Techniques are disclosed relating to scoring partial matches between words. In certain embodiments, a method may include receiving a request to determine a similarity between an input text data and a stored text data. The method also includes determining, based on comparing one or more words included in the input text data with one or more words included in the stored text data, a set of word pairs and a set of unpaired words. Further, in response to determining that the set of unpaired words passes elimination criteria, the method includes calculating a base similarity score between the input text data and the stored text data based on the set of word pairs. The method also includes determining a scoring penalty based on the set of unpaired words and generating a final similarity score between the input text data and the stored text data by modifying the base similarity score based on the scoring penalty.

Examples of the present disclosure describe systems and methods for automatically assisting conversations using a graph database. In order to minimize misunderstanding of words and phrases used by participants during a conversation, phrases from the conversation may be received by conversation assistance application as the conversation takes place. Entities may be extracted from the phrase based on natural language recognition according to a domain context of the participant being assisted. One or more tags may be looked up from a graph database, and may be provided to the participant as a list of hashtags related to the conversation. Links to documents may be extracted based on the tags for the participant for viewing during the conversation.

Examples of the present disclosure describe systems and methods for automatically assisting conversations using a graph database. In order to minimize misunderstanding of words and phrases used by participants during a conversation, phrases from the conversation may be received by conversation assistance application as the conversation takes place. Entities may be extracted from the phrase based on natural language recognition according to a domain context of the participant being assisted. One or more tags may be looked up from a graph database, and may be provided to the participant as a list of hashtags related to the conversation. Links to documents may be extracted based on the tags for the participant for viewing during the conversation.

Briefly stated, the invention is directed to retrieving a semantically matched knowledge structure. A question and answer pair is received, wherein the answer is received from a query of a search engine. A question is constraint-matched with the answer based on maximizing a plurality of constraints, wherein at least one of the plurality of the constraints is a similarity score between question and answer, wherein the constraint matching generates a matched sequence. For one or more answer sequences, a subsequence is found that are not parsed as answer slots. Query results are obtained from another search engine based on a combination of the answer or question, and the non-answer subsequence. And a KB based is refined on the query results and the constraint matching and based on a neural network training, for a further subsequent semantic matching, wherein the KB includes a dense semantic vector indication of concepts.

A computing system can include one or more machine-learned models configured to receive context data that describes one or more entities to be named. In response to receipt of the context data, the machine-learned model(s) can generate output data that describes one or more names for the entity or entities described by the context data. The computing system can be configured to perform operations including inputting the context data into the machine-learned model(s). The operations can include receiving, as an output of the machine-learned model(s), the output data that describes the name(s) for the entity or entities described by the context data. The operations can include storing at least one name described by the output data.

A computing system can include one or more machine-learned models configured to receive context data that describes one or more entities to be named. In response to receipt of the context data, the machine-learned model(s) can generate output data that describes one or more names for the entity or entities described by the context data. The computing system can be configured to perform operations including inputting the context data into the machine-learned model(s). The operations can include receiving, as an output of the machine-learned model(s), the output data that describes the name(s) for the entity or entities described by the context data. The operations can include storing at least one name described by the output data.

Due to the high language use variability in real-life, manual construction of semantic resources to cover all synonyms is prohibitively expensive and may result in limited coverage. Described herein are systems and methods that automate the process of synonymy resource development, including both formal entities and noisy descriptions from end-users. Embodiments of a multi-task model with hierarchical task relationship are presented that learn more representative entity/term embeddings and apply them to synonym prediction. In model embodiments, a skip-gram word embedding model is extended by introducing an auxiliary task “neighboring word/term semantic type prediction” and hierarchically organize them based on the task complexity. In one or more embodiments, existing term-term synonymous knowledge is integrated into the word embedding learning framework. Embeddings trained from the multi-task model embodiments yield significant improvement for entity semantic relatedness evaluation, neighboring word/term semantic type prediction, and synonym prediction compared with baselines.

A speech-processing system configured to determine entities corresponding to ambiguous words such as anaphora (“he,” “she,” “they,” etc.) included in an utterance. The system may associate incoming utterances with a speaker identification (ID), device ID, and other data. The system then tracks entities referred to in utterances so that if a later utterance includes an ambiguous entity reference, the system may take the speaker ID, device ID, etc. from the ambiguous reference, along with the text of the utterance and other data, and compare that information to previously mentioned entities (or other entities that may be relevant) to identify the entity mentioned in the ambiguous statement. Once the entity is determined, the system may then complete command processing of the utterance using the identified entity.

A speech-processing system configured to determine entities corresponding to ambiguous words such as anaphora (“he,” “she,” “they,” etc.) included in an utterance. The system may associate incoming utterances with a speaker identification (ID), device ID, and other data. The system then tracks entities referred to in utterances so that if a later utterance includes an ambiguous entity reference, the system may take the speaker ID, device ID, etc. from the ambiguous reference, along with the text of the utterance and other data, and compare that information to previously mentioned entities (or other entities that may be relevant) to identify the entity mentioned in the ambiguous statement. Once the entity is determined, the system may then complete command processing of the utterance using the identified entity.

Systems and methods are provided for performing operations including: receiving, by a messaging application, input that selects an option to generate a message using an avatar with a caption; presenting, by the messaging application, the avatar and a caption entry region proximate to the avatar; populating, by the messaging application, the caption entry region with a text string comprising one or more words; determining, by the messaging application, context based on the one or more words in the text string; and modifying, by the messaging application, an expression of the avatar based on the determined context.