Abstract: In one embodiment, a method includes accessing a first set of posts that includes a number of posts of the online social network posted within a first timeframe. In particular embodiments, each post is associated with one or more topics of a number of topics of the online social network. The method also includes determining a trending topic from the plurality of topics of the online social network; and generating a news-event object associated with the trending topic. In particular embodiments, the news-event object includes a seed post from the first set of posts and a tagging-algorithm for identifying posts matching the trending topic. The method also includes identifying, by the tagging-algorithm, one or more posts from the first set of posts that match the trending topic; and generating one or more search-results modules. In particular embodiments, each search-results module includes one or more search results.

Abstract: In one embodiment, a method includes receiving a search query inputted by a first user, wherein the search query comprises one or more n-grams; calculating a needle-confidence score for the search query that is calculated by a needle-intent classifier based on at least the n-grams of the search query and a language model analysis of the n-grams, and wherein the needle-confidence score represents a probability that the search query was intended as a needle search; classifying the search query as a needle search if the calculated needle-confidence score is above a threshold confidence score; and generating a plurality of search-result modules, each search-result module comprising one or more search results matching the search query, wherein one of the search-result modules is a social module, and wherein the number of search results in the social module is based on the classification of the search query as a needle search.

Abstract: In one embodiment, a method includes receiving, from a client system of a first user, a text query including one or more n-grams, including an ambiguous n-gram. The method includes searching multiple keyword generators to identify one or more keyword suggestions matching the ambiguous n-gram, each keyword generator having a particular type. The method includes calculating, by a particular scoring algorithm for each keyword generator, a keyword score for each identified keyword suggestion. The scoring algorithm includes multiple weighting factors chosen based on the type of the keyword generator. The method includes generating a set of suggested queries including at least a portion of the text query and one or more identified keyword suggestions and filtering suggested queries from the set based on privacy settings associated with the identified keyword suggestions. The method includes sending, to the client system, instructions for presenting one or more of the suggested queries.

Abstract: In one embodiment, a method includes accessing a document, identifying one or more noun phrases in the document by performing a pre-processing on the accessed document, generating, for each identified noun phrase, a list of candidate entities corresponding to the noun phrase, wherein the list of candidate entities is looked up in an entity index using the noun phrase, computing, for each candidate entity corresponding to each identified noun phrase, a confidence score that the noun phrase is intended to reference the candidate entity by analyzing the accessed document by a machine learning model, constructing a pool of mention-entity pairs for the accessed document, filtering the pool of mention-entity pairs by removing each mention-entity pair from the pool based on their computed confidence scores, and storing the post-filtered pool of mention-entity pairs in a data store in association with the accessed document.

Abstract: In one embodiment, a method includes receiving, from a client device of a first user of an online social network, a search query associated with a first topic. The method also includes identifying one or more key-authors associated with the first topic. The method further includes retrieving multiple objects of the online social network matching the search query, where one or more of the retrieved objects are associated with the first topic and are authored by at least one of the identified key-authors. The method also includes generating multiple search-results modules, each search-result module including references to one or more of the retrieved objects. At least one of the search-results modules is a key-authors-module that includes references to one or more of the retrieved objects associated with the first topic that are authored by at least one of the identified key-authors.

Abstract: In one embodiment, a method includes identifying one or more entity candidates matching one or more n-grams in a character string, wherein each entity candidate corresponds to an entity associated with the online social network, each entity candidate being associated with one or more entity features and one or more type features, and wherein each entity candidate is of a particular entity type; calculating, for each entity candidate, a confidence score representing a likelihood that the entity candidate is intended to match the corresponding n-gram, wherein the confidence score is based on: an entity-score based on the one or more entity features and a type-score based on the one or more type features; and assigning an entity candidate to one or more of the n-grams based on the calculated confidence scores corresponding to the n-grams.

