Mena Badieh Habib

A Generic Open World Named Entity Disambiguation Approach for Tweets

Social media is a rich source of information. To make use of this information, it is sometimes required to extract and disambiguate named entities. In this paper we focus on named entity disambiguation (NED) in twitter messages. NED in tweets is challenging in two ways. First, the limited length of Tweet makes it hard to have enough context while many disambiguation techniques depend on it. The second is that many named entities in tweets do not exist in a knowledge base (KB). In this paper we share ideas from information retrieval (IR) and NED to propose solutions for both challenges. For the first problem we make use of the gregarious nature of tweets to get enough context needed for disambiguation. For the second problem we look for an alternative home page if there is no Wikipedia page represents the entity. Given a mention, we obtain a list of Wikipedia candidates from YAGO KB in addition to top ranked pages from Google search engine. We use Support Vector Machine (SVM) to rank the candidate pages to find the best representative entities. Experiments conducted on two data sets show better disambiguation results compared with the baselines and a competitor.

Neogeography: The challenge of channelling large and ill-behaved data streams

Neogeography is the combination of user generated data and experiences with mapping technologies. In this paper we present a research project to extract valuable structured information with a geographic component from unstructured user generated text in wikis, forums, or SMSes. The project intends to help workers communities in developing countries to share their knowledge, providing a simple and cheap way to contribute and get benefit using the available communication technology.

TwitterNEED: a hybrid approach for named entity extraction and disambiguation for tweets

Twitter is a rich source of continuously and instantly updated information. Shortness and informality of tweets are challenges for Natural Language Processing tasks. In this paper, we present TwitterNEED, a hybrid approach for Named Entity Extraction and Named Entity Disambiguation for tweets. We believe that disambiguation can help to improve the extraction process. This mimics the way humans understand language and reduces error propagation in the whole system. Our extraction approach aims for high extraction recall first, after which a Support Vector Machine attempts to filter out false positives among the extracted candidates using features derived from the disambiguation phase in addition to other word shape and Knowledge Base features. For Named Entity Disambiguation, we obtain a list of entity candidates from the YAGO Knowledge Base in addition to top-ranked pages from the Google search engine for each extracted mention. We use a Support Vector Machine to rank the candidate pages according to a set of URL and context similarity features. For evaluation, five data sets are used to evaluate the extraction approach, and three of them to evaluate both the disambiguation approach and the combined extraction and disambiguation approach. Experiments show better results compared to our competitors DBpedia Spotlight, Stanford Named Entity Recognition, and the AIDA disambiguation system.

A hybrid approach for robust multilingual toponym extraction and disambiguation

Toponym extraction and disambiguation are key topics recently addressed by fields of Information Extraction and Geographical Information Retrieval. Toponym extraction and disambiguation are highly dependent processes. Not only toponym extraction effectiveness affects disambiguation, but also disambiguation results may help improving extraction accuracy. In this paper we propose a hybrid toponym extraction approach based on Hidden Markov Models (HMM) and Support Vector Machines (SVM). Hidden Markov Model is used for extraction with high recall and low precision. Then SVM is used to find false positives based on informativeness features and coherence features derived from the disambiguation results. Experimental results conducted with a set of descriptions of holiday homes with the aim to extract and disambiguate toponyms showed that the proposed approach outperform the state of the art methods of extraction and also proved to be robust. \mbh{Robustness is proved on three aspects: language independence, high and low HMM threshold settings, and limited training data.

NEED4Tweet: A Twitterbot for Tweets Named Entity Extraction and Disambiguation

In this demo paper, we present NEED4Tweet, a Twitterbot for named entity extraction (NEE) and disambiguation (NED) for Tweets. The straightforward application of state-of-the-art extraction and disambiguation approaches on informal text widely used in Tweets, typically results in significantly degraded performance due to the lack of formal structure; the lack of sufficient context required; and the seldom entities involved. In this paper, we introduce a novel framework that copes with the introduced challenges. We rely on contextual and semantic features more than syntactic features which are less informative. We believe that disambiguation can help to improve the extraction process. This mimics the way humans understand language.

Improving toponym extraction and disambiguation using feedback loop

This paper addresses two problems with toponym extraction and disambiguation. First, almost no existing works examine the extraction and disambiguation interdependency. Second, existing disambiguation techniques mostly take as input extracted toponyms without considering the uncertainty and imperfection of the extraction process. It is the aim of this paper to investigate both avenues and to show that explicit handling of the uncertainty of annotation has much potential for making both extraction and disambiguation more robust.

Detecting Hacked Twitter Accounts based on Behavioural Change

Social media accounts are valuable for hackers for spreading phishing links, malware and spam. Furthermore, some people deliberately hack an acquaintance to damage his or her image. This paper describes a classification for detecting hacked Twitter accounts. The model is mainly based on features associated with behavioural change such as changes in language, source, URLs, retweets, frequency and time. We experiment with a Twitter data set containing tweets of more than 100 Dutch users including 37 who were hacked. The model detects 99% of the malicious tweets which proves that behavioural changes can reveal a hack and that anomaly-based features perform better than regular features. Our approach can be used by social media systems such as Twitter to automatically detect a hack of an account only a short time after the fact allowing the legitimate owner of the account to be warned or protected, preventing reputational damage and annoyance.

Incremental Data Uncertainty Handling Using Evidence Combination: A Case Study on Maritime Data Reasoning

Semantic incompatibility is a conflict that occurs in the meanings of data. In this paper, we propose an approach for data cleaning by resolving semantic incompatibility. Our approach applies a dynamic and incremental enhancement of data quality. It checks the coherency/conflict of the newly recorded facts/relations against the existing ones. It reasons over the existing information and comes up with new discovered facts/relations. We choose maritime data cleaning as a validation scenario.

Toponym Extraction and Disambiguation Enhancement Using Loops of Feedback

Toponym extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. This paper addresses two problems with toponym extraction and disambiguation. First, almost no existing works examine the extraction and disambiguation interdependency. Second, existing disambiguation techniques mostly take as input extracted named entities without considering the uncertainty and imperfection of the extraction process. In this paper we aim to investigate both avenues and to show that explicit handling of the uncertainty of annotation has much potential for making both extraction and disambiguation more robust. We conducted experiments with a set of holiday home descriptions with the aim to extract and disambiguate toponyms. We show that the extraction confidence probabilities are useful in enhancing the effectiveness of disambiguation. Reciprocally, retraining the extraction models with information automatically derived from the disambiguation results, improves the extraction models. This mutual reinforcement is shown to even have an effect after several automatic iterations.