A Symbolic and Connectionist Approach To Legal Information Retrieval

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Even systems with so-called "relevance feedback" are incapable of learning from experience with users.

Neural network

By using a hybrid of symbolic and connectionist artificial intelligence techniques, SCALIR develops a conceptual representation of document relationships without explicit knowledge engineering. It then uses these browsing patterns to improve its performance by modifying its representation, resulting in a communal repository of expertise for all of its users. While certain legal concepts are precise and rule-like, others -- which legal scholars call "open-textured" -- are subject to interpretation.

The meaning of legal text is established through the parallel and distributed precedence-based judicial appeal system. SCALIR represents documents and terms as nodes in a network, capturing the duality of the legal system by using symbolic semantic network and connectionist links. The former correspond to a priori knowledge such as the fact that one case overturned another on appeal. The latter correspond to statistical inferences such as the relevance of a term describing a case.

The hybrid representation also suggests a way to resolve the apparent incompatibility between the two prominent paradigms in artificial intelligence, the "classical" symbol-manipulation approach and the neurally-inspired connectionist approach.

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Part of the book focuses on a characterization of the two paradigms and an investigation of when and how -- as in the legal research domain -- they can be effectively combined. Search all titles. Search all titles Search all collections. Your Account Logout. Three solutions exist for dealing with the synonym problem: synonym list, conceptor-based retrieval and inference network. The synonym list can be easily implemented. The problem lies in the maintenance of the list and some misunderstandings of the user. In conceptor based retrieval, queries are structured according to the inherent ideas in the search problem.

Documents are relevant if these ideas are in part contained in the document. During the search formulation, every idea is described by a term class conceptor as a class of words that represent the same idea Bing , et seq. A ranking occurs according to the number of ideas which are contained in the document. As a second criterion for ranking, the number of the words of the term classes which are contained in the document is used. The basis of conceptor based retrieval consists in a good knowledge of the concept structure.

Homonyms are words that match in spelling but have different meanings Burkart , et seq. Polysems are expressions with initially the same importance, however by transfer, analogy, historical or regional development have developed different meanings or are employed as general terms in different contexts. For this problem the Boolean search in inverted files offers no solution since words with the same spelling are taken as homonyms.

In jurisprudence, polysems are very frequent but represented with other characteristics. Many legal terms are employed in a different context than in natural language. The exact importance of a legal term can only be understood in its context.

This phenomenon is considerably more frequent than the similar homonymy and substantially affects the quality of results in legal database queries. Homonymy and polysemy depress precision because such words have more than one meaning. Sense resolution will be an important component in future retrieval systems. The disambiguation techniques are mainly based on the idea that a set of words occurring together in context determine appropriate connotations.

This approach can be used to compute various senses of descriptors see below the KONTERM project or determine appropriate senses for word sets despite each individual word being multiply ambiguous.

So far, the experiments with word sense disambiguation techniques have been disappointing. Voorhees uses the word senses in the lexicon WorldNet for disambiguation. The improvement of the retrieval is modest. The sense disambiguation is very difficult in short query statements. Missing correct matches because of incorrect sense resolution have a deleterious effect on retrieval performance. Sanderson uses pseudo-words for disambiguation. The results on retrieval performance are similar to those of Voorhees.

Connectionist Models of Cognition

An implementation can only be recommended if the disambiguator is able to resolve word senses to a high degree of accuracy. The NetSerf application of Chakravarthy, Haase is a programme for searching in information archives on the Internet with semantic knowledge representations and with a disambiguator using the lexicon WorldNet.

Semantic knowledge representations of archives lead to a remarkable increase of performance. The disambiguated version performs slightly worse than the undisambiguated version. The situation is different if the technique of word sense disambiguation is used for automatic indexing.

A Symbolic And Connectionist Approach To Legal Information Retrieval Rose Daniel E (ePUB/PDF)

The aim of the project KONTERM workstation is to provide a hybrid application of methods of legal knowledge representation assisting lawyers in their task of managing large quantities of legal information contained in natural language documents. A major part of the project consisted of developing the word sense disambiguation. Other important elements were document description and the automatic generation of hypertext links.

