Information Extraction (IE) systems assist analysts to assimilate information from electronic documents. This paper focuses on IE tasks designed to support information discovery applications. Since information discovery implies examining large volumes of heterogeneous documents for situations that cannot be anticipated a priori, they require IE systems to have breadth as well as depth. This implies the need for a domain-independent IE system that can easily be customized for specific domains: end users must be given tools to customize the system on their own. It also implies the need for defining new intermediate level IE tasks that are richer than the subject-verb-object (SVO) triples produced by shallow systems, yet not as complex as the domain-specific scenarios defined by the Message Understanding Conference (MUC). This paper describes InfoXtract, a robust, scalable, intermediate-level IE engine that can be ported to various domains. It describes new IE tasks such as synthesis of entity profiles, and extraction of concept-based general events which represent realistic near-term goals focused on deriving useful, actionable information. Entity profiles consolidate information about a person/organization/location etc. within a document and across documents into a single template; this takes into account aliases and anaphoric references as well as key relationships and events pertaining to that entity. Concept-based events attempt to normalize information such as time expressions (e.g., yesterday) as well as ambiguous location references (e.g., Buffalo). These new tasks facilitate the correlation of output from an IE engine with structured data to enable text mining. InfoXtract's hybrid architecture comprised of grammatical processing and machine learning is described in detail. Benchmarking results for the core engine and applications utilizing the engine are presented.

Attempting to automatically learn to identify verb complements from natural language corpora without the help of sophisticated linguistic resources like grammars, parsers or treebanks leads to a significant amount of noise in the data. In machine learning terms, where learning from examples is performed using class-labelled feature-value vectors, noise leads to an imbalanced set of vectors: assuming that the class label takes two values (in this work complement/non-complement), one class (complements) is heavily underrepresented in the data in comparison to the other. To overcome the drop in accuracy when predicting instances of the rare class due to this disproportion, we balance the learning data by applying one-sided sampling to the training corpus and thus by reducing the number of non-complement instances. This approach has been used in the past in several domains (image processing, medicine, etc) but not in natural language processing. For identifying the examples that are safe to remove, we use the value difference metric, which proves to be more suitable for nominal attributes like the ones this work deals with, unlike the Euclidean distance, which has been used traditionally in one-sided sampling. We experiment with different learning algorithms which have been widely used and their performance is well known to the machine learning community: Bayesian learners, instance-based learners and decision trees. Additionally we present and test a variation of Bayesian belief networks, the COr-BBN (Class-oriented Bayesian belief network). The performance improves up to 22% after balancing the dataset, reaching 73.7% f-measure for the complement class, having made use only a phrase chunker and basic morphological information for preprocessing.

This paper describes CLIME, a web-based legal advisory system with a multilingual natural language interface. CLIME is a ‘proof-of-concept’ system which answers queries relating to ship-building and ship-operating regulations. Its core knowledge source is a set of such regulations encoded as a conceptual domain model and a set of formalised legal inference rules. The system supports retrieval of regulations via the conceptual model, and assessment of the legality of a situation or activity on a ship according to the legal inference rules. The focus of this paper is on the natural language aspects of the system, which help the user to construct semantically complex queries using WYSIWYM technology, allow the system to produce extended and cohesive responses and explanations, and support the whole interaction through a hybrid synchronous/asynchronous dialogue structure. Multilinguality (English and French) is viewed simply as interface localisation: the core representations are language-neutral, and the system can present extended or local interactions in either language at any time. The development of CLIME featured a high degree of client involvement, and the specification, implementation and evaluation of natural language components in this context are also discussed.

This paper describes DialogueView, a tool for annotating dialogues with utterance boundaries, speech repairs, speech act tags, and hierarchical discourse blocks. The tool provides three views of a dialogue: WordView, which shows the transcribed words time-aligned with the audio signal; UtteranceView, which shows the dialogue line-by-line as if it were a script for a movie; and BlockView, which shows an outline of the dialogue. The different views provide different abstractions of what is occurring in the dialogue. Abstraction helps users focus on what is important for different annotation tasks. For example, for annotating speech repairs, utterance boundaries, and overlapping and abandoned utterances, the tool provides the exact timing information. For coding speech act tags and hierarchical discourse structure, a broader context is created by hiding such low-level details, which can still be accessed if needed. We find that the different abstractions allow users to annotate dialogues more quickly without sacrificing accuracy. The tool can be configured to meet the requirements of a variety of annotation schemes.

