Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity

In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as manually labeling training data or creating gazetteers. Second, the system can handle more than the three classical named-entity types (person, location, and organization). We describe the system’s architecture and compare its performance with a supervised system. We experimentally evaluate the system on a standard corpus, with the three classical named-entity types, and also on a new corpus, with a new named-entity type (car brands)

A combining approach to find all taxon names (FAT)

Most of the literature on natural history is hidden in millions of pages stacked up in our libraries. Various initiatives aim now at making these publications digitally accessible and searchable, applying xml-mark up technologies. The unique biological names play a crucial role to link content related to a particular taxon. Thus discovering and marking them up is extremely important. Since their manual extraction and markup is cumbersome and time-intensive, it needs be automated. In this paper, we present computational linguistics techniques and evaluate how they can help to extract taxonomic names auto-matically. We build on an existing approach for extraction of such names (Koning et al. 2005) and combine it with several other learning techniques. We apply them to the texts sequentially so that each technique can use the results from the preceding ones. In particular, we use structural rules, dynamic lexica with fuzzy lookups, and word-level language recognition. We use legacy documents from different sources and times as test bed for our evaluation. The experimental results for our combining approach (FAT) show greater than 99% precision and recall. They reveal the potential of computational linguis-tics techniques towards an automated markup of biosystematics publications

Generalisation in named entity recognition: A quantitative analysis

Named Entity Recognition (NER) is a key NLP task, which is all the more challenging on Web and user-generated content with their diverse and continuously changing language. This paper aims to quantify how this diversity impacts state-of-the-art NER methods, by measuring named entity (NE) and context variability, feature sparsity, and their effects on precision and recall. In particular, our findings indicate that NER approaches struggle to generalise in diverse genres with limited training data. Unseen NEs, in particular, play an important role, which have a higher incidence in diverse genres such as social media than in more regular genres such as newswire. Coupled with a higher incidence of unseen features more generally and the lack of large training corpora, this leads to significantly lower F1 scores for diverse genres as compared to more regular ones. We also find that leading systems rely heavily on surface forms found in training data, having problems generalising beyond these, and offer explanations for this observation

Named Entity Recognition without Gazetteers

It is often claimed that Named En-tity recognition systems need extensive gazetteers|lists of names of people, or-ganisations, locations, and other named entities. Indeed, the compilation of such gazetteers is sometimes mentioned as a bottleneck in the design of Named En-tity recognition systems. We report on a Named Entity recogni-tion system which combines rule-based grammars with statistical (maximum en-tropy) models. We report on the sys-tem's performance with gazetteers of dif-ferent types and dierent sizes, using test material from the muc{7 competition. We show that, for the text type and task of this competition, it is suÆcient to use relatively small gazetteers of well-known names, rather than large gazetteers of low-frequency names. We conclude with observations about the domain indepen-dence of the competition and of our ex-periments.

Semi-Supervised Named Entity Recognition:\ud Learning to Recognize 100 Entity Types with Little Supervision\ud

Named Entity Recognition (NER) aims to extract and to classify rigid designators in text such as proper names, biological species, and temporal expressions. There has been growing interest in this field of research since the early 1990s. In this thesis, we document a trend moving away from handcrafted rules, and towards machine learning approaches. Still, recent machine learning approaches have a problem with annotated data availability, which is a serious shortcoming in building and maintaining large-scale NER systems. \ud \ud In this thesis, we present an NER system built with very little supervision. Human supervision is indeed limited to listing a few examples of each named entity (NE) type. First, we introduce a proof-of-concept semi-supervised system that can recognize four NE types. Then, we expand its capacities by improving key technologies, and we apply the system to an entire hierarchy comprised of 100 NE types. \ud \ud Our work makes the following contributions: the creation of a proof-of-concept semi-supervised NER system; the demonstration of an innovative noise filtering technique for generating NE lists; the validation of a strategy for learning disambiguation rules using automatically identified, unambiguous NEs; and finally, the development of an acronym detection algorithm, thus solving a rare but very difficult problem in alias resolution. \ud \ud We believe semi-supervised learning techniques are about to break new ground in the machine learning community. In this thesis, we show that limited supervision can build complete NER systems. On standard evaluation corpora, we report performances that compare to baseline supervised systems in the task of annotating NEs in texts. \u