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The emphasis was on the analysis of language Schweighofer, Winiwarter b , Schweighofer and document description Schweighofer, Winiwarter a. Description of the document space with cluster analysis and Kohonen net. In practice we used the results obtained from statistical cluster analysis although the results achieved with the self-organising maps were slightly better.

This is because of the very long time needed to train the self-organising maps especially when given long document descriptions that are natural in a real working environment. The core of lexical analysis of concept meanings is the recognition of linguistic patterns.

A characteristic of the approach is the inclusion of knowledge on the legal language in a cascaded architecture. Features of analysis are:. The selection of the document segments to be analysed occurs with the aid of descriptors as trigger words or context-sensitive rules.

The context is either the sentence or a sentence group which includes the previous and subsequent sentence. During implementation of the vector space model, unimportant words are removed with a stop wordlist. The analysis occurs with a text corpus from approx. The use of thesauri is based on equation of syntactic signs as denotations natural-language descriptors or also notations with elements of information.

The assignment of descriptors to documents occurs in principle intellectually. In the case of this approach, one examines in which form descriptors with a specific importance are available in texts themselves. The thesaurus is brought into a computer readable form when the context of the respective terms is used for differentiation of the various meanings. The occurrences of the descriptors of the thesaurus are represented as vectors whose length corresponds to the number of words in the document corpus with the exception of the stop words.

The relevant sentence is employed as a context. Every occurrence of a descriptor is stored in the descriptor file considering the comparison module. Herewith both compound descriptors and synonyms can be employed. Vectors are computed for all sentences in which a descriptor of the thesaurus occurs.

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This vector represents context information with the particular term meaning. The binary indexing was chosen for the purpose of simplicity of the model. The vectors are clustered in groups if a specific threshold value is given. With the aid of the threshold value, the degree of the concurrence between the document segments can be determined. The most frequent words are used for the description of the clusters. The result is a list of the different meanings. This list needs the intellectual refinement when clusters are eliminated with unimportant meanings e.

The similarity between the vectors of two descriptor occurrences is computed as a function of the number of identical qualities.

Jude W. Shavlik

The symmetrical coefficient of Dice cf. Salton, McGill , et seq. The range of the coefficient of Dice is the interval [0, 1]. The value of the coefficient produces the percentage of the words which exist in both contexts. This model is employed for a lexical analysis of the descriptor occurrences as well as the automatic generation of search synonyms for particular concept meanings. The clusters are formed in accordance with the similarity values.

This formation of groups is carried out with the aid of a simple cluster algorithm Panyr with what non-hierarchical disjunct clusters are created. Best results were achieved with a threshold value of 0. In order to receive an expedient representation of the different sizes of the word meanings of a descriptor every cluster is described with those five words which occur most frequently in the context.

From the contexts of every cluster, a word index with the respective frequencies is generated. In the further processing, these can be employed automatically as search synonyms. We give an example of the procedure of sense disambiguation with the concept of mandates:. Figure 4: Description of the Term mandate. The five various meanings concern mandates for inspection or implementation cluster 1 , expansion of the territorial field of application to mandated territories cluster 2 , UN trusteeship system for mandates cluster 3 , mandate of the Inter-American Convention of Human Rights occurrence 1 not allowing arbitrary allocation and expansion of the mandate occurrence 2 not allowing arbitrary allocation.

A Symbolic and Connectionist Approach To Legal Information Retrieval A Symbolic and Connectionist Approach To Legal Information Retrieval
A Symbolic and Connectionist Approach To Legal Information Retrieval A Symbolic and Connectionist Approach To Legal Information Retrieval
A Symbolic and Connectionist Approach To Legal Information Retrieval A Symbolic and Connectionist Approach To Legal Information Retrieval
A Symbolic and Connectionist Approach To Legal Information Retrieval A Symbolic and Connectionist Approach To Legal Information Retrieval
A Symbolic and Connectionist Approach To Legal Information Retrieval A Symbolic and Connectionist Approach To Legal Information Retrieval

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