I am honoured to address you as the new Executive Editor of the journal, a role I took on recently from Professor John Tait. As someone who, along with the other editors and members of the Editorial Board, has the responsibility for the overall quality of the journal, my main goal is to continue actively pursuing the journal objectives and to raise the standards even higher. These objectives are concerned with promoting applied natural language processing (NLP) research in the form of first-class original research and with bridging the gap between traditional computational linguistics research and the implementation of practical applications with potential real-world use.

A year ago in this column I wondered aloud whether 2007 was to be the year in which question-answering (QA) really took off in the commercial space. I was provoked to ask that question by the increasing number of Web-based QA systems that were portraying themselves as the Next Thing in search for the masses. There was, in particular, a lot of buzz around the $12.5 million funding deal announced by Powerset. The San Francisco-based company had gained exclusive access to parsing technology from PARC, but hadn't at that point displayed any of its wares to the general public. However, the company was inviting people to sign up for access to Powerset Labs, where, we were told, we would get the opportunity to be the first to play with the technology and to provide feedback to make it better. Since then, there have been occasional screen images of the application seen in blog posts and other news items, and a small number of claimed sightings by bloggers who were granted privileged access. But Powerset Labs was finally launched at TechCrunch 40 in mid-September. The company's Web site says that they've begun to let people have access to the technology, and that they'll be ‘letting in the next wave of users as soon as possible’. My surf board is ready, but I'm not holding my breath. I signed up in June 2007, and haven't heard a thing since. Comments posted on the Powerset site suggest that there might be quite a few in the line before me.

Problems in the area of text and document processing can often be described as text rewriting tasks: given an input text, produce a new text by applying some fixed set of rewriting rules. In its simplest form, a rewriting rule is given by a pair of strings, representing a source string (the “original”) and its substitute. By a rewriting dictionary, we mean a finite list of such pairs; dictionary-based text rewriting means to replace in an input text occurrences of originals by their substitutes. We present an efficient method for constructing, given a rewriting dictionary D, a subsequential transducer that accepts any text t as input and outputs the intended rewriting result under the so-called “leftmost-longest match” replacement with skips, t'. The time needed to compute the transducer is linear in the size of the input dictionary. Given the transducer, any text t of length |t| is rewritten in a deterministic manner in time O(|t|+|t'|), where t' denotes the resulting output text. Hence the resulting rewriting mechanism is very efficient. As a second advantage, using standard tools, the transducer can be directly composed with other transducers to efficiently solve more complex rewriting tasks in a single processing step.

We present a simple and intuitive unsound corpus-driven approximation method for turning unification-based grammars, such as HPSG, CLE, or PATR-II into context-free grammars (CFGs). Our research is motivated by the idea that we can exploit (large-scale), hand-written unification grammars not only for the purpose of describing natural language and obtaining a syntactic structure (and perhaps a semantic form), but also to address several other very practical topics. Firstly, to speed up deep parsing by having a cheap recognition pre-flter (the approximated CFG). Secondly, to obtain an indirect stochastic parsing model for the unification grammar through a trained PCFG, obtained from the approximated CFG. This gives us an efficient disambiguation model for the unification-based grammar. Thirdly, to generate domain-specific subgrammars for application areas such as information extraction or question answering. And finally, to compile context-free language models which assist the acoustic model of a speech recognizer. The approximation method is unsound in that it does not generate a CFG whose language is a true superset of the language accepted by the original unification-based grammar. It is a corpus-driven method in that it relies on a corpus of parsed sentences and generates broader CFGs when given more input samples. Our open approach can be fine-tuned in different directions, allowing us to monotonically come close to the original parse trees by shifting more information into the context-free symbols. The approach has been fully implemented in JAVA.

In this paper we discuss the task of dialogue act recognition as a part of interpreting user utterances in context. To deal with the uncertainty that is inherent in natural language processing in general and dialogue act recognition in particular we use machine learning techniques to train classifiers from corpus data. These classifiers make use of both lexical features of the (Dutch) keyboard-typed utterances in the corpus used, and context features in the form of dialogue acts of previous utterances. In particular, we consider probabilistic models in the form of Bayesian networks to be proposed as a more general framework for dealing with uncertainty in the dialogue modelling process.

