked type list to form a list of documents ranked by relevance and type.

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Beijing, China

Yunbo Cao