Inferring syntactic rules for word alignment through Inductive Logic Programming

International audienceThis paper presents and evaluates an original approach to automatically align bitexts at the word level. It relies on a syntactic dependency analysis of the source and target texts and is based on a machine-learning technique, namely inductive logic programming (ILP). We show that ILP is particularly well suited for this task in which the data can only be expressed by (translational and syntactic) relations. It allows us to infer easily rules called syntactic alignment rules. These rules make the most of the syntactic information to align words. A simple bootstrapping technique provides the examples needed by ILP, making this machine learning approach entirely automatic. Moreover, through different experiments, we show that this approach requires a very small amount of training data, and its performance rivals some of the best existing alignment systems. Furthermore, cases of syntactic isomorphisms or non-isomorphisms between the source language and the target language are easily identified through the inferred rules

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Domain-Specific Knowledge Acquisition for Conceptual Sentence Analysis

The availability of on-line corpora is rapidly changing the field of natural language processing (NLP) from one dominated by theoretical models of often very specific linguistic phenomena to one guided by computational models that simultaneously account for a wide variety of phenomena that occur in real-world text. Thus far, among the best-performing and most robust systems for reading and summarizing large amounts of real-world text are knowledge-based natural language systems. These systems rely heavily on domain-specific, handcrafted knowledge to handle the myriad syntactic, semantic, and pragmatic ambiguities that pervade virtually all aspects of sentence analysis. Not surprisingly, however, generating this knowledge for new domains is time-consuming, difficult, and error-prone, and requires the expertise of computational linguists familiar with the underlying NLP system. This thesis presents Kenmore, a general framework for domain-specific knowledge acquisition for conceptual sentence analysis. To ease the acquisition of knowledge in new domains, Kenmore exploits an on-line corpus using symbolic machine learning techniques and robust sentence analysis while requiring only minimal human intervention. Unlike most approaches to knowledge acquisition for natural language systems, the framework uniformly addresses a range of subproblems in sentence analysis, each of which traditionally had required a separate computational mechanism. The thesis presents the results of using Kenmore with corpora from two real-world domains (1) to perform part-of-speech tagging, semantic feature tagging, and concept tagging of all open-class words in the corpus; (2) to acquire heuristics for part-ofspeech disambiguation, semantic feature disambiguation, and concept activation; and (3) to find the antecedents of relative pronouns

A Language-Independent Approach to Extracting Derivational Relations from an Inflectional Lexicon

International audienceIn this paper, we describe and evaluate an unsupervised method for cquiring pairs of lexical entries belonging to the same morphological family, i.e., derivationally related words, starting from a purely inflectional lexicon. Our approach relies on transformation rules that relate lexical entries with the one another, and which are automatically extracted from the inflected lexicon based on surface form analogies and on part-of-speech information. It is generic enough to be applied to any language with a mainly concatenative derivational morphology. Results were obtained and evaluated on English, French, German and Spanish. Precision results are satisfying, and our French results favorably compare with another resource, although its construction relied on manually developed lexicographic information whereas our approach only requires an inflectional lexicon

Mrežni sintaksno-semantički okvir za izvlačenje leksičkih relacija deterministričkim modelom prirodnog jezika