I've just come back from the 45th Annual Meeting of the Association for Computational Linguistics (ACL) in Prague; this was the biggest ever ACL conference, with more than 1,000 people attending for the first time. Attendance at ACL conferences has been growing year on year, and that is a sign of a healthy field. Another sign of health is industry sponsorship. For this year's conference, the Gold Sponsor was Google, and Microsoft and Yahoo! were Silver Sponsors, along with a few companies we have not seen as ACL sponsors before: −textkernel, News Tin, and – a name that seems now to pop up regularly in this column – Powerset. There are all sorts of reasons why companies sponsor conferences like this, but clearly a major purpose is to make themselves visible to potential employees. And, if companies are hiring, that's good news across the board: it gives us a way of attracting more students into the field, and more generally, it speaks to the industrial and commercial relevance of what we do. There is nothing like external validation – especially commercial validation – to wash away those niggling self-doubts about the utility of your research endeavours. I remember attending MT Summit VII in Singapore in 1999, when Jo Lernout, then of Lernout & Hauspie, gave an invited talk in which (I'm sure I'm remembering this correctly) he said his ambition was to hire everyone in the hall. There were around 250 attendees – big for an NLP conference at the time – so that created quite a buzz. Jo wanted to hire everyone, not just cherry pick those with the near-to-product big ideas; in his vision of the future, every teensy-weensy tightly-focused research contribution had a role to play. For just a moment, everybody felt wanted.

The PARC 700 dependency bank is a potentially very useful resource for parser evaluation that has, so to speak, a high barrier to entry, because of tokenisation that is quite different from the source of the data, the Penn Treebank, and because there is no representation of word order, producing an uncertainty factor of some 15%. There is also a small, but perhaps not insignificant, number of errors. When using the dependency bank for evaluation, it seems likely that these things will cause inflated counts for mismatches, so to obtain more accurate measurements, it is desirable to eliminate them. The work reported here consists of an automatic conversion of the dependency bank into a Prolog representation where the word order is explicit, as well as graphical representations of the dependency trees for all 700 sentences, automatically generated from the Prolog data. As a side effect of the transformation, errors were detected and corrected. It is hoped that this work will lead to more widespread use of the PARC 700 dependency bank for parser evaluation.

“Powerset Hype to Boiling Point”, said a February headline on TechCrunch. In the last installment of this column, I asked whether 2007 would be the year of question-answering. My query was occasioned by a number of new attempts at natural language question-answering that were being promoted in the marketplace as the next advance upon search, and particularly by the buzz around the stealth-mode natural language search company Powerset. That buzz continued with a major news item in the first quarter of this year: in February, Xerox PARC and PowerSet struck a much-anticipated deal whereby PowerSet won exclusive rights to use PARC's natural language technology, as announced in a VentureBeat posting. Following the scoop, other news sources drew the battle lines with titles like “Can natural language search bring down Google?”, “Xerox vs. Google?”, and “Powerset and Xerox PARC team up to beat Google”. An April posting on Barron's Online noted that an analyst at Global Equities Research had cited Powerset in his downgrading of Google from Buy to Neutral. And, all this on the basis of a product which, at the time of writing, very few people have actually seen. Indications are that the search engine is expected to go live by the end of the year, so we have a few more months to wait to see whether this really is a Google-killer. Meanwhile, another question remaining unanswered is what happened to the Powerset engineer who seemed less sure about the technology's capabilities: see the segment at the end of D7TV's PartyCrasher video from the Powerset launch party. For a more confident appraisal of natural language search, check out the podcast of Barney Pell, CEO of Powerset, giving a lecture at the University of California–Berkeley.

Back at the beginning of 2000, I was a member of a working group tasked to come up with some guidelines for revamping my University's website.During one of our meetings, someone made the suggestion thatdecisions about howto structure and present information on the website should be driven by the kinds of questions that users come to the site with.Suddenly a light went on, and there appeared an idea for data gathering that might provide us withsome useful information. To find out what people really wanted to know whenthey visited the website, we would replace the University's searchengine by a page that invited the user to type in his or her query as a full natural language question. Appropriately chosen examples would be given todemonstrate that using real questions delivered better pages as a result. The data gathered would tell us what people were really looking for, more than could be gleaned from conventional search queries, and would therefore help usto better structure the information available on the website.

Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible manner. Experimental evaluation confirms that MaltParser can achieve robust, efficient and accurate parsing for a wide range of languages without language-specific enhancements and with rather limited amounts of training data.