Facebook, Inc.

Stanislav Funiak

Juan Miguel Pino

Gaurav Lochan

Mridul Malpani

Mark E Zuckerberg

Steven Jay Young

Arun Vijayvergiya

Ryan David Mack

Raylene Kay Yung

Paul M McDonald

Serkan Piantino

Jeff Huang

Daniel Klatzko Gibson

Ryan Case

Zizhuang Yang

Jonathan D Chang

Fei Huang

Deepayan Chakrabarti

Amaç Herdagdelen

Joshua Wiseman

Sofus Attila Macskassy

Aditya Pal

Josh Wiseman

Daniel Matthew Merl

Seyoung Park

Embodiments are disclosed for transliterating text entries across different script systems. A method according to some embodiments includes steps of: receiving an input string in a first script system input using a keyboard; segmenting, using a probabilistic model, the input string into phonemes that correspond to characters or sets of characters in a second script system; converting the phonemes in the first script system into the characters or sets of characters in the second script system, the characters or sets of characters forming a word or a word prefix in the second script system; and outputting the word or the word prefix in the second script system.

Transliteration of text entry across scripts

Embodiments are disclosed for transliteration decoding using a tree structure. A method according to some embodiments includes steps of: generating a tree structure for an input string in a first script system, the tree structure including nodes representing segments of the input string; identifying segmentation candidates for the input string based on paths of the tree structure, the segmentation candidates segmenting the input string into character groups; selecting a segmentation candidate based on probabilities of the segmentation candidates predicted by a probabilistic model; segmenting the input string into character groups that correspond to characters in a second script system; decoding the character groups in the first script system into the characters in the second script system, the characters forming a word or a word prefix in the second script system; and outputting the word or the word prefix in the second script system.

Transliteration decoding using a tree structure

The invention provides a display interface in a social networking system that enables the presentation of information related to a user in a timeline or map view. The system accesses information about a user of a social networking system, including both data about the user and social network activities related to the user. The system then selects one or more of these pieces of data and/or activities from a certain time period and gathers them into timeline units based on their relatedness and their relevance to users. These timeline units are ranked by relevance to the user, and are used to generate a timeline or map view for the user containing visual representations of the timeline units organized by location or time. The timeline or map view is then provided to other users of the social networking system that wish to view information about the user.

Selecting social networking system user information for display via a timeline interface

A system for identifying language(s) for content items is disclosed. The system can identify different languages for content item words segments by identifying segment languages that maximize a probability across the segments. The probability can be a combination of: an author's likelihood for the language identified for the first word; a combination of transition frequencies for selected languages identified for words, the transition frequencies indicating likelihoods that a transition occurred to the selected language from the previous word's language; and a combination of observation probabilities indicating, for a given word in the content item, a likelihood the given word is in the identified language. For an in-vocabulary word, the observation probabilities can be based on learned probability for that word. For an out-of-vocabulary word, the probability can be computed by breaking the word into overlapping n-grams and computing combined learned probabilities that each n-gram is in the given language.

Identifying multiple languages in a content item

At least one embodiment of this disclosure includes a method of inferring attribute labels for a user in a social networking system based on the user's social connections and user-specified attribute labels in the social networking system. The method can include: establishing variational equations based on attribute labels of nodes in an ego network in a social graph of a social networking system; determining likelihood scores for at least a portion of the attribute labels of neighboring nodes from a focal user node in the ego network based on user-specified attribute labels from the social networking system; and calculating probability distributions of possible attribute labels for the focal user node of the ego network based on the variational equations and the likelihood scores.

Label inference in a social network

Growing community of inventors

Lawrence, KS, United States of America

Stanislav Funiak