Legally, a written legal agreement between two people or companies that stipulates what each must do for the other, or gives the other a document stating that the person who just bought a property is now its rightful owner. This is different from something like a declaration of cessation and abstention or a will and will. which have different legal requirements, but are also enforceable in court. PandaDoc offers a variety of legal forms and free legal templates. Don`t forget to check out the other documents in our template library. These can be downloaded in PDF format. PandaDoc users can also import them into the document editor for full editorial control. Figure 4 shows the similarity estimate using BCTs, as described above for a pair of sample evaluations. As shown in the figure, for the first judgment (Doc1), three terms are identified, which are represented by three corresponding words. For the second judgment (Doc. 2), two concepts are identified.
The similarity shown in the figure is calculated using the representative sentences of these concepts, as explained above. A clause in a contract stating that the rest of the contract should continue to apply if any part of the contract is found to be illegal or unenforceable. Learn how to find and fill out legal forms, how to create your own legal documents, and how to file documents with the court. Legal A legal agreement between two people, for example with regard to a house, land or property. The ease of access to legal information resources through online legal databases has urgently accelerated research in the area of legal information retrieval (LIR). LIR aims to retrieve relevant legal information objects at the request of a user. Legal information objects are various documents such as court records, judgments, legal documents, and judgments that are generated during court proceedings. These documents are primary resources for interpreting the law of any judicial branch and are therefore necessary for a lawyer to make decisions and reason. The specific characteristics of legal documents such as document size, internal document structure, temporal properties, specific legal terminology, polysemy and heterogeneity make LIR extremely complex compared to other areas. Because each legal document raises one or more legal issues, the legal domain requires contextual document retrieval, not just data-driven retrieval.
Contextualizing a legal problem is a non-trivial task because of the complexity inherent in this field. Moreover, the notion of “agreement” or “relevance” in the legal field is multidimensional (Van Opijnen, 2012). LIR is therefore a very demanding field of research, as the field simultaneously requires a very generic to very specific abstraction of a legal document. Retrieving relevant legal documents from a vast collection of resources requires a thorough understanding of the notion of relevance in this area and smart methods for identifying and presenting legal concepts to determine relevance. Many important business documents such as purchase orders, confidentiality agreements, or intellectual property agreements are so common that they can even be converted into templates to save time in document creation. From articles of association and shareholder agreements to NDAs and job offers, PandaDoc can help you create legal documents that protect your business interests. Choose one of our free legal document templates to get started, or use the PandaDoc document editor to create a new contract template from scratch. Some courts now have e-filing systems that allow lawyers and sometimes self-represented parties to easily upload electronic documents in portable document format (“PDF”) to a secure court-run website or private commercial department. Legal A legal document that contains the promise that one person will pay money to another person A legal document that says what someone should or should not do Natural language processing (NLP) provides a wide range of methods and approaches for identifying topics from a document.
The traditional vector space model based on TF-IDF and latent Dirichlet allocation (LDA) use word distribution (Moody, 2016) in the paper to extract subjects. These methods do not take into account the neighborhood of words and are based on the exact correspondence of words. Graphic subject extraction is another popular approach (Ying et al., 2017 and Sayyadi & Rashid, 2013) to identify large topics in documents. These methods are based on establishing a relationship between words/concepts using estimates such as co-occurrence, semantic similarity, etc. to extract important themes in a document. Variations of the above two approaches are often used for topic identification and are available as standard tools for identifying important words in the document. The different degrees of similarity thus determined can also be aggregated to calculate a unique similarity score, which could be useful for finding all relevant documents related to a particular judgment. Given the different similarity scores viewed from different angles between two judgments, we use the ordered weighted average operator to aggregate the different similarity scores into one. OWA is a family of aggregation operators introduced by Yager (2003), has a special application for multi-attribute decision problems, especially in the presence of fuzzy data, and allows the inclusion of linguistic criteria for aggregation. Especially if there are elements in an area that need to be assessed according to criteria “p” T1, T2,…,Tp s.t.
Tj(item) is the extent to which `item` satisfies criterion `j`, so it is possible to use the OWA family of aggregation operators to assess how well `item` satisfies `certain criteria`, `all criteria`. “most criteria”, etc.