Given the extraordinary growth in online documents, methods for automated extraction of semantic relations became popular, and shortly after, became necessary. This thesis proposes a new deterministic language model, with the associated artifact, which acts as an online Syntactic and Semantic Framework (SSF) for the extraction of morphosyntactic and semantic relations. The model covers all fundamental linguistic fields: Morphology (formation, composition, and word paradigms), Lexicography (storing words and their features in network lexicons), Syntax (the composition of words in meaningful parts: phrases, sentences, and pragmatics), and Semantics (determining the meaning of phrases). To achieve this, a new tagging system with more complex structures was developed. Instead of the commonly used vectored systems, this new tagging system uses tree-likeT-structures with hierarchical, grammatical Word of Speech (WOS), and Semantic of Word (SOW) tags. For relations extraction, it was necessary to develop a syntactic (sub)model of language, which ultimately is the foundation for performing semantic analysis. This was achieved by introducing a new ‘O-structure’, which represents the union of WOS/SOW features from T-structures of words and enables the creation of syntagmatic patterns. Such patterns are a powerful mechanism for the extraction of conceptual structures (e.g., metonymies, similes, or metaphors), breaking sentences into main and subordinate clauses, or detection of a sentence’s main construction parts (subject, predicate, and object). Since all program modules are developed as general and generative entities, SSF can be used for any of the Indo-European languages, although validation and network lexicons have been developed for the Croatian language only. The SSF has three types of lexicons (morphs/syllables, words, and multi-word expressions), and the main words lexicon is included in the Global Linguistic Linked Open Data (LLOD) Cloud, allowing interoperability with all other world languages. The SSF model and its artifact represent a complete natural language model which can be used to extract the lexical relations from single sentences, paragraphs, and also from large collections of documents.Pojavom velikoga broja digitalnih dokumenata u okružju virtualnih mreža (interneta i dr.), postali su zanimljivi, a nedugo zatim i nužni, načini identifikacije i strojnoga izvlačenja semantičkih relacija iz (digitalnih) dokumenata (tekstova). U ovome radu predlaže se novi, deterministički jezični model s pripadnim artefaktom (Syntactic and Semantic Framework - SSF), koji će služiti kao mrežni okvir za izvlačenje morfosintaktičkih i semantičkih relacija iz digitalnog teksta, ali i pružati mnoge druge jezikoslovne funkcije. Model pokriva sva temeljna područja jezikoslovlja: morfologiju (tvorbu, sastav i paradigme riječi) s leksikografijom (spremanjem riječi i njihovih značenja u mrežne leksikone), sintaksu (tj. skladnju riječi u cjeline: sintagme, rečenice i pragmatiku) i semantiku (određivanje značenja sintagmi). Da bi se to ostvarilo, bilo je nužno označiti riječ složenijom strukturom, umjesto do sada korištenih vektoriziranih gramatičkih obilježja predložene su nove T-strukture s hijerarhijskim, gramatičkim (Word of Speech - WOS) i semantičkim (Semantic of Word - SOW) tagovima. Da bi se relacije mogle pronalaziti bilo je potrebno osmisliti sintaktički (pod)model jezika, na kojem će se u konačnici graditi i semantička analiza. To je postignuto uvođenjem nove, tzv. O-strukture, koja predstavlja uniju WOS/SOW obilježja iz T-struktura pojedinih riječi i omogućuje stvaranje sintagmatskih uzoraka. Takvi uzorci predstavljaju snažan mehanizam za izvlačenje konceptualnih struktura (npr. metonimija, simila ili metafora), razbijanje zavisnih rečenica ili prepoznavanje rečeničnih dijelova (subjekta, predikata i objekta). S obzirom da su svi programski moduli mrežnog okvira razvijeni kao opći i generativni entiteti, ne postoji nikakav problem korištenje SSF-a za bilo koji od indoeuropskih jezika, premda su provjera njegovog rada i mrežni leksikoni izvedeni za sada samo za hrvatski jezik. Mrežni okvir ima tri vrste leksikona (morphovi/slogovi, riječi i višeriječnice), a glavni leksikon riječi već je uključen u globalni lingvistički oblak povezanih podataka, što znači da je interoperabilnost s drugim jezicima već postignuta. S ovako osmišljenim i realiziranim načinom, SSF model i njegov realizirani artefakt, predstavljaju potpuni model prirodnoga jezika s kojim se mogu izvlačiti leksičke relacije iz pojedinačne rečenice, odlomka, ali i velikog korpusa (eng. big data) podataka

Annotating the semantic web

The web of today has evolved into a huge repository of rich Multimedia content for human consumption. The exponential growth of the web made it possible for information size to reach astronomical proportions; far more than a mere human can manage, causing the problem of information overload. Because of this, the creators of the web(lO) spoke of using computer agents in order to process the large amounts of data. To do this, they planned to extend the current web to make it understandable by computer programs. This new web is being referred to as the Semantic Web. Given the huge size of the web, a collective effort is necessary to extend the web. For this to happen, tools easy enough for non-experts to use must be available. This thesis first proposes a methodology which semi-automatically labels semantic entities in web pages. The methodology first requires a user to provide some initial examples. The tool then learns how to reproduce the user's examples and generalises over them by making use of Adaptive Information Extraction (AlE) techniques. When its level of performance is good enough when compared to the user, it then takes over the process and processes the remaining documents autonomously. The second methodology goes a step further and attempts to gather semantically typed information from web pages automatically. It starts from the assumption that semantics are already available all over the web, and by making use of a number of freely available resources (like databases) combined with AlE techniques, it is possible to extract most information automatically. These techniques will certainly not provide all the solutions for the problems brought about with the advent of the Semantic Web. They are intended to provide a step forward towards making the Semantic Web a reality

Acquiring information extraction patterns from unannotated corpora

Information Extraction (IE) can be defined as the task of automatically extracting preespecified kind of information from a text document. The extracted information is encoded in the required format and then can be used, for example, for text summarization or as accurate index to retrieve new documents.The main issue when building IE systems is how to obtain the knowledge needed to identify relevant information in a document. Today, IE systems are commonly based on extraction rules or IE patterns to represent the kind of information to be extracted. Most approaches to IE pattern acquisition require expert human intervention in many steps of the acquisition process. This dissertation presents a novel method for acquiring IE patterns, Essence, that significantly reduces the need for human intervention. The method is based on ELA, a specifically designed learning algorithm for acquiring IE patterns from unannotated corpora.The distinctive features of Essence and ELA are that 1) they permit the automatic acquisition of IE patterns from unrestricted and untagged text representative of the domain, due to 2) their ability to identify regularities around semantically relevant concept-words for the IE task by 3) using non-domain-specific lexical knowledge tools such as WordNet and 4) restricting the human intervention to defining the task, and validating and typifying the set of IE patterns obtained.Since Essence does not require a corpus annotated with the type of information to be extracted and it does makes use of a general purpose ontology and widely applied syntactic tools, it reduces the expert effort required to build an IE system and therefore also reduces the effort of porting the method to any domain.In order to Essence be validated we conducted a set of experiments to test the performance of the method. We used Essence to generate IE patterns for a MUC-like task. Nevertheless, the evaluation procedure for MUC competitions does not provide a sound evaluation of IE systems, especially of learning systems. For this reason, we conducted an exhaustive set of experiments to further test the abilities of Essence.The results of these experiments indicate that the proposed method is able to learn effective IE patterns