Methods and systems for entity recognition and disambiguation using natural language processing techniques are provided. Example embodiments provide an entity recognition and disambiguation system (ERDS) and process that, based upon input of a text segment, automatically determines which entities are being referred to by the text using both natural language processing techniques and analysis of information gleaned from contextual data in the surrounding text. In at least some embodiments, supplemental or related information that can be used to assist in the recognition and/or disambiguation process can be retrieved from knowledge repositories such as an ontology knowledge base. In one embodiment, the ERDS comprises a linguistic analysis engine, a knowledge analysis engine, and a disambiguation engine that cooperate to identify candidate entities from a knowledge repository and determine which of the candidates best matches the one or more detected entities in a text segment using context information.

Nlp-based entity recognition and disambiguation

Methods, systems and software are described for analyzing micro-blog messages to detect abnormal activity of interest. The system includes a clusterer for clustering micro-blog messages received over a first period of time, a classifier for scoring the clustered messages; a knowledge base, a rule generator for generating classification rules from the knowledge base; and a matcher for matching the scored messages to information requests. Methods for operating the system and its components are described.

Methods, apparatus and software for analyzing the content of micro-blog messages

Techniques for content recommendation are described. Some embodiments provide a content recommendation system (“CRS”) configured to recommend content items that are related to a collection of entities. A content item may be considered related to a collection of entities based on various factors, including whether and how often the article references or otherwise covers the entities of the collection, the size of the article, other entities that are covered by the article but that are not in the collection, article recency, or article credibility. Recommending content items may also or instead include determining entities that are related to a collection. An entity can be considered related to a collection based on various factors, such as whether the entity is of the same or similar type to entities of the collection, or whether the entity appears in some article in a relationship with one or more entities of the collection.

Content recommendation based on collections of entities

One common application of text mining is event extraction, which encompasses deducing specific knowledge concerning incidents referred to in texts. Event extraction can be applied to various types of written text, e.g., (online) news messages, blogs, and manuscripts. This literature survey reviews text mining techniques that are employed for various event extraction purposes. It provides general guidelines on how to choose a particular event extraction technique depending on the user, the available content, and the scenario of use.

/pdf/an-overview-of-event-extraction-from-text-v53006fo57.pdf

An overview of event extraction from text

Modeling and simulation have long been dominated by equation-based approaches, until the recent advent of agent-based approaches. To curb the resulting complexity of models, Axelrod promoted the KISS principle: " Keep It Simple, Stupid ". But the community is divided and a new principle appeared: KIDS, " Keep It Descriptive Simple ". Richer models were thus developed for a variety of phenomena, while agent cognition still tends to be modelled with simple reactive particle-like agents. This is not always appropriate, in particular in the social sciences trying to account for the complexity of human behaviour. One solution is to model humans as BDI agents, an expressive paradigm using concepts from folk psychology, making it easier for modellers and users to understand the simulation. This paper provides a methodological guide to the use of BDI agents in social simulations, and an overview of existing methodologies and tools for using them.

https://hal.inria.fr/hal-01363962/document

BDI agents in social simulations: a survey

A document set, and history documents including documents, etc., browsed by a user are input. The document set and the history documents are each analyzed to obtain characteristic vectors. A plurality of topic clusters and a plurality of sub-topic clusters are obtained by clustering the document set. A transition structure showing transitions of topics among the sub-topic clusters is generated, and a characteristic attribute is extracted from each topic cluster and each sub-topic cluster. An cluster-of-interest is extracted in comparison among characteristic vectors of the history documents and a characteristic vector of each document included in the document set, a sub-topic cluster having transition relations with the cluster-of-interest is obtained on the basis of a transition structure owned by the cluster-of-interest, and a document included in the sub-topic cluster is extracted as a recommended document to be presented together with the characteristic attribute.

Information recommendation device and information recommendation method

This paper presents a method for the detection of occasional or volatile local events using topic extraction technologies. This is a new application of topic extraction technologies that has not been addressed in general location-based services. A two-level hierarchical clustering method was applied to topics and their transitions using time-series blog entries collected with search queries including place names. According to experiments using 764 events from 37 locations in Tokyo and its vicinity, our method achieved 77.0% event findability. It was found that the number of blog entries in urban areas was sufficient for the extraction of topics, and the proposed method could extract typical volatile events, such as performances of music groups, and places of interest, such as popular restaurants.

Discovering Volatile Events in Your Neighborhood: Local-Area Topic Extraction from Blog Entries

A keyword analysis device obtains word vectors represented by the documents by analyzing keywords contained in each of documents input in a designated period. A topic cluster extraction device extracts topic clusters belonging to the same topic from a plurality of documents. A keyword extraction device extracts, as a characteristic keyword group, a predetermined number of keywords from the topic cluster in descending order of appearance frequency. A topic structurization determination device determines whether the topic can be structurized, by segmenting the topic cluster into subtopic clusters with reference to the number of documents, the variance of dates contained in the documents, or the C-value of keyword contained in the documents, as a determination criterion. And a keyword presentation device presents the characteristic keyword group in the subtopic cluster upon arranging the keyword group on the basis of the date information.

Keyword outputting apparatus and method

PROBLEM TO BE SOLVED: To segmentalize subjects to sub subjects, and to extract and present keywords representing the sub-subjects. SOLUTION: A keyword presentation device is provided with: a keyword analysis means for calculating word vectors represented by respective documents by analyzing keywords included in each of a plurality of the documents input in a specific period; a subject cluster extraction means for extracting subject clusters belonging to the same subject in a set of documents in the plurality of documents; a keyword extraction means for extracting the predetermined number of keywords in an order from the highest appearance frequency from the subject clusters as characteristic keyword groups; a subject structurization determination means for determining whether or not the subject can be structurized by dividing the subject clusters into subject clusters of the sub subjects by one of criteria of the number of documents, the distribution of dates included in the document, C-values of the keywords included in the documents; and a keyword presentation means for arranging the characteristic keyword groups based on date and time information presented in the sub subject clusters. COPYRIGHT: (C)2009,JPO&INPIT

Device, method, and program for keyword presentation

Effect of thermal aging on fracture toughness of RPV steel

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