Adam Funk

Ontology-based information extraction for business intelligence

Business Intelligence (BI) requires the acquisition and aggregation of key pieces of knowledge from multiple sources in order to provide valuable information to customers or feed statistical BI models and tools. The massive amount of information available to business analysts makes information extraction and other natural language processing tools key enablers for the acquisition and use of that semantic information. We describe the application of ontology-based extraction and merging in the context of a practical e-business application for the EU MUSING Project where the goal is to gather international company intelligence and country/region information. The results of our experiments so far are very promising and we are now in the process of building a complete end-to-end solution.

CLOnE: controlled language for ontology editing

This paper presents a controlled language for ontology editing and a software implementation, based partly on standard NLP tools, for processing that language and manipulating an ontology. The input sentences are analysed deterministically and compositionally with respect to a given ontology, which the software consults in order to interpret the inputs semantics; this allows the user to learn fewer syntactic structures since some of them can be used to refer to either classes or instances, for example. A repeated-measures, task-based evaluation has been carried out in comparison with a well-known ontology editor; our software received favourable results for basic tasks. The paper also discusses work in progress and future plans for developing this language and tool.

Automatic detection of political opinions in tweets

In this paper, we discuss a variety of issues related to opinion mining from microposts, and the challenges they impose on an NLP system, along with an example application we have developed to determine political leanings from a set of pre-election tweets. While there are a number of sentiment analysis tools available which summarise positive, negative and neutral tweets about a given keyword or topic, these tools generally produce poor results, and operate in a fairly simplistic way, using only the presence of certain positive and negative adjectives as indicators, or simple learning techniques which do not work well on short microposts. On the other hand, intelligent tools which work well on movie and customer reviews cannot be used on microposts due to their brevity and lack of context. Our methods make use of a variety of sophisticated NLP techniques in order to extract more meaningful and higher quality opinions, and incorporate extra-linguistic contextual information.

RoundTrip Ontology Authoring

Controlled Language (CL) for Ontology Editing tools offer an attractive alternative for naive users wishing to create ontologies, but they are still required to spend time learning the correct syntactic structures and vocabulary in order to use the Controlled Language properly. This paper extends previous work (CLOnE) which uses standard NLP tools to process the language and manipulate an ontology. Here we also generate text in the CL from an existing ontology using template-based (or shallow) Natural Language Generation (NLG). The text generator and the CLOnE authoring process combine to form a RoundTrip Ontology Authoring environment: one can start with an existing imported ontology or one originally produced using CLOnE, (re)produce the Controlled Language, modify or edit the text as required and then turn the text back into the ontology in the CLOnE environment. Building on previous methodology we undertook an evaluation, comparing the RoundTrip Ontology Authoring process with a well-known ontology editor; where previous work required a CL reference manual with several examples in order to use the controlled language, the use of NLG reduces this learning curve for users and improves on existing results for basic ontology editing tasks.

Human Language Technologies

A tension exists between the increasingly rich semantic models in knowledge management systems and the continuing prevalence of human language materials in large organisations. The process of tying semantic models and natural language together is referred to as semantic annotation, which may also be char- acterized as the dynamic creation of bidirectional relationships between ontologies and unstructured and semi-structured documents. Information extraction (IE) takes unseen texts as input and produces fixed-format, unambiguous data as output. It involves processing text to identify selected infor- mation, such as particular named entities or relations among them from text docu- ments. Named entities include people, organizations, locations and so on, while relations typically include physical relations (located, near, part-whole, etc.), per- sonal or social relations (business, family, etc.), and membership (employ-staff, member-of-group, etc.). Ontology-based information extraction (OBIE) can be adapted specifically for semantic annotation tasks. An important difference between traditional IE and OBIE is the latters closely coupled use of an ontology as one of the systems resources - the ontology serves not only as a schema or list of classifications in the output, but also as input data - its structure affects the training and tagging processes. We present here two ontology-based developments for information extrac- tion. OBIE experiments demonstrate clearly that the integration of ontologies as a knowledge source within HLT applications leads to improved perform- ance. Another important finding is that computational efficiency of the under- lying machine learning methods is especially important for HLT tasks, as the system may need to train hundreds of classifiers depending on the size of the ontology.

Should I Care about Your Opinion? : Detection of Opinion Interestingness and Dynamics in Social Media

In this paper, we describe a set of reusable text processing components for extracting opinionated information from social media, rating it for interestingness, and for detecting opinion events. We have developed applications in GATE to extract named entities, terms and events and to detect opinions about them, which are then used as the starting point for opinion event detection. The opinions are then aggregated over larger sections of text, to give some overall sentiment about topics and documents, and also some degree of information about interestingness based on opinion diversity. We go beyond traditional opinion mining techniques in a number of ways: by focusing on specific opinion-target extraction related to key terms and events, by examining and dealing with a number of specific linguistic phenomena, by analysing and visualising opinion dynamics over time, and by aggregating the opinions in different ways for a more flexible view of the information contained in the documents.

Analysing and Enriching Focused Semantic Web Archives for Parliament Applications

The web and the social web play an increasingly important role as an information source for Members of Parliament and their assistants, journalists, political analysts and researchers. It provides important and crucial background information, like reactions to political events and comments made by the general public. The case study presented in this paper is driven by two European parliaments (the Greek and the Austrian parliament) and targets an effective exploration of political web archives. In this paper, we describe semantic technologies deployed to ease the exploration of the archived web and social web content and present evaluation results.

Automatic Label Generation for News Comment Clusters

We present a supervised approach to automat- ically labelling topic clusters of reader com- ments to online news. We use a feature set that includes both features capturing proper- ties local to the cluster and features that cap- ture aspects from the news article and from comments outside the cluster. We evaluate the approach in an automatic and a manual, task-based setting. Both evaluations show the approach to outperform a baseline method, which uses tf*idf to select comment-internal terms for use as topic labels. We illustrate how cluster labels can be used to generate cluster summaries and present two alternative sum- mary formats: a pie chart summary and an ab- stractive summary.

The SENSEI Overview of Newspaper Readers’ Comments

Automatic summarization of reader comments in on-line news is a challenging but clearly useful task. Work to date has produced extractive summaries using well-known techniques from other areas of NLP. But do users really want these, and do they support users in realistic tasks? We specify an alternative summary type for reader comments, based on the notions of issues and viewpoints, and demonstrate our user interface to present it. An evaluation to assess how well summarization systems support users in time-limited tasks (identifying issues and characterizing opinions) gives good results for this prototype.