Abstract: In one embodiment, a method includes identifying one or more entity candidates matching one or more n-grams in a character string, wherein each entity candidate corresponds to an entity associated with the online social network, each entity candidate being associated with one or more entity features and one or more type features, and wherein each entity candidate is of a particular entity type; calculating, for each entity candidate, a confidence score representing a likelihood that the entity candidate is intended to match the corresponding n-gram, wherein the confidence score is based on: an entity-score based on the one or more entity features and a type-score based on the one or more type features; and assigning an entity candidate to one or more of the n-grams based on the calculated confidence scores corresponding to the n-grams.

Abstract: In one embodiment, a method includes receiving a search query inputted by a first user, wherein the search query comprises one or more n-grams; calculating a needle-confidence score for the search query that is calculated by a needle-intent classifier based on at least the n-grams of the search query and a language model analysis of the n-grams, and wherein the needle-confidence score represents a probability that the search query was intended as a needle search; classifying the search query as a needle search if the calculated needle-confidence score is above a threshold confidence score; and generating a plurality of search-result modules, each search-result module comprising one or more search results matching the search query, wherein one of the search-result modules is a social module, and wherein the number of search results in the social module is based on the classification of the search query as a needle search.

Abstract: In one embodiment, a method includes receiving, from a client system of a first user, a text query including one or more n-grams, including an ambiguous n-gram. The method includes searching multiple keyword generators to identify one or more keyword suggestions matching the ambiguous n-gram, each keyword generator having a particular type. The method includes calculating, by a particular scoring algorithm for each keyword generator, a keyword score for each identified keyword suggestion. The scoring algorithm includes multiple weighting factors chosen based on the type of the keyword generator. The method includes generating a set of suggested queries including at least a portion of the text query and one or more identified keyword suggestions and filtering suggested queries from the set based on privacy settings associated with the identified keyword suggestions. The method includes sending, to the client system, instructions for presenting one or more of the suggested queries.

Abstract: In one embodiment, a method includes receiving from a client system of a first user an unstructured text query. The method includes parsing the text query to identify one or more n-grams. At least one of the n-grams is an ambiguous n-gram. The method includes searching a plurality of keyword generators to identify one or more keyword suggestions matching the ambiguous n-gram. The method further includes calculating a keyword score for each identified keyword suggestions and generating one or more suggested queries including one or more n-grams identified from the text query. The one or more identified keyword suggestions having a keyword score greater than a threshold keyword score. The method includes sending one or more of the suggested queries to the client system of the first user for display.

Abstract: In one embodiment, a method includes accessing a first set of posts that includes a number of posts of the online social network posted within a first timeframe. In particular embodiments, each post is associated with one or more topics of a number of topics of the online social network. The method also includes determining a trending topic from the plurality of topics of the online social network; and generating a news-event object associated with the trending topic. In particular embodiments, the news-event object includes a seed post from the first set of posts and a tagging-algorithm for identifying posts matching the trending topic. The method also includes identifying, by the tagging-algorithm, one or more posts from the first set of posts that match the trending topic; and generating one or more search-results modules. In particular embodiments, each search-results module includes one or more search results.

Abstract: In one embodiment, a method includes receiving, from a client device of a first user of an online social network, a search query associated with a first topic. The method also includes identifying one or more key-authors associated with the first topic. The method further includes retrieving multiple objects of the online social network matching the search query, where one or more of the retrieved objects are associated with the first topic and are authored by at least one of the identified key-authors. The method also includes generating multiple search-results modules, each search-result module including references to one or more of the retrieved objects. At least one of the search-results modules is a key-authors-module that includes references to one or more of the retrieved objects associated with the first topic that are authored by at least one of the identified key-authors.

Abstract: In one embodiment, a method includes receiving from a client system of a first user an unstructured text query. The method includes parsing the text query to identify one or more n-grams. At least one of the n-grams is an ambiguous n-gram. The method includes searching a plurality of keyword generators to identify one or more keyword suggestions matching the ambiguous n-gram. The method further includes calculating a keyword score for each identified keyword suggestions and generating one or more suggested queries including one or more n-grams identified from the text query. The one or more identified keyword suggestions having a keyword score greater than a threshold keyword score. The method includes sending one or more of the suggested queries to the client system of the first